The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity

*Boris Yurchak*

## **Abstract**

Spiral cloud-rain bands (SCRBs) are some of the most distinguishing features inherent in satellite and radar images of tropical cyclones (TC). The subject of the proposed research is the finding of a physically substantiated method for estimation of the TC's intensity using SCRBs' configuration parameters. To connect a rainband pattern to a physical process that conditions the spiraling feature of a rainband, it is assumed that the rainband's configuration near the core of a TC is governed primarily by a streamline. In turn, based on the distribution of primarily forces in a TC, an analytical expression as a combination of hyperbolic and logarithmic spirals (HLS) for the description of TC spiral streamline (rainband) is retrieved. Parameters of the HLS are determined by the physical parameters of a TC, particularly, by the maximal wind speed (MWS). To apply this theoretical finding to practical estimation of the TC's intensity, several approximation techniques are developed to "convert" rainband configuration to the estimation of the MWS. The developed techniques have been tested by exploring satellite infrared imageries and airborne and coastal radar data, and the outcomes were compared with in situ measurements of wind speeds and the best track data of tropical cyclones.

**Keywords:** hyperbolic-logarithmic spiral, tropical cyclone, spiral cloud-rain bands, maximum wind speed, approximation

## **1. Introduction**

The issue addressed in this chapter relates to methods for estimating the intensity of a tropical cyclone (TC) from the characteristics of its cloud-rain field (CRF) structure. In general, these methods are empirical and semiempirical, i.e., they are based on the correlation of the structural features of the CRF with the intensity of the TC found from observations from satellites and radars. The most widely used method in operational practice is the Dvorak method [1, 2]. This chapter relates to the exploring of one of the most pronounced structural elements of the CRF, which are spiral cloud-rain bands (SCRBs). Attention was first attracted to these bands by Wexler [3] based on aircraft observations. It was suggested that SCRBs indicate the mature cyclone and its organization follows the streamlines. The authors of [4] described the SCRBs observed on the radar and suggested to use a modified logarithmic spiral to express its configuration in mathematic form. Although SCRBs have been studied for a long time, there is currently no consensus about their origin and mechanism of generation. Reviews of proposed hypotheses are given in [5–8]. In the paper of Lahiri [9], a first attempt was undertaken to estimate the effect of the TC intensity on the geometric characteristics of SCRBs. In this study, a simple model of a TC in which the low-level streamlines were described by logarithmic spirals was suggested. It was shown that the rate of generation of a latent heat in the model was proportional to the crossing angle that is a parameter of the spiral. (As follows from [4], the "crossing angle" of a SCRB of a finite width is the angle at which the longitudinal axial curve of the SCRB crosses the concentric circle (centered at the center of the cyclone) at a given arbitrary point belonging to the axial curve; that is, this is the angle between the tangents to the axial curve and the said circle at this point). As a result, crossing angle decreases as cyclones matured since the latent heat is very small in this stage. However, no relationship between the maximum wind speed (MWS) and the crossing angle was obtained. Moreover, the description of SCRBs by a logarithmic spiral only is an internally contradictory approximation due to the following circumstances. On the one hand, the main property of the logarithmic spiral is the constancy of the crossing angle for any of its points. Therefore, in particular, the alternative name of this spiral is an equiangular spiral. On the other hand, it was found that the crossing angle is sensitive to wind speed. But wind speed is not constant along the SCRB. Therefore, the crossing angle cannot have a constant value along the SCRB as well. Thus, the main feature of the logarithmic spiral (constancy of the crossing angle) is not consistent with the physics of the process. In the modified logarithmic spiral [4], the experimentally observed dependence of the crossing angle on distance is taken into account by introducing a radius of so-called inner limiting circle, at which the crossing angle is zero. However, the relationship of this radius with the intensity of the cyclone and the radius of the maximum wind (RMW) has not been established. It should be noted that the "spiralness" of the SCRB is also used in the empirical Dvorak method, although only at the qualitative level, by estimating the sector occupied by the spiral structure ("count the tenths" method), which is approximated by a logarithmic spiral with the crossing angle of 10°. However, as it was stated in [10], this spiral does not have a physical basis. In our papers [11–14], the assumption of authors [3, 4, 9] that rainbands are well arranged along streamlines was also used. In general, this assumption is confirmed by a comparison of radar and aircraft data (e.g., [15, 16]). The same orientation of the principal rainband along the jet is demonstrated in [7]. Further, the expression for spiral streamline has been derived in [11] in the closed form as hyperbolic-logarithmic spiral (HLS). The most advantage feature of the HLS is the dependence of one of its parameters on the MWS. Unlike the previous studies, the HLS was not assumed but was accurately derived based on physical considerations. At the same time, it turned out that only the peripheral portion of the HLS is similar to the logarithmic spiral. The size of this portion and the corresponding crossing angle are determined by the parameters of the cyclone, including the MWS. Unlike the modified logarithmic spiral, the change in the tangent of the crossing angle in the HLS is governed not by the distance weight function (equal to zero at the distance equal to the radius of the inner limiting circle and approaches unity at the outer edge of the cyclone), but by the physical parameters of the cyclone. However, it is not possible to determine the MWS by the crossing angle of the logarithmic section of the HLS only, as well, due to multifactor influence on the crossing angle. On the other hand, approximation of a SCRB by the HLS allows determining the MWS based on the known characteristics of the cyclone in cases when the SCRB is sufficiently long and markedly different from the logarithmic spiral. A discussion of the physical basis of the proposed method, methodology, and results of its application is the subject of this chapter. In particular, the derivation of the HLS based on the distribution of forces affecting

the cyclone, methods for approximating the SCRB, the results of estimating the MWS from radar, and satellite data in comparison with the data of direct measurements and final conclusions of the corresponding meteorological services (Best

As per Batchelor [17], radial (*v*) and tangential (*u*) components of a fluid flow

*<sup>d</sup><sup>φ</sup> ,* (1)

*dr ,* (2)

*dr:* (3)

tan *μ*ð Þ*r* (4)

*<sup>v</sup>* <sup>¼</sup> <sup>1</sup> *r dψ*

*<sup>u</sup>* ¼ � *<sup>d</sup><sup>ψ</sup>*

�*r dφ <sup>u</sup>* <sup>¼</sup> <sup>1</sup> *v*

� *<sup>d</sup><sup>φ</sup> dr* <sup>¼</sup> <sup>1</sup> *r*

where *r* and *φ* are polar radius and polar angle, respectively, and *Ψ* is the stream function. The combination of (1) and (2) results the streamline equation in polar

It should be noted that the sum of inflow and crossing angles is the right angle

*To the derivation of the streamline equation. α is the crossing angle; μ is the inflow angle; the bold dashed curve is the streamline; solid curves designate p and p-Δp isobars (p is the pressure, Δp is the pressure change between isobars); V is the wind speed of air in point S; u and v are tangential and radial components of the wind speed V at point S, respectively; and φ is the polar angle of radius vector r counted off from arbitrary selected point S0.*

*<sup>v</sup>* <sup>¼</sup> tan ð Þ *<sup>μ</sup>* , where *<sup>μ</sup>* is the inflow angle (**Figure 1**), the final form of

**2. Hyperbolic-logarithmic model of a streamline in a cyclone**

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

Track reports) are provided.

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

are respectively:

coordinates:

(**Figure 1**).

**Figure 1.**

**85**

Considering *<sup>u</sup>*

the streamline equation is

**2.1 Streamline equation in polar coordinates**

the cyclone, methods for approximating the SCRB, the results of estimating the MWS from radar, and satellite data in comparison with the data of direct measurements and final conclusions of the corresponding meteorological services (Best Track reports) are provided.

## **2. Hyperbolic-logarithmic model of a streamline in a cyclone**

#### **2.1 Streamline equation in polar coordinates**

and mechanism of generation. Reviews of proposed hypotheses are given in [5–8]. In the paper of Lahiri [9], a first attempt was undertaken to estimate the effect of the TC intensity on the geometric characteristics of SCRBs. In this study, a simple model of a TC in which the low-level streamlines were described by logarithmic spirals was suggested. It was shown that the rate of generation of a latent heat in the model was proportional to the crossing angle that is a parameter of the spiral. (As follows from [4], the "crossing angle" of a SCRB of a finite width is the angle at which the longitudinal axial curve of the SCRB crosses the concentric circle (centered at the center of the cyclone) at a given arbitrary point belonging to the axial curve; that is, this is the angle between the tangents to the axial curve and the said circle at this point). As a result, crossing angle decreases as cyclones matured since the latent heat is very small in this stage. However, no relationship between the maximum wind speed (MWS) and the crossing angle was obtained. Moreover, the description of SCRBs by a logarithmic spiral only is an internally contradictory approximation due to the following circumstances. On the one hand, the main property of the logarithmic spiral is the constancy of the crossing angle for any of its points. Therefore, in particular, the alternative name of this spiral is an equiangular spiral. On the other hand, it was found that the crossing angle is sensitive to wind speed. But wind speed is not constant along the SCRB. Therefore, the crossing angle cannot have a constant value along the SCRB as well. Thus, the main feature of the logarithmic spiral (constancy of the crossing angle) is not consistent with the physics of the process. In the modified logarithmic spiral [4], the experimentally observed dependence of the crossing angle on distance is taken into account by introducing a radius of so-called inner limiting circle, at which the crossing angle is zero. However, the relationship of this radius with the intensity of the cyclone and the radius of the maximum wind (RMW) has not been established. It should be noted that the "spiralness" of the SCRB is also used in the empirical Dvorak method, although only at the qualitative level, by estimating the sector occupied by the spiral structure ("count the tenths" method), which is approximated by a logarithmic spiral with the crossing angle of 10°. However, as it was stated in [10], this spiral does not have a physical basis. In our papers [11–14], the assumption of authors [3, 4, 9] that rainbands are well arranged along streamlines was also used. In general, this assumption is confirmed by a comparison of radar and aircraft data (e.g., [15, 16]). The same orientation of the principal rainband along the jet is demonstrated in [7]. Further, the expression for spiral streamline has been derived in [11] in the closed form as hyperbolic-logarithmic spiral (HLS). The most advantage feature of the HLS is the dependence of one of its parameters on the MWS. Unlike the previous studies, the HLS was not assumed but was accurately derived based on physical considerations. At the same time, it turned out that only the peripheral portion of the HLS is similar to the logarithmic spiral. The size of this portion and the corresponding crossing angle are determined by the parameters of the cyclone, including the MWS. Unlike the modified logarithmic spiral, the change in the tangent of the crossing angle in the HLS is governed not by the distance weight function (equal to zero at the distance equal to the radius of the inner limiting circle and approaches unity at the outer edge of the cyclone), but by the physical parameters of the cyclone. However, it is not possible to determine the MWS by the crossing angle of the logarithmic section of the HLS only, as well, due to multifactor influence on the crossing angle. On the other hand, approximation of a SCRB by the HLS allows determining the MWS based on the known characteristics of the cyclone in cases when the SCRB is sufficiently long and markedly different from the logarithmic spiral. A discussion of the physical basis of the proposed method, methodology, and results of its application is the subject of this chapter. In particular, the derivation of the HLS based on the distribution of forces affecting

*Current Topics in Tropical Cyclone Research*

**84**

As per Batchelor [17], radial (*v*) and tangential (*u*) components of a fluid flow are respectively:

$$v = \frac{1}{r} \frac{d\mu}{d\rho},\tag{1}$$

$$
\mu = -\frac{d\mu}{dr},\tag{2}
$$

where *r* and *φ* are polar radius and polar angle, respectively, and *Ψ* is the stream function. The combination of (1) and (2) results the streamline equation in polar coordinates:

$$-r\frac{d\rho}{u} = \frac{1}{v}dr.\tag{3}$$

Considering *<sup>u</sup> <sup>v</sup>* <sup>¼</sup> tan ð Þ *<sup>μ</sup>* , where *<sup>μ</sup>* is the inflow angle (**Figure 1**), the final form of the streamline equation is

$$-\frac{d\rho}{dr} = \frac{1}{r}\tan\mu(r)\tag{4}$$

It should be noted that the sum of inflow and crossing angles is the right angle (**Figure 1**).

#### **Figure 1.**

*To the derivation of the streamline equation. α is the crossing angle; μ is the inflow angle; the bold dashed curve is the streamline; solid curves designate p and p-Δp isobars (p is the pressure, Δp is the pressure change between isobars); V is the wind speed of air in point S; u and v are tangential and radial components of the wind speed V at point S, respectively; and φ is the polar angle of radius vector r counted off from arbitrary selected point S0.*

### **2.2 Inflow angle from the balance of forces in a cyclone**

To express the streamline Eq. (4) through the physical parameters of a cyclone, the inflow angle should be evaluated based on the balance of forces in a cyclone. The diagram of forces is depicted in **Figure 2** as follows from [18].

The balance condition called Guldberg-Mohn balance [19] is expressed as follows:

$$F\_G = F\_B \tag{5}$$

where

$$F\_G = -\frac{1}{\rho} \frac{\partial p}{\partial r} \tag{6}$$

is the gradient force (*ρ* is the air density);

$$
\overrightarrow{F\_B} = \overrightarrow{F\_c} + \overrightarrow{F\_d} + \overrightarrow{F\_R} \tag{7}
$$

where

*the same as for Figure 1.*

**Figure 2.**

and

one can get

expression

**87**

The solution of Eq. (13) is [11]

*B* ¼ *f =k* (14)

<sup>m</sup>*=k:* (15)

� �*,* (16)

� *B* � ln *y,* (17)

*V*m*:* (18)

n o � *<sup>B</sup>* � ln *<sup>y</sup>:* (19)

*b* ¼ *V*m*r*

� *<sup>b</sup> n* þ 1

*yn*þ<sup>1</sup> � <sup>1</sup> � �

*<sup>A</sup>* <sup>¼</sup> *rn*

where *y* ¼ *r=r*<sup>0</sup> is the normalized polar radius and coefficient *A* is given by an

m *k n*ð Þ <sup>þ</sup> <sup>1</sup> *<sup>r</sup><sup>n</sup>*þ<sup>1</sup> 0

The first and second terms of Eq. (17) can be named, respectively, as hyperbolic and logarithmic components of the streamline equation of wind in a TC in the polar coordinates. Accordingly, Eq. (17) can be named the hyperbolic-logarithmic spiral.

Because *<sup>y</sup><sup>n</sup>*þ<sup>1</sup> � exp f g ð Þ *<sup>n</sup>* <sup>þ</sup> <sup>1</sup> ln *<sup>y</sup>* , the expression (17) can be transformed to the

�ð Þ *<sup>n</sup>*þ<sup>1</sup> ln *<sup>y</sup>* � <sup>1</sup>

�*<sup>φ</sup>* <sup>¼</sup> *<sup>B</sup>*ln *<sup>r</sup>*

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

*r*0

*<sup>φ</sup>* <sup>¼</sup> *<sup>A</sup>* � <sup>1</sup>

**2.4 Alternate representations of HLS streamline**

*ϕ* ¼ *A* � *e*

exponent-logarithmic form

*n*

*Balance of forces in a cyclone under steady-state air movement in the friction layer (after [18]); designations are*

where *r*<sup>0</sup> is the radius where it is assumed that *φ* = 0. Simplifying this expression,

1 *rn*þ<sup>1</sup> � <sup>1</sup> *r<sup>n</sup>*þ<sup>1</sup> 0

is the balancing force containing

$$F\_c = \frac{V^2}{r},\tag{8}$$

which is the centrifugal force;

$$F\_d = V \cdot f,\tag{9}$$

which is the deflecting force (*f* ¼ 2*ω*sin *ϕ* is the Coriolis parameter, ω is the angular speed of the Earth rotation, and *ϕ* is the altitude); and

$$F\_R = -k \cdot V,\tag{10}$$

which is the frictional force, where *k* is the friction factor (*s* �1 ). All forces in the above expressions (8)–(10) are given per unit mass. As it follows from **Figure 2** and Eqs. (8)–(10), the tangent of inflow angle under the steady-state air movement in the friction layer is defined by a relationship:

$$\tan \mu(r) = \frac{F\_c + F\_d}{|F\_R|} = \frac{f}{k} + \frac{V(r)}{kr} \,. \tag{11}$$

#### **2.3 The streamline equation as a function of physical parameters of a cyclone**

Let us assume for definiteness that the speed of wind *V*(*r*) changes with cyclone radius in accordance with a power law which is inherent to the Rankine vortex [20]:

$$V(r) = V\_{\rm m} \left(\frac{r\_{\rm m}}{r}\right)^{n}, \quad r \ge r\_{\rm m} \tag{12}$$

where *V*<sup>m</sup> is the MWS, *n* is the exponent (hyperbolic index), and *r*<sup>m</sup> is the radius of the maximum wind (RMW). Under these conditions, Eq. (4) can be written as

$$-d\rho = \left(\frac{B}{r} + b\frac{\mathbb{1}}{r^{n+2}}\right)dr,\tag{13}$$

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity DOI: http://dx.doi.org/10.5772/intechopen.88683*

#### **Figure 2.**

**2.2 Inflow angle from the balance of forces in a cyclone**

*Current Topics in Tropical Cyclone Research*

is the gradient force (*ρ* is the air density);

is the balancing force containing

which is the centrifugal force;

the friction layer is defined by a relationship:

follows:

**86**

where

The diagram of forces is depicted in **Figure 2** as follows from [18].

*FB* ! ¼ *Fc* ! þ*Fd* ! þ*FR* !

angular speed of the Earth rotation, and *ϕ* is the altitude); and

which is the frictional force, where *k* is the friction factor (*s*

tan *μ*ð Þ¼ *r*

*V r*ð Þ¼ *V*<sup>m</sup>

�*d<sup>φ</sup>* <sup>¼</sup> *<sup>B</sup>*

*r*

To express the streamline Eq. (4) through the physical parameters of a cyclone, the inflow angle should be evaluated based on the balance of forces in a cyclone.

*FG* ¼ *FB* (5)

*<sup>r</sup> ,* (8)

*Fd* ¼ *V* � *f,* (9)

*FR* ¼ �*k* � *V,* (10)

�1

*kr :* (11)

*, r*≥*r*m*,* (12)

*dr,* (13)

). All forces in the

(6)

(7)

The balance condition called Guldberg-Mohn balance [19] is expressed as

*FG* ¼ � <sup>1</sup> *ρ ∂p ∂r*

*Fc* <sup>¼</sup> *<sup>V</sup>*<sup>2</sup>

which is the deflecting force (*f* ¼ 2*ω*sin *ϕ* is the Coriolis parameter, ω is the

above expressions (8)–(10) are given per unit mass. As it follows from **Figure 2** and Eqs. (8)–(10), the tangent of inflow angle under the steady-state air movement in

> *Fc* þ *Fd* j j *FR*

**2.3 The streamline equation as a function of physical parameters of a cyclone**

*r*m *r <sup>n</sup>*

Let us assume for definiteness that the speed of wind *V*(*r*) changes with cyclone radius in accordance with a power law which is inherent to the Rankine vortex [20]:

where *V*<sup>m</sup> is the MWS, *n* is the exponent (hyperbolic index), and *r*<sup>m</sup> is the radius of the maximum wind (RMW). Under these conditions, Eq. (4) can be written as

> <sup>þ</sup> *<sup>b</sup>* <sup>1</sup> *rn*þ<sup>2</sup>

¼ *f k* þ *V r*ð Þ

*Balance of forces in a cyclone under steady-state air movement in the friction layer (after [18]); designations are the same as for Figure 1.*

where

$$B = f / k \tag{14}$$

and

$$b = V\_{\mathfrak{m}} r\_{\mathfrak{m}}^n / k. \tag{15}$$

The solution of Eq. (13) is [11]

$$-\rho = B\ln\frac{r}{r\_0} - \frac{b}{n+1} \left(\frac{1}{r^{n+1}} - \frac{1}{r\_0^{n+1}}\right),\tag{16}$$

where *r*<sup>0</sup> is the radius where it is assumed that *φ* = 0. Simplifying this expression, one can get

$$
\rho = A \cdot \left(\frac{1}{\mathcal{Y}^{n+1}} - 1\right) - B \cdot \ln \mathcal{y},
\tag{17}
$$

where *y* ¼ *r=r*<sup>0</sup> is the normalized polar radius and coefficient *A* is given by an expression

$$A = \frac{r\_{\rm m}^{\rm n}}{k(n+1)r\_0^{n+1}} V\_{\rm m}. \tag{18}$$

The first and second terms of Eq. (17) can be named, respectively, as hyperbolic and logarithmic components of the streamline equation of wind in a TC in the polar coordinates. Accordingly, Eq. (17) can be named the hyperbolic-logarithmic spiral.

#### **2.4 Alternate representations of HLS streamline**

Because *<sup>y</sup><sup>n</sup>*þ<sup>1</sup> � exp f g ð Þ *<sup>n</sup>* <sup>þ</sup> <sup>1</sup> ln *<sup>y</sup>* , the expression (17) can be transformed to the exponent-logarithmic form

$$\phi = A \cdot \left\{ e^{-(n+1)\ln y} - \mathbf{1} \right\} - B \cdot \ln y. \tag{19}$$

Using the Maclaurin series expansion for an exponential function *<sup>e</sup><sup>x</sup>* <sup>¼</sup> <sup>P</sup><sup>∞</sup> *i*¼0 *xi =i*! � � and combining the coefficients at the argument of the first power, Eq. (19) can be written as a polynomial:

$$\varphi = A \sum\_{i=2}^{\infty} \frac{\{- (n+1)\}^i}{i!} (\ln y)^i - \{A(n+1) + B\} \ln y \tag{20}$$

As follows from Eq. (20), the HLS contains linear and nonlinear parts in regard to the logarithm of the relative polar radius (ln*y*). Taking into account the expression for coefficient *A* (18), the linear part is the logarithmic spiral (*φ*L) that can be expressed in the form:

$$\log \rho\_{\rm L} = -\{A(n+1) + B\} \ln \mathcal{y} = -\left(\frac{r\_{\rm m}^n}{k r\_0^{n+1}} V\_{\rm m} + B\right) \ln \mathcal{y} = -B \left(\mathcal{y}\_{\rm m}^n \frac{V\_{\rm m}}{V\_{\rm C}} + 1\right) \ln \mathcal{y} \tag{21}$$

where *V*<sup>C</sup> ¼ *fr*<sup>0</sup> is the Coriolis speed at distance *r*<sup>0</sup> and *y*<sup>m</sup> ¼ *r*m*=r*<sup>0</sup> is the relative RMW. As follows from Eq. (21), the tangent of the crossing angle of the linear part of the HLS depends on many factors as follows:

$$\tan a = \left\{ B \left( \mathbf{y}\_{\text{m}}^{n} \frac{V\_{\text{max}}}{V\_{\text{C}}} + \mathbf{1} \right) \right\}^{-1}. \tag{22}$$

why the linear parts of pairs of curves in **Figure 4** with the same friction factor

*Illustration of HLS configuration in semilogarithmic coordinates versus variable maximal wind speed and*

*), r0 = 500 km, n = 0.6, ym = 0.1, and f = 1<sup>10</sup><sup>5</sup> <sup>s</sup>*

*Illustration of HLS configuration in polar coordinates versus variable maximal wind speed and friction factor;*

different tilts for different MWS. It is the explanation of the well-known experimental fact, mentioned in the Introduction section, regarding the sensitivity of the

The task of using the analytical expression of the streamline (17) or (19) considered in the previous section is to use it to determine the MWS in a TC. The base for

) and the same Coriolis parameter (*f*) have notable

*), k (s<sup>1</sup>*

*), r0 = 500 km, n = 0.6, ym = 0.1,*

*<sup>1</sup> (latitude = 20° N).*

(*k =* 10<sup>5</sup> s

**89**

*friction factor; Vm(m s<sup>1</sup>*

**Figure 4.**

**Figure 3.**

*and f = 1<sup>10</sup><sup>5</sup> <sup>s</sup>*

<sup>1</sup> or *k =* 10<sup>4</sup> s

*), k (s<sup>1</sup>*

crossing angle on the intensity of a TC.

**3. HLS approximation techniques**

1

*ordinate and abscissa axis are relative polar radius (y); Vm(m s<sup>1</sup>*

*N).*

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

*<sup>1</sup> (latitude = 20°*

#### **2.5 Graphical representations of HLS streamline**

To illustrate the dependence of HLS primary features versus the MWS and the friction factor, the graphical diagrams in polar coordinates (**Figure 3**) and semilogarithmic coordinates (**Figure 4**) for three values of MWS and two values of the friction factor are provided below.

**Figure 3** reflects the known feature of a rainband, as it has been summarized by Willoughby [16]. A rainband of a cyclone from the periphery to its center is curved, due to the increase in the angular velocity, and therefore can be transformed into a logarithmic spiral. As the intensity of a cyclone increases, the slope of the twist decreases, and the band configuration becomes like a circular arc. Indeed, the HLS is similar to a logarithmic spiral, but only on the periphery of a cyclone, as shown in Eq. (20), where the normalized polar radius (*y*) is a little less than 1. Toward the center of the cyclone (*y* < 1), the first term in Eq. (20) begins to prevail over the second one, and the HLS begins to "round off" (with constant *k* and *n*) accordingly with the observations. The distance where it happens depends on the parameter ratio between the linear and nonlinear parts of Eq. (20). Plots of HLS with *V*<sup>m</sup> = 40 m s�<sup>1</sup> are missing in **Figure 4** to simplify its reading. In the semilogarithmic coordinates in **Figure 4**, the canonical logarithmic spiral *<sup>φ</sup>* ¼ �ð Þ tan *<sup>α</sup>* �<sup>1</sup> � ln *<sup>y</sup>* would be depicted as a straight line with the angle coefficient equal to the inverse value of the tangent of the corresponding crossing angle. As follows from the plots in **Figure 4**, the HLS is more similar to the logarithmic spiral (straight line) within one turn (2π), and for 0*:*1≤ *y*≤1 ð Þ �2*:*3 ≤ln *y* ≤0 for any MWS, the greater is the friction factor (compare lower and upper pairs of curves). For small friction factor, the greater the MWS is, the shorter the linear part of the HLS (logarithmic) is (compare two lower curves). As follows from Eq. (22), in contrast to the canonical logarithmic spiral mentioned above where tan *α* ¼ *const* at any *y*, the tangent of the crossing angle of the HLS linear part depends on the term *yn* <sup>m</sup>*V*m*=V*C. It is a reason

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity DOI: http://dx.doi.org/10.5772/intechopen.88683*

**Figure 3.**

Using the Maclaurin series expansion for an exponential function

f g �ð Þ *<sup>n</sup>* <sup>þ</sup> <sup>1</sup> *<sup>i</sup>*

*<sup>i</sup>*! ð Þ ln *<sup>y</sup>*

m *krn*þ<sup>1</sup> 0

tan *<sup>α</sup>* <sup>¼</sup> *B y<sup>n</sup>*

*i*

As follows from Eq. (20), the HLS contains linear and nonlinear parts in regard to the logarithm of the relative polar radius (ln*y*). Taking into account the expression for coefficient *A* (18), the linear part is the logarithmic spiral (*φ*L) that can be

> *V*<sup>m</sup> þ *B* !

where *V*<sup>C</sup> ¼ *fr*<sup>0</sup> is the Coriolis speed at distance *r*<sup>0</sup> and *y*<sup>m</sup> ¼ *r*m*=r*<sup>0</sup> is the relative RMW. As follows from Eq. (21), the tangent of the crossing angle of the linear part

> m *V*max *V*<sup>C</sup>

To illustrate the dependence of HLS primary features versus the MWS and the friction factor, the graphical diagrams in polar coordinates (**Figure 3**) and semilogarithmic coordinates (**Figure 4**) for three values of MWS and two values of the

**Figure 3** reflects the known feature of a rainband, as it has been summarized by Willoughby [16]. A rainband of a cyclone from the periphery to its center is curved, due to the increase in the angular velocity, and therefore can be transformed into a logarithmic spiral. As the intensity of a cyclone increases, the slope of the twist decreases, and the band configuration becomes like a circular arc. Indeed, the HLS is similar to a logarithmic spiral, but only on the periphery of a cyclone, as shown in Eq. (20), where the normalized polar radius (*y*) is a little less than 1. Toward the center of the cyclone (*y* < 1), the first term in Eq. (20) begins to prevail over the second one, and the HLS begins to "round off" (with constant *k* and *n*) accordingly with the observations. The distance where it happens depends on the parameter ratio between the linear and nonlinear parts of Eq. (20). Plots of HLS with *V*<sup>m</sup> = 40 m s�<sup>1</sup> are missing in **Figure 4** to simplify its reading. In the semilogarithmic coordinates in **Figure 4**, the canonical logarithmic spiral *<sup>φ</sup>* ¼ �ð Þ tan *<sup>α</sup>* �<sup>1</sup> � ln *<sup>y</sup>* would be depicted as a straight line with the angle coefficient equal to the inverse value of the tangent of the corresponding crossing angle. As follows from the plots in **Figure 4**, the HLS is more similar to the logarithmic spiral (straight line) within one turn (2π), and for 0*:*1≤ *y*≤1 ð Þ �2*:*3 ≤ln *y* ≤0 for any MWS, the greater is the friction factor (compare lower and upper pairs of curves). For small friction factor, the greater the MWS is, the shorter the linear part of the HLS (logarithmic) is (compare two lower curves). As follows from Eq. (22), in contrast to the canonical logarithmic spiral mentioned above where tan *α* ¼ *const* at any *y*, the tangent of the

þ 1 � � � � �<sup>1</sup>

ln *<sup>y</sup>* ¼ �*B y<sup>n</sup>*

and combining the coefficients at the argument of the first power,

� f g *A n*ð Þþ þ 1 *B* ln *y* (20)

m *V*<sup>m</sup> *V*<sup>C</sup> þ 1 � �

*:* (22)

<sup>m</sup>*V*m*=V*C. It is a reason

ln *y* (21)

*<sup>e</sup><sup>x</sup>* <sup>¼</sup> <sup>P</sup><sup>∞</sup> *i*¼0 *xi =i*!

� �

expressed in the form:

Eq. (19) can be written as a polynomial:

*Current Topics in Tropical Cyclone Research*

*φ* ¼ *A*

*<sup>φ</sup>*<sup>L</sup> ¼ �f g *A n*ð Þþ <sup>þ</sup> <sup>1</sup> *<sup>B</sup>* ln *<sup>y</sup>* ¼ � *<sup>r</sup><sup>n</sup>*

friction factor are provided below.

**88**

of the HLS depends on many factors as follows:

**2.5 Graphical representations of HLS streamline**

crossing angle of the HLS linear part depends on the term *yn*

X∞ *i*¼2

*Illustration of HLS configuration in polar coordinates versus variable maximal wind speed and friction factor; ordinate and abscissa axis are relative polar radius (y); Vm(m s<sup>1</sup> ), k (s<sup>1</sup> ), r0 = 500 km, n = 0.6, ym = 0.1, and f = 1<sup>10</sup><sup>5</sup> <sup>s</sup> <sup>1</sup> (latitude = 20° N).*

#### **Figure 4.**

*Illustration of HLS configuration in semilogarithmic coordinates versus variable maximal wind speed and friction factor; Vm(m s<sup>1</sup> ), k (s<sup>1</sup> ), r0 = 500 km, n = 0.6, ym = 0.1, and f = 1<sup>10</sup><sup>5</sup> <sup>s</sup> <sup>1</sup> (latitude = 20° N).*

why the linear parts of pairs of curves in **Figure 4** with the same friction factor (*k =* 10<sup>5</sup> s <sup>1</sup> or *k =* 10<sup>4</sup> s 1 ) and the same Coriolis parameter (*f*) have notable different tilts for different MWS. It is the explanation of the well-known experimental fact, mentioned in the Introduction section, regarding the sensitivity of the crossing angle on the intensity of a TC.

#### **3. HLS approximation techniques**

The task of using the analytical expression of the streamline (17) or (19) considered in the previous section is to use it to determine the MWS in a TC. The base for resolving this task is the expression (18) for the coefficient *A* that included the MWS. From this expression, taking into account the relationship between the friction coefficient and the coefficient *B* (14), one can get the desired formulae for calculation the MWS using the HLS parameters:

$$V\_{\rm m} = A \frac{k(n+1)r\_0^{n+1}}{r\_{\rm m}^{\rm n}} = \frac{A f(n+1) r\_0^{n+1}}{B} = \frac{A}{B} (n+1) \mathbf{y}\_{\rm m}^{-1} V\_{\rm C} \tag{23}$$

<sup>Δ</sup>*φ*<sup>2</sup> � � <sup>¼</sup> <sup>X</sup>

*N*

*i*¼1

*a SCRB with the HLS*

*φi,* exp *er:* � *φi,approx:* � �<sup>2</sup>

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

and terms in (28) and (29) are *<sup>Q</sup>*<sup>11</sup> <sup>¼</sup> *<sup>x</sup>*<sup>2</sup> ½ �

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

evaluated data should satisfy the relationships:

logarithms, i.e., *φ<sup>i</sup>* ¼ *f* ln *yi*

**91**

*Aiy<sup>n</sup>*þ<sup>1</sup>

<sup>0</sup>*,i* <sup>¼</sup> *Ay,i* <sup>¼</sup> *<sup>y</sup><sup>n</sup>*

*Bi* <sup>¼</sup> *<sup>f</sup>*

where *Ay* and *B* are constants pertained to the given spiral element, *r*0,0 is the polar radius of remotest SPA where the spiral signature can be described by the HLS, *y*<sup>m</sup> ¼ *r*m*=r*0*,*0, and *y*0*,i* ¼ *r*0*,i=r*0*,*0. Relationships (32) and (33) together combine the condition of the HLS stationarity. That is, if a spiral signature is really described by the HLS, then the parameters *Аy,i* and *Bi* should be independent (theoretically) on selection of the SPA. Let us assume that the stationarity condition is satisfied within a polar radius range that corresponds to SPAs of indexes from *is* to *ie*. The definition of the stationary part above is related to the general case, where a spiral signature is represented by *N* points with numbers *i* = 1, 2… *is* … *ie* … *N*. The first SPA that is remotest from the center of the cyclone (*i* = 1) is selected arbitrarily, and a SPA with index *is* has polar radius *r*0,0. This ordered nest of point coordinates, where the polar radii of the points are normalized by a distance from the remotest SPA, combines the normalized profile (NP) of a spiral signature. A logarithmic modification of NP, where relative radii are substituted by their natural

m *k n*ð Þ þ 1 *r*0*,*<sup>0</sup>

� �, is a logarithmic NP (LNP). The sequential

*V*<sup>m</sup> ¼ *Ay,* (32)

*<sup>k</sup>* <sup>¼</sup> *B,* (33)

<sup>¼</sup> <sup>X</sup> *N*

*3.1.2 The condition of stationarity under the application of the LSM for approximation of*

As follows from Eq. (18), coefficient *A* of the HLS depends on the selection of radius *r*0. On the other hand, based on the physical sense, the cyclone's physical parameters evaluated from coefficients *A* and *B* must not depend on selection of the initial point (point of spiral reference). To satisfy this requirement, the selected spiral signature should be fitted by the HLS with coefficient *A* that should be proportional to the polar radius with power –*(n* + 1) (in accordance with Eq. (18)) and coefficient *B* that should be a constant (in accordance with (14)). This validates the fitting of a given spiral signature by the HLS. Substantiation of these conditions, which characterize the "HLS stationarity" and the control technique, is provided below. The term "HLS stationarity" can be explained by providing the following example. Suppose some spiral signature is introduced by a set of point coordinates. This set of points is aimed to be approximated by the HLS using the LSM. Let us select arbitrary the first starting point of approximation (SPA) at the peripheral part of the spiral signature. Suppose its polar radius is *r*0,1. The applied LSM procedure evaluates parameters *A*<sup>1</sup> and *B*1. Next, the another SPA is taken with *r*0,2 < *r*0,1. The approximation procedure is repeated, and the second set of parameters *A*<sup>2</sup> and *B*<sup>2</sup> is obtained. Taking consequently SPAs along the signature and applying the LSM approximation procedure, the number of sets of *Ai* and *Bi* can be obtained. It should be noted that the MWS *V*m, the RMW *r*m, friction factor *k*, exponent *n*, and the Coriolis parameter *f* are the same for all approximation runs and different SPAs. Because of that, the values of the HLS coefficients pertained to different sets of

*i*¼1

*<sup>D</sup> , Q*<sup>22</sup> <sup>¼</sup> *<sup>T</sup>*<sup>2</sup> ½ � ð Þ *<sup>x</sup>*

*<sup>φ</sup>i,* exp *er:* � *<sup>A</sup>*<sup>~</sup> � *T x*ð Þþ*<sup>i</sup> <sup>B</sup>*<sup>~</sup> � *xi* � �<sup>2</sup>

*<sup>D</sup>* .

(31)

Thus, the MWS can be estimated by approximating the SCRB of the TC with the HLS Eq. (19). Under this approximation, values of *A* and *B* are determined, assuming the value of the hyperbolic index *n*, as well as the measured or estimated value of the MWR. Coriolis velocity (*V*C) is determined by known latitude of the center of the TC and the radius of given initial point of the SCRB. To get the approximation estimate of the HLS coefficients, the standard least squares method (LSM) and the assimilation technique were considered and tested. Brief descriptions of these techniques are provided below.

#### **3.1 Estimation of coefficients of the HLS by the least squares method**

#### *3.1.1 Relationships for calculation*

Taking the exponentially logarithmic form of the HLS (19) and denoting ln*y=x* and *T x*ð Þ¼ *<sup>e</sup>*�ð Þ *<sup>n</sup>*þ<sup>1</sup> *<sup>x</sup>* � 1, it is possible to write

$$
\rho = A \cdot T(\mathbf{x}) - B \cdot \mathbf{x}.\tag{24}
$$

Performing the routine LSM procedure, one gets the calculation relationships for the HLS coefficients estimates which are provided below in the Gauss designation ½�¼*<sup>s</sup>* <sup>P</sup> ð Þ*<sup>s</sup>*

$$\tilde{A} = \frac{1}{D} \{ \left[ \mathbf{x}^2 \right] \cdot \left[ \boldsymbol{\rho} \cdot \boldsymbol{T}(\boldsymbol{\varkappa}) \right] - \left[ \boldsymbol{\varkappa} \cdot \boldsymbol{T}(\boldsymbol{\varkappa}) \right] \cdot \left[ \boldsymbol{\varrho} \cdot \boldsymbol{\varkappa} \right] \}, \tag{25}$$

$$\tilde{B} = \frac{1}{D} \left\{ \left[ \mathbf{x} \cdot T(\mathbf{x}) \right] \cdot \left[ \boldsymbol{\rho} \cdot T(\mathbf{x}) \right] - \left[ T^2(\mathbf{x}) \right] \cdot \left[ \boldsymbol{\rho} \cdot \mathbf{x} \right] \right\}. \tag{26}$$

where

$$D = \left[T^2(\mathbf{x})\right] \left[\mathbf{x}^2\right] - \left[\mathbf{x} \cdot T(\mathbf{x})\right]^2. \tag{27}$$

The error estimates of the coefficients are equal:

$$
\sigma\_A = \sigma \cdot \sqrt{Q\_{1\mathcal{V}}} \tag{28}
$$

$$
\sigma\_{\mathcal{B}} = \sigma \cdot \sqrt{\mathcal{Q}\_{22}} \tag{29}
$$

where

$$
\sigma = \sqrt{\frac{[\Delta \rho^2]}{N-2}},\tag{30}
$$

is the residual variance, where

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity DOI: http://dx.doi.org/10.5772/intechopen.88683*

$$\mathbb{E}\left[\Delta\boldsymbol{\rho}^{2}\right] = \sum\_{i=1}^{N} \left(\rho\_{i,\text{ exp}\boldsymbol{\sigma}\boldsymbol{\tau}} - \rho\_{i,\text{apprx.}}\right)^{2} = \sum\_{i=1}^{N} \left(\rho\_{i,\text{ exp}\boldsymbol{\tau}.} - \tilde{\boldsymbol{A}} \cdot \boldsymbol{T}(\boldsymbol{\chi}\_{i}) + \tilde{\boldsymbol{B}} \cdot \boldsymbol{\infty}\_{i}\right)^{2} \tag{31}$$

and terms in (28) and (29) are *<sup>Q</sup>*<sup>11</sup> <sup>¼</sup> *<sup>x</sup>*<sup>2</sup> ½ � *<sup>D</sup> , Q*<sup>22</sup> <sup>¼</sup> *<sup>T</sup>*<sup>2</sup> ½ � ð Þ *<sup>x</sup> <sup>D</sup>* .

## *3.1.2 The condition of stationarity under the application of the LSM for approximation of a SCRB with the HLS*

As follows from Eq. (18), coefficient *A* of the HLS depends on the selection of radius *r*0. On the other hand, based on the physical sense, the cyclone's physical parameters evaluated from coefficients *A* and *B* must not depend on selection of the initial point (point of spiral reference). To satisfy this requirement, the selected spiral signature should be fitted by the HLS with coefficient *A* that should be proportional to the polar radius with power –*(n* + 1) (in accordance with Eq. (18)) and coefficient *B* that should be a constant (in accordance with (14)). This validates the fitting of a given spiral signature by the HLS. Substantiation of these conditions, which characterize the "HLS stationarity" and the control technique, is provided below. The term "HLS stationarity" can be explained by providing the following example. Suppose some spiral signature is introduced by a set of point coordinates. This set of points is aimed to be approximated by the HLS using the LSM. Let us select arbitrary the first starting point of approximation (SPA) at the peripheral part of the spiral signature. Suppose its polar radius is *r*0,1. The applied LSM procedure evaluates parameters *A*<sup>1</sup> and *B*1. Next, the another SPA is taken with *r*0,2 < *r*0,1. The approximation procedure is repeated, and the second set of parameters *A*<sup>2</sup> and *B*<sup>2</sup> is obtained. Taking consequently SPAs along the signature and applying the LSM approximation procedure, the number of sets of *Ai* and *Bi* can be obtained. It should be noted that the MWS *V*m, the RMW *r*m, friction factor *k*, exponent *n*, and the Coriolis parameter *f* are the same for all approximation runs and different SPAs. Because of that, the values of the HLS coefficients pertained to different sets of evaluated data should satisfy the relationships:

$$A\_{i}\mathcal{Y}\_{0,i}^{n+1} = A\_{\text{y},i} = \frac{\mathcal{Y}\_{\text{m}}^{n}}{k(n+1)r\_{0,0}}V\_{\text{m}} = A\_{\text{y}}\tag{32}$$

$$B\_i = \frac{f}{k} = B,\tag{33}$$

where *Ay* and *B* are constants pertained to the given spiral element, *r*0,0 is the polar radius of remotest SPA where the spiral signature can be described by the HLS, *y*<sup>m</sup> ¼ *r*m*=r*0*,*0, and *y*0*,i* ¼ *r*0*,i=r*0*,*0. Relationships (32) and (33) together combine the condition of the HLS stationarity. That is, if a spiral signature is really described by the HLS, then the parameters *Аy,i* and *Bi* should be independent (theoretically) on selection of the SPA. Let us assume that the stationarity condition is satisfied within a polar radius range that corresponds to SPAs of indexes from *is* to *ie*. The definition of the stationary part above is related to the general case, where a spiral signature is represented by *N* points with numbers *i* = 1, 2… *is* … *ie* … *N*. The first SPA that is remotest from the center of the cyclone (*i* = 1) is selected arbitrarily, and a SPA with index *is* has polar radius *r*0,0. This ordered nest of point coordinates, where the polar radii of the points are normalized by a distance from the remotest SPA, combines the normalized profile (NP) of a spiral signature. A logarithmic modification of NP, where relative radii are substituted by their natural logarithms, i.e., *φ<sup>i</sup>* ¼ *f* ln *yi* � �, is a logarithmic NP (LNP). The sequential

resolving this task is the expression (18) for the coefficient *A* that included the MWS. From this expression, taking into account the relationship between the friction coefficient and the coefficient *B* (14), one can get the desired formulae for

> *n*þ1 0 *rn* m

¼ *A B*

**3.1 Estimation of coefficients of the HLS by the least squares method**

*<sup>D</sup>* ½ �� *<sup>x</sup>* � *T x*ð Þ ½ �� *<sup>φ</sup>* � *T x*ð Þ *<sup>T</sup>*<sup>2</sup>

*<sup>σ</sup><sup>A</sup>* <sup>¼</sup> *<sup>σ</sup>* � ffiffiffiffiffiffiffi

r

*σ* ¼

*<sup>σ</sup><sup>B</sup>* <sup>¼</sup> *<sup>σ</sup>* � ffiffiffiffiffiffiffiffi

*<sup>D</sup>* <sup>¼</sup> *<sup>T</sup>*<sup>2</sup>

The error estimates of the coefficients are equal:

*f n*ð Þ <sup>þ</sup> <sup>1</sup> *<sup>r</sup>n*þ<sup>1</sup> 0 *rn* m

Thus, the MWS can be estimated by approximating the SCRB of the TC with the HLS Eq. (19). Under this approximation, values of *A* and *B* are determined, assuming the value of the hyperbolic index *n*, as well as the measured or estimated value of the MWR. Coriolis velocity (*V*C) is determined by known latitude of the center of the TC and the radius of given initial point of the SCRB. To get the approximation estimate of the HLS coefficients, the standard least squares method (LSM) and the assimilation technique were considered and tested. Brief descriptions of these tech-

Taking the exponentially logarithmic form of the HLS (19) and denoting ln*y=x*

Performing the routine LSM procedure, one gets the calculation relationships for the HLS coefficients estimates which are provided below in the Gauss designation

ð Þ *<sup>x</sup>* � � *<sup>x</sup>*<sup>2</sup> � � � ½ � *<sup>x</sup>* � *T x*ð Þ <sup>2</sup>

*Q*<sup>11</sup>

*Q*<sup>22</sup>

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Δ*φ*<sup>2</sup> ½ � *N* � 2 *,*

¼ *A*

*φ* ¼ *A* � *T x*ð Þ� *B* � *x:* (24)

*<sup>D</sup> <sup>x</sup>*<sup>2</sup> � � � ½ �� *<sup>φ</sup>* � *T x*ð Þ ½ �� *<sup>x</sup>* � *T x*ð Þ ½ � *<sup>φ</sup>* � *<sup>x</sup>* � �*,* (25)

ð Þ *<sup>x</sup>* � � � ½ � *<sup>φ</sup>* � *<sup>x</sup>* � �*:* (26)

*:* (27)

(30)

p *,* (28)

p *,* (29)

*<sup>B</sup>* ð Þ *<sup>n</sup>* <sup>þ</sup> <sup>1</sup> *<sup>y</sup>*�<sup>1</sup>

<sup>m</sup> *V*<sup>C</sup> (23)

calculation the MWS using the HLS parameters:

*<sup>V</sup>*<sup>m</sup> <sup>¼</sup> *<sup>A</sup> k n*ð Þ <sup>þ</sup> <sup>1</sup> *<sup>r</sup>*

*Current Topics in Tropical Cyclone Research*

niques are provided below.

*3.1.1 Relationships for calculation*

½�¼*<sup>s</sup>* <sup>P</sup> ð Þ*<sup>s</sup>*

where

where

**90**

and *T x*ð Þ¼ *<sup>e</sup>*�ð Þ *<sup>n</sup>*þ<sup>1</sup> *<sup>x</sup>* � 1, it is possible to write

*<sup>A</sup>*<sup>~</sup> <sup>¼</sup> <sup>1</sup>

*<sup>B</sup>*<sup>~</sup> <sup>¼</sup> <sup>1</sup>

is the residual variance, where

approximation of a spiral signature is performed by the selection of consequent SPAs toward the cyclone center by a given number of points. The deficiencies of conditions (32) and (33) are primarily due to two reasons. The starting of a nonstationary part of a spiral signature {*i* = 1 … (*is*-1)} is due to the incorrect tracing of streamlines by clouds or the deviation of a streamline from the theoretical HLS for some reason. Regarding the "floor bounding" of the stationary part under SPA with *i*≥*ie* þ 1, it is possible to show that this feature is due to the change of wind and friction regimes near the area of maximum winds. Beginning from this point, the increase of wind speed becomes slower than that of power *n*opt. It might be assumed also that due to high wind speed, the friction factor becomes lower [21] or its dependence on wind speed becomes not linear. At that, the approximation estimates of the HLS coefficients were conducted under *n*opt divergent from their stationary values, *B n* <sup>~</sup> *opt* ! <sup>∞</sup> and *<sup>A</sup>*~*<sup>y</sup> nopt* ! 0. Therefore, the HLS coefficients should be selected from the results of the approximation of a spiral signature within a range where the stationary condition is satisfied. On the other hand, the distance from the center, where such divergence occurs, can be taken as an additional parameter of the cyclonic vortex (radius of divergence). Simulation experiments validated the stationarity and "divergent" property of the HLS coefficients.

## *3.1.3 An example of HLS approximation with LSM*

As an example of the application of the above approach to the approximation of the SCRB by the HLS, we consider the estimate of the MWS from an image from the geostationary satellite GMS-5 in the visible range of TC Mitag, at 01:31 UTC on March 5, 2002 (**Figure 5**).

The corresponding logarithmic normalized profile of the annotated signature is shown in **Figure 6**.

As can be seen from **Figure 6**, the "regularity" of the profile is broken in the range lny = (� 0.6)–(� 0.7). Apparently this is due to not quite correct annotation of the final segment of the SCRB. It would have to be carried out along the steeper contrast border observed in this area. Therefore, the main part of the profile in the range lny = 0–(� 0.7) was subjected to approximation, only. The main characteristics of the signature were radius of the most distant starting point of the signature *r*<sup>0</sup> = 447 km, *r*<sup>m</sup> = 130 km, and *f* = 3.3 10–5 s�<sup>1</sup> (latitude 13.29°N). As the consecutive

tests showed, the stationarity conditions were best performed for both HLS coeffi-

The average values of the coefficients *Ay* and *B* in the stationary segment were *Ay* = 0.41 0.04 and *<sup>B</sup>* = 0.85 0.22, which corresponds to *<sup>V</sup>*<sup>m</sup> = 58.1 16.1 ms<sup>1</sup>

The meteorological assessment of the TC intensity for this point in time was 115 kts

account the difference in altitudes for which the meteorological assessment is made (10 m) and the upper boundary of clouds for which the HLS estimate was calculated. According to experimental data, the MWS in a TC is observed at the level of 850–900 hPa: *V*m(850 hPa) [22]. The average speed at the level of cloud top *V*m(240 hPa) makes up about 70–80% of *V*m(850 hPa) [22, 23]. The surface wind speed (at 10 m height) is smaller than *V*m(850 hPa) by about 25–30% [24]. Therefore, considering that the configuration of the spiral signature depends on wind speed at the height of its existence, the HLS estimates can be compared (as a first

As follows from the previous section, for the HLS approximation of SCRB by the LSM with the subsequent checking for stationarity, a lot of operations are needed to be performed. This requires considerable time and, therefore, is unlikely to have a

), which can be considered a satisfactory coincidence even taking into

.

cients in this particular case with the hyperbolic index *n* = 1.1 (**Figure 7**).

*Logarithmic normalized profile of SCRB annotated in Figure 5 (right snapshot).*

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

guess) with surface data of meteorological services.

*Graph of stationarity of HLS approximation at n = 1.1.*

**3.2 Assimilation technique**

*3.2.1 Motivation*

**93**

(59.2 ms<sup>1</sup>

**Figure 7.**

**Figure 6.**

#### **Figure 5.**

*Image of TC Mitag (2002) in visible range; left image—original image; right snapshot—a contrasted image with an annotated position of the center of the eye and annotated outer edge of the SCRB adjacent to the cyclone core.*

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity DOI: http://dx.doi.org/10.5772/intechopen.88683*

**Figure 6.** *Logarithmic normalized profile of SCRB annotated in Figure 5 (right snapshot).*

**Figure 7.** *Graph of stationarity of HLS approximation at n = 1.1.*

tests showed, the stationarity conditions were best performed for both HLS coefficients in this particular case with the hyperbolic index *n* = 1.1 (**Figure 7**).

The average values of the coefficients *Ay* and *B* in the stationary segment were *Ay* = 0.41 0.04 and *<sup>B</sup>* = 0.85 0.22, which corresponds to *<sup>V</sup>*<sup>m</sup> = 58.1 16.1 ms<sup>1</sup> . The meteorological assessment of the TC intensity for this point in time was 115 kts (59.2 ms<sup>1</sup> ), which can be considered a satisfactory coincidence even taking into account the difference in altitudes for which the meteorological assessment is made (10 m) and the upper boundary of clouds for which the HLS estimate was calculated. According to experimental data, the MWS in a TC is observed at the level of 850–900 hPa: *V*m(850 hPa) [22]. The average speed at the level of cloud top *V*m(240 hPa) makes up about 70–80% of *V*m(850 hPa) [22, 23]. The surface wind speed (at 10 m height) is smaller than *V*m(850 hPa) by about 25–30% [24]. Therefore, considering that the configuration of the spiral signature depends on wind speed at the height of its existence, the HLS estimates can be compared (as a first guess) with surface data of meteorological services.

#### **3.2 Assimilation technique**

#### *3.2.1 Motivation*

As follows from the previous section, for the HLS approximation of SCRB by the LSM with the subsequent checking for stationarity, a lot of operations are needed to be performed. This requires considerable time and, therefore, is unlikely to have a

approximation of a spiral signature is performed by the selection of consequent SPAs toward the cyclone center by a given number of points. The deficiencies of conditions (32) and (33) are primarily due to two reasons. The starting of a

! <sup>∞</sup> and *<sup>A</sup>*~*<sup>y</sup> nopt*

*3.1.3 An example of HLS approximation with LSM*

stationary values, *B n* ~ *opt*

*Current Topics in Tropical Cyclone Research*

March 5, 2002 (**Figure 5**).

shown in **Figure 6**.

**Figure 5.**

*core.*

**92**

nonstationary part of a spiral signature {*i* = 1 … (*is*-1)} is due to the incorrect tracing of streamlines by clouds or the deviation of a streamline from the theoretical HLS for some reason. Regarding the "floor bounding" of the stationary part under SPA with *i*≥*ie* þ 1, it is possible to show that this feature is due to the change of wind and friction regimes near the area of maximum winds. Beginning from this point, the increase of wind speed becomes slower than that of power *n*opt. It might be assumed also that due to high wind speed, the friction factor becomes lower [21] or its dependence on wind speed becomes not linear. At that, the approximation estimates of the HLS coefficients were conducted under *n*opt divergent from their

should be selected from the results of the approximation of a spiral signature within a range where the stationary condition is satisfied. On the other hand, the distance from the center, where such divergence occurs, can be taken as an additional parameter of the cyclonic vortex (radius of divergence). Simulation experiments validated the stationarity and "divergent" property of the HLS coefficients.

As an example of the application of the above approach to the approximation of the SCRB by the HLS, we consider the estimate of the MWS from an image from the geostationary satellite GMS-5 in the visible range of TC Mitag, at 01:31 UTC on

The corresponding logarithmic normalized profile of the annotated signature is

As can be seen from **Figure 6**, the "regularity" of the profile is broken in the range lny = (� 0.6)–(� 0.7). Apparently this is due to not quite correct annotation of the final segment of the SCRB. It would have to be carried out along the steeper contrast border observed in this area. Therefore, the main part of the profile in the range lny = 0–(� 0.7) was subjected to approximation, only. The main characteristics of the signature were radius of the most distant starting point of the signature *r*<sup>0</sup> = 447 km, *r*<sup>m</sup> = 130 km, and *f* = 3.3 10–5 s�<sup>1</sup> (latitude 13.29°N). As the consecutive

*Image of TC Mitag (2002) in visible range; left image—original image; right snapshot—a contrasted image with an annotated position of the center of the eye and annotated outer edge of the SCRB adjacent to the cyclone*

! 0. Therefore, the HLS coefficients

prospect for use in operational practice. In addition, annotation of the spiral structure on the image is, to a certain extent, a subjective process as was shown by the example of **Figure 6**. Moreover, sometimes, as was shown in [11], the LSM approximation leads to nonphysical values of HLS coefficients due to incorrect annotation of the spiral structure. The main problem of the HLS approximation with the LSM is that, by its nature, this technique is applicable mainly to so-called thin or clearly depicted spiral structures, which have a small width or sharp contour in satellite or radar images, and are suitable for its uniquely annotation. However, the same thin spiraling bands very often turn out to be squall lines, which are not related to streamlines. These circumstances stimulated the search for another technique that would be applicable to typical SCRBs, having a noticeable width and fuzzy contours. This technique, called the assimilative technique, is discussed below.

Specialized Meteorological Center (RSMC-JMA; Tokyo, Japan). IR spiral rainband patterns pertain to the top of cyclone's cloud system (280–230 hPa, altitude 10– 12 km). The rationale for comparison of the MWS resulted from the HLS processing

UTC on August 14, 2002, till 12:31 UTC on August 18, 2002. Their results were subsequently united to six estimates. The length of the period covered by the analysis was 84 h. The estimates of the MWS are used to identify the stages of intensification and weakening of TC which are satisfactorily synchronized with the respective data of the Navy/Air Force Joint Typhoon Warning Center (JTWC). The correlation with data of the Regional Specialized Meteorological Center (RSMC-JMA) is observed only for the stage of intensification. Data of JTWC did not correlate either with RSMC data at the stage of TC weakening. In terms of the absolute value of the MWS, JTWC data exceed RSMC data for the moment of maximum TC intensification. It should be noted that data of the mentioned meteorological services differ from each other by 33% in terms of maximum TC intensity. The estimates of the MWS based on the HLS approximation occupy intermediate position between the above data. A detailed description of this study is given in [13].

Twenty-one images were processed which corresponded to the period from 21:31

In the current paper, an example of application of the HLS enhanced assimilation technique under processing IR images of a TC is provided exploring observation data of Hurricane Rita (AL182005) from geostationary satellite GOES-10 when the hurricane was in Gulf of Mexico. An image acquired at 6:30 UTC 23 September

The image contains two spiral rainbands. They are the outer impressive band that starts from the northwest corner of the image and the inner one that starts from the north. Both bands reach the core of the cyclone close to its south sector. The radius of maximum wind has been assessed to be 70 km as a distance from the eye's

*IR image of Hurricane Rita 09/23/2005 at 06:30 UTC (eye center location: 26.58° N; 90.53° W) from GOES-10 geostationary satellite; source: Naval Research Laboratory (NRL, USA; http://www.nrlmry.navy.mil/sat\_ products.html); file name, 20050923\_0630\_goes10\_x\_ir1km\_18LRITA\_120kts-924mb-265 N-907 W; original geographic grid is applied at intervals of two degrees; color temperature scale (roughly), blue is "40°*

with the best track data is the same as mentioned above.

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

*4.1.2 Hurricane Rita (2005)*

**Figure 8.**

**95**

*C," green is "60°C," and red is "70°C."*

2005 has been processed (**Figure 8**).

#### *3.2.2 A principle of assimilative technique*

When choosing a technique for determining the HLS coefficients (3) and (4), it should be noted that a spiral rainband observable on a radar image has a finite width and is a mapping of the resultant involvement of cloud-rain particles in the region affected by the streamline. In this case, parameters of the HLS describing all possible streamlines within the rainband should be considered as "equally possible." In this technique, an approach based on the allocation of HLSs "fitting" into geometric boundaries of a rainband and determining the "expected" (mean) and "modal" HLSs was used. The fitting spirals were designated as "signatural" HLSs. The signatural HLSs have coefficients *A* (3) and *B* (4) which are determined using different combinations of *V*m, *k*, and *n*. This triplet of parameters is hereafter referred to by the term "physical characteristics." The mean and modal values of a selected physical parameter over all signatural HLSs are taken as the corresponding statistical estimates of this parameter. The above procedure is called "assimilative HLS approximation." For the first time, this technique was used for processing of satellite infrared images of a TC in [13]. Further, it was improved in [14]. The primary changes in enhanced technique are the limitation of a range of possible maximum wind speeds resulted from the spiral band processing and finding the modal value of *V*<sup>m</sup> distribution over all signatural HLSs in addition to the mean value. The technique was called "assimilative" due to a priori assignment of the type of the approximating function (HLS) and the range of variation of its basic parameters. The essentials of the technique are provided in [14].

## **4. Application of HLS assimilation technique for assessment of the maximum wind speed from satellite and radar data**

## **4.1 HLS assimilation approximation of spiral rainbands applied to satellite IR images**

#### *4.1.1 Typhoon Phanfone (2002)*

The first attempt to apply the HLS assimilation approximation to spiral rainbands on satellite IR images was undertaken by exploring data from the GMS-5 geostationary satellite during the monitoring of typhoon Phanfone existing in the Pacific Northwest in August 2002 [13]. The source of satellite data was the archive of IR images from the Naval Research Laboratory (NRL, USA; http://www.nrlmry. navy.mil/sat\_products.html). Meteorological data were taken from the Navy/Air Force Joint Typhoon Warning Center (JTWC-WP; Hawaii, USA) and Regional

### *The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity DOI: http://dx.doi.org/10.5772/intechopen.88683*

Specialized Meteorological Center (RSMC-JMA; Tokyo, Japan). IR spiral rainband patterns pertain to the top of cyclone's cloud system (280–230 hPa, altitude 10– 12 km). The rationale for comparison of the MWS resulted from the HLS processing with the best track data is the same as mentioned above.

Twenty-one images were processed which corresponded to the period from 21:31 UTC on August 14, 2002, till 12:31 UTC on August 18, 2002. Their results were subsequently united to six estimates. The length of the period covered by the analysis was 84 h. The estimates of the MWS are used to identify the stages of intensification and weakening of TC which are satisfactorily synchronized with the respective data of the Navy/Air Force Joint Typhoon Warning Center (JTWC). The correlation with data of the Regional Specialized Meteorological Center (RSMC-JMA) is observed only for the stage of intensification. Data of JTWC did not correlate either with RSMC data at the stage of TC weakening. In terms of the absolute value of the MWS, JTWC data exceed RSMC data for the moment of maximum TC intensification. It should be noted that data of the mentioned meteorological services differ from each other by 33% in terms of maximum TC intensity. The estimates of the MWS based on the HLS approximation occupy intermediate position between the above data. A detailed description of this study is given in [13].

### *4.1.2 Hurricane Rita (2005)*

prospect for use in operational practice. In addition, annotation of the spiral structure on the image is, to a certain extent, a subjective process as was shown by the example of **Figure 6**. Moreover, sometimes, as was shown in [11], the LSM approximation leads to nonphysical values of HLS coefficients due to incorrect annotation of the spiral structure. The main problem of the HLS approximation with the LSM is that, by its nature, this technique is applicable mainly to so-called thin or clearly depicted spiral structures, which have a small width or sharp contour in satellite or radar images, and are suitable for its uniquely annotation. However, the same thin spiraling bands very often turn out to be squall lines, which are not related to streamlines. These circumstances stimulated the search for another technique that would be applicable to typical SCRBs, having a noticeable width and fuzzy contours. This technique, called the assimilative technique, is discussed below.

When choosing a technique for determining the HLS coefficients (3) and (4), it should be noted that a spiral rainband observable on a radar image has a finite width and is a mapping of the resultant involvement of cloud-rain particles in the region affected by the streamline. In this case, parameters of the HLS describing all possible streamlines within the rainband should be considered as "equally possible." In this technique, an approach based on the allocation of HLSs "fitting" into geometric boundaries of a rainband and determining the "expected" (mean) and "modal" HLSs was used. The fitting spirals were designated as "signatural" HLSs. The signatural HLSs have coefficients *A* (3) and *B* (4) which are determined using different combinations of *V*m, *k*, and *n*. This triplet of parameters is hereafter referred to by the term "physical characteristics." The mean and modal values of a selected physical parameter over all signatural HLSs are taken as the corresponding statistical estimates of this parameter. The above procedure is called "assimilative HLS approximation." For the first time, this technique was used for processing of satellite infrared images of a TC in [13]. Further, it was improved in [14]. The primary changes in enhanced technique are the limitation of a range of possible maximum wind speeds resulted from the spiral band processing and finding the modal value of *V*<sup>m</sup> distribution over all signatural HLSs in addition to the mean value. The technique was called "assimilative" due to a priori assignment of the type of the approximating function (HLS) and the range of variation of its basic param-

*3.2.2 A principle of assimilative technique*

*Current Topics in Tropical Cyclone Research*

eters. The essentials of the technique are provided in [14].

**images**

**94**

*4.1.1 Typhoon Phanfone (2002)*

**maximum wind speed from satellite and radar data**

**4. Application of HLS assimilation technique for assessment of the**

The first attempt to apply the HLS assimilation approximation to spiral rainbands on satellite IR images was undertaken by exploring data from the GMS-5 geostationary satellite during the monitoring of typhoon Phanfone existing in the Pacific Northwest in August 2002 [13]. The source of satellite data was the archive of IR images from the Naval Research Laboratory (NRL, USA; http://www.nrlmry. navy.mil/sat\_products.html). Meteorological data were taken from the Navy/Air Force Joint Typhoon Warning Center (JTWC-WP; Hawaii, USA) and Regional

**4.1 HLS assimilation approximation of spiral rainbands applied to satellite IR**

In the current paper, an example of application of the HLS enhanced assimilation technique under processing IR images of a TC is provided exploring observation data of Hurricane Rita (AL182005) from geostationary satellite GOES-10 when the hurricane was in Gulf of Mexico. An image acquired at 6:30 UTC 23 September 2005 has been processed (**Figure 8**).

The image contains two spiral rainbands. They are the outer impressive band that starts from the northwest corner of the image and the inner one that starts from the north. Both bands reach the core of the cyclone close to its south sector. The radius of maximum wind has been assessed to be 70 km as a distance from the eye's

#### **Figure 8.**

*IR image of Hurricane Rita 09/23/2005 at 06:30 UTC (eye center location: 26.58° N; 90.53° W) from GOES-10 geostationary satellite; source: Naval Research Laboratory (NRL, USA; http://www.nrlmry.navy.mil/sat\_ products.html); file name, 20050923\_0630\_goes10\_x\_ir1km\_18LRITA\_120kts-924mb-265 N-907 W; original geographic grid is applied at intervals of two degrees; color temperature scale (roughly), blue is "40° C," green is "60°C," and red is "70°C."*

Atlantic basin from August 30 to September 12, 2017, and reached category 5 intensity. The radar data from airborne radars were taken from the HRD's Atlantic Oceanographic and Meteorological Laboratory (AOML) website (http://www.aoml. noaa.gov/hrd/data\_sub/radar.html). The best track and aircraft data were taken from the National Hurricane Center's Tropical Cyclone Report [26]. The data pertain to one of eight aircraft missions to the cyclone. This mission was conducted from morning to afternoon September 5, 2017. The numerical outcomes of the HLS

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

The data of the first two time points were acquired at altitude from 5 km to 6 km, the six others from 2 km to 3 km. At the higher altitude, the MWS was approximately from 80 kts to 100 kts; at the lower level, the mean modal wind speed was about 161 5 kts. This estimate is for a middle time point at approximately 10:45 UTC September 5 and manifests the maximum TC intensity that was estimated by the HLS approximation of all period of the HLS application from 21:16 UTC September 3 till 23:51 UTC September 8. As follows from [26], the wind speed of 164 kts measured directly by the aircraft at the flight level occurred approximately 10 h later. The best track MWS, which is for 10 m altitude, was about 150 kts. As per the best track data, the maximum intensity was 154 kts from noon September 5 to approximately 18:00 UTC September 6. The HLS estimates for this period are provided in [14] and amounted to 158–146 kts. These results indicate the satisfactory agreement of the HLS approach with in situ data (compare 161 kts and 164 kts). Lower speeds evaluated at high levels (two first time points in **Table 2**) follow the contemporary understanding of the vertical tangential wind profile in a TC (e.g., [24]) that presumes the decreasing of the wind speed up from the level of the maximum wind at altitude approximately 1–1.3 km (850–900 mb). The average error of the HLS approximation for the entire observation time ( 130 h) of the comparative analysis with the best track data given in [14] was no more than 5%.

*4.2.2 The maximum wind speed in TC Irma (2017) by the HLS approximation of the*

During the passage of the TC Irma near the island of Puerto Rico, the cyclone was in the survey zone of the weather radar WSR-88D installed in the city of San Juan. The base for processing was the reflectivity image at 21:15 UTC 6 September

**hours**

 6089 8:41:44 56.68 53 103.0 2.0 3.9 30 5244 8:47:58 56.78 40 77.8 3.0 5.8 26 2999 9:27:39 57.45 75.5 146.8 2.5 4.9 34 25a 2999 9:43:20 57.72 76 147.7 4 7.8 33 25b 3000 9:48:04 57.80 89 173.0 1 1.9 30 2510 10:31:26 58.52 79 153.6 3 5.8 34 2369 11:01:38 59.02 89 173.0 7 13.6 30 2531 12:01:32 60.02 88 171.4 4 7.8 31

*Results of the HLS approximation of radar spiral signatures of TC Irma acquired during airborne sounding on*

*V***m\_mdl Error**

**m s<sup>1</sup> kts m s<sup>1</sup> kts**

*V***m\_mdl**

*R***m, km**

**UTC Observation time,**

*rainband signatures from the coastal San Juan radar in comparison with best track*

approximation are listed in **Table 2**.

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

*data*

**Time point no.**

**Table 2.**

**97**

**Altitude, m**

*September 5, 2017 (collection index 20170905H1).*

#### **Figure 9.**

*Illustration of HLS approximation (black spiral sector) of inner rainband (left picture) and the west sector of outer rainband (right picture) shown in Figure 8.*


#### **Table 1.**

*Maximum wind speed derived from HLS approximation in comparison with the best track data of hurricane Rita.*

center to the middle of a convective eyewall. Illustrations of the HLS approximation of both rainbands are provided in **Figure 9**. Resulted maximum wind speeds in comparison with the best track data [25] are presented in **Table 1**.

Following the HLS technique feature that presumes to use for approximation a signature closest to the TC core (where a streamline impact on cloud organization is most pronounced), the western sector of the outer rainband was approximated only. As follows from **Table 1**, the approximation of both rainbands results in the modal maximum wind speeds close to the best track speed with the acceptable accuracies (less than 12%). It should be noted that among other things, this illustrative example also shows the possibility of increasing the reliability of the HLS approximation by combining multiband estimates. In particular, the combined weighted estimate of MWS based on the data provided in **Table 1** is 116.6 7.4 kts.

## **4.2 HLS assimilation approximation of spiral rainbands applied to airborne and coastal radar images**

### *4.2.1 Comparison of operational and HLS estimates of the intensity of TC Irma (AL112017) based on the airborne and the best track data*

Maximum wind speeds in Hurricane Irma (AL112017) were estimated in [14] using its rainband radar signatures acquired by the NOAA Hurricane Research Division during routine aircraft missions into the hurricane. Most appropriate radar and other accompanied data were taken from the NOAA Hurricane Research Division (HRD) archive acquired for Hurricane Irma (AL112017) that existed in the

#### *The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity DOI: http://dx.doi.org/10.5772/intechopen.88683*

Atlantic basin from August 30 to September 12, 2017, and reached category 5 intensity. The radar data from airborne radars were taken from the HRD's Atlantic Oceanographic and Meteorological Laboratory (AOML) website (http://www.aoml. noaa.gov/hrd/data\_sub/radar.html). The best track and aircraft data were taken from the National Hurricane Center's Tropical Cyclone Report [26]. The data pertain to one of eight aircraft missions to the cyclone. This mission was conducted from morning to afternoon September 5, 2017. The numerical outcomes of the HLS approximation are listed in **Table 2**.

The data of the first two time points were acquired at altitude from 5 km to 6 km, the six others from 2 km to 3 km. At the higher altitude, the MWS was approximately from 80 kts to 100 kts; at the lower level, the mean modal wind speed was about 161 5 kts. This estimate is for a middle time point at approximately 10:45 UTC September 5 and manifests the maximum TC intensity that was estimated by the HLS approximation of all period of the HLS application from 21:16 UTC September 3 till 23:51 UTC September 8. As follows from [26], the wind speed of 164 kts measured directly by the aircraft at the flight level occurred approximately 10 h later. The best track MWS, which is for 10 m altitude, was about 150 kts. As per the best track data, the maximum intensity was 154 kts from noon September 5 to approximately 18:00 UTC September 6. The HLS estimates for this period are provided in [14] and amounted to 158–146 kts. These results indicate the satisfactory agreement of the HLS approach with in situ data (compare 161 kts and 164 kts). Lower speeds evaluated at high levels (two first time points in **Table 2**) follow the contemporary understanding of the vertical tangential wind profile in a TC (e.g., [24]) that presumes the decreasing of the wind speed up from the level of the maximum wind at altitude approximately 1–1.3 km (850–900 mb). The average error of the HLS approximation for the entire observation time ( 130 h) of the comparative analysis with the best track data given in [14] was no more than 5%.

## *4.2.2 The maximum wind speed in TC Irma (2017) by the HLS approximation of the rainband signatures from the coastal San Juan radar in comparison with best track data*

During the passage of the TC Irma near the island of Puerto Rico, the cyclone was in the survey zone of the weather radar WSR-88D installed in the city of San Juan. The base for processing was the reflectivity image at 21:15 UTC 6 September


#### **Table 2.**

*Results of the HLS approximation of radar spiral signatures of TC Irma acquired during airborne sounding on September 5, 2017 (collection index 20170905H1).*

center to the middle of a convective eyewall. Illustrations of the HLS approximation of both rainbands are provided in **Figure 9**. Resulted maximum wind speeds in

*Maximum wind speed derived from HLS approximation in comparison with the best track data of hurricane*

*Illustration of HLS approximation (black spiral sector) of inner rainband (left picture) and the west sector of*

**Modal MWS derived from HLS approximation Best track\***

**Rainband Maximum wind speed, kts**

Inner band 117.7 8.7 115

Following the HLS technique feature that presumes to use for approximation a signature closest to the TC core (where a streamline impact on cloud organization is most pronounced), the western sector of the outer rainband was approximated only. As follows from **Table 1**, the approximation of both rainbands results in the modal maximum wind speeds close to the best track speed with the acceptable accuracies (less than 12%). It should be noted that among other things, this illustrative example also shows the possibility of increasing the reliability of the HLS approximation by combining multiband estimates. In particular, the combined weighted estimate of MWS based on the data provided in **Table 1** is 116.6 7.4 kts.

**4.2 HLS assimilation approximation of spiral rainbands applied to airborne and**

Maximum wind speeds in Hurricane Irma (AL112017) were estimated in [14] using its rainband radar signatures acquired by the NOAA Hurricane Research Division during routine aircraft missions into the hurricane. Most appropriate radar and other accompanied data were taken from the NOAA Hurricane Research Division (HRD) archive acquired for Hurricane Irma (AL112017) that existed in the

*4.2.1 Comparison of operational and HLS estimates of the intensity of TC Irma*

*(AL112017) based on the airborne and the best track data*

comparison with the best track data [25] are presented in **Table 1**.

Western sector of outer band 114.1 13.6

**coastal radar images**

**Figure 9.**

*\**

**Table 1.**

*Rita.*

**96**

*Extrapolated from [25].*

*outer rainband (right picture) shown in Figure 8.*

*Current Topics in Tropical Cyclone Research*

given in [11, 13]. The results obtained suggest that the development and improvement of the proposed approach will make it possible to use the radar and satellite information more fully to assess the physical characteristics of a TC. The HLS approach to retrieve the TC's intensity is particularly benefited for ground-based coastal radar probing of a TC before its landfall and the absence of aircraft recon-

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

naissance missions.

**Author details**

Environmental Protection Agency, Washington D.C., USA

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: yurchak.boris@epa.gov

provided the original work is properly cited.

Boris Yurchak

**99**

**Figure 10.**

*Left image—reflectivity image of TC Irma from San Juan WSR-88D (2115 UTC September 6, 2017 [26]); right image—illustration of HLS approximation of the NW rainband.*

2017 that is shown in **Figure 10** (left image) when the TC center was located at 18.9° N/65.4° W [25]. The image refers to the beginning of the cyclone weakening stage and has the double eyewall structure that indicates a double wind maximum. Under the HLS approximation, the RMW was selected within the outer eyewall, as follows from findings in [27, 28], and estimated to be about 62 km.

The modal and mean maximum wind speeds estimated from the HLS approximation of the northwest rainband (**Figure 10**, right image) were 74 m s<sup>1</sup> (143.9 kts) and 75.4 m s<sup>1</sup> (146.6 kts), respectively. Accordingly, these speeds are approximately by 6 kts and 3 kts lower than the best track speed 150 kts at this time.

## **5. Summary**

A new approach to use the characteristics of spiral cloud-rain bands of a tropical cyclone, observed by ground-based and aircraft radars, as well as satellites in visible and infrared wavelengths, is considered. The physical substantiation of the proposed approach is (1) the assumption about the orientation of the SCRBs mainly along the streamlines and (2) the analytically derived streamline equation in the form of the hyperbolic-logarithmic spiral. It is shown that the logarithmic spiral usually used to describe the configuration of SCRB is only a special case of the HLS. Unlike the empirically applying logarithmic spiral, the HLS coefficients depend on the MWS and the friction factor. The analysis of changes in the configuration of the HLS depending on the intensity of a TC is conducted. An explanation for experimentally observed phenomenon of the "rounding" of the SCRB (i.e., a decrease in the crossing angle) with increasing intensity of the TC as a whole, as well as with approaching to the radius of maximum wind, is proposed. The similarity of the SCRB configuration to the logarithmic spiral in some cases is interpreted also. The technique for approximation of a SCRB in the form of the HLS based on the least squares method and on the assimilation procedure was developed for obtaining MWS estimates at the height of the SCRB location. Testing of the proposed approach was performed based on literature data from ground-based coastal and aircraft radars, data of regular aircraft reconnaissance missions in the TC, and satellite data available via the Internet. A certain disadvantage of the method is its applicability, as a rule, for mature tropical cyclones, where its cloudy field manifests well-defined SCRBs and a clearly defined circulation center (eye center). On the other hand, the physically based configuration of a SCRB as the hyperboliclogarithmic spiral allows one to develop the method for estimating the position of the circulation center with the eye covered with clouds, examples of which are

## *The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity DOI: http://dx.doi.org/10.5772/intechopen.88683*

given in [11, 13]. The results obtained suggest that the development and improvement of the proposed approach will make it possible to use the radar and satellite information more fully to assess the physical characteristics of a TC. The HLS approach to retrieve the TC's intensity is particularly benefited for ground-based coastal radar probing of a TC before its landfall and the absence of aircraft reconnaissance missions.

## **Author details**

2017 that is shown in **Figure 10** (left image) when the TC center was located at 18.9° N/65.4° W [25]. The image refers to the beginning of the cyclone weakening stage and has the double eyewall structure that indicates a double wind maximum. Under the HLS approximation, the RMW was selected within the outer eyewall, as follows

*Left image—reflectivity image of TC Irma from San Juan WSR-88D (2115 UTC September 6, 2017 [26]);*

The modal and mean maximum wind speeds estimated from the HLS approximation of the northwest rainband (**Figure 10**, right image) were 74 m s<sup>1</sup> (143.9 kts) and 75.4 m s<sup>1</sup> (146.6 kts), respectively. Accordingly, these speeds are approximately by 6 kts and 3 kts lower than the best track speed 150 kts at this time.

A new approach to use the characteristics of spiral cloud-rain bands of a tropical cyclone, observed by ground-based and aircraft radars, as well as satellites in visible and infrared wavelengths, is considered. The physical substantiation of the proposed approach is (1) the assumption about the orientation of the SCRBs mainly along the streamlines and (2) the analytically derived streamline equation in the form of the hyperbolic-logarithmic spiral. It is shown that the logarithmic spiral usually used to describe the configuration of SCRB is only a special case of the HLS. Unlike the empirically applying logarithmic spiral, the HLS coefficients depend on the MWS and the friction factor. The analysis of changes in the configuration of the HLS depending on the intensity of a TC is conducted. An explanation for experimentally observed phenomenon of the "rounding" of the SCRB (i.e., a decrease in the crossing angle) with increasing intensity of the TC as a whole, as well as with approaching to the radius of maximum wind, is proposed. The similarity of the SCRB configuration to the logarithmic spiral in some cases is interpreted also. The technique for approximation of a SCRB in the form of the HLS based on the least squares method and on the assimilation procedure was developed for obtaining MWS estimates at the height of the SCRB location. Testing of the proposed approach was performed based on literature data from ground-based coastal and aircraft radars, data of regular aircraft reconnaissance missions in the TC, and satellite data available via the Internet. A certain disadvantage of the method is its applicability, as a rule, for mature tropical cyclones, where its cloudy field manifests well-defined SCRBs and a clearly defined circulation center (eye center). On the other hand, the physically based configuration of a SCRB as the hyperboliclogarithmic spiral allows one to develop the method for estimating the position of the circulation center with the eye covered with clouds, examples of which are

from findings in [27, 28], and estimated to be about 62 km.

*right image—illustration of HLS approximation of the NW rainband.*

*Current Topics in Tropical Cyclone Research*

**5. Summary**

**98**

**Figure 10.**

Boris Yurchak Environmental Protection Agency, Washington D.C., USA

\*Address all correspondence to: yurchak.boris@epa.gov

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Dvorak VF. Tropical cyclone intensity analysis and forecasting from satellite imagery. Monthly Weather Review. 1975;**103**:420-430

[2] Olander TL, Velden CS. The advanced Dvorak technique: Continued development of objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Weather and Forecasting. 2007;**22**: 287-298

[3] Wexler H. Structure of hurricanes as determined by radar. Annals of the New York Academy of Sciences. 1947;**48**: 821-844

[4] Senn HV, Hiser HW, Bourret RC. Studies of Hurricane Spiral Bands as Observed on Radar. National Hurricane Research Project. U.S. Department of Commerce. Report No. 12; 1957. 13 p

[5] Fung IY. The organization of spiral rain bands in a hurricane. Doctoral dissertation thesis. Massachusetts: Massachusetts Institute of Technology; 1977. 140 p

[6] Fernandez W. Organization and motion of the spiral rainbands in hurricanes: A review. Ciencia y Tecnología. 1982;**6**(1–2):49-98

[7] Houze RA Jr. Clouds in tropical cyclones. Review. Monthly Weather Review. 2010;**138**:293-344

[8] Wang Y. Recent research progress on tropical cyclone structure and intensity. Tropical Cyclone Research and Review. 2012;**1**(2):254-275

[9] Lahiri A. A study of cloud spirals of tropical cyclones. Mausam. 1981;**32**(2): 155-158

[10] Burton A, Velden C. Curved band patterns. In: Proceedings of the International Workshop on Satellite

Analysis of Tropical Cyclones. Report Number TCP-52. Honolulu, Hawaii, USA: World Meteorological Organization; 2011, 2011. pp. 65-67

Gidrometeoizdat, Leningrad, 1972. 416

*DOI: http://dx.doi.org/10.5772/intechopen.88683*

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity*

secondary wind maxima, and the evolution of the hurricane vortex. Journal of the Atmospheric Sciences.

[28] Samsury CE, Zipser EJ. Secondary wind maxima in hurricanes: Airflow and relationship to rainbands. Monthly Weather Review. 1995;**123**:3502-3517

1982;**39**(2):395-411

Theoretical and Applied. Kennelly Press. (Copyright, 1944 by Hewson EW and

[20] Anthes RA. Tropical cyclones. Their evolution, structure and effects. AMS, Meteorological Monographs. 1982;

Reinhold TA. Reduced drag coefficient for high wind speeds in tropical cyclones. Nature. 2003;**422**:279-283

[22] Frank WM. The structure and energetics of the tropical cyclone I. storm structure. Monthly Weather Review. 1977;**105**:1119-1135

[23] Franklin JL, Lord SJ, Feuer SE, Marks FD Jr. The kinematic structure of hurricane gloria (1985) determined from nested analysis of dropwindsonde and doppler radar data. Monthly Weather Review. 1993;**121**:2433-2450

[24] Franklin JL, Black ML, Valde K. GPS

[25] Knabb KD, Brown DP, Rhome JR. Tropical Cyclone Report Hurricane Rita,

Hurricane Center; 2006. 36 p. Available online: https://www.nhc.noaa.gov/data/

[26] Cangialosi JP, Latto AS, Berg R. National Hurricane Center Tropical Cyclone Report. Hurricane Irma (AL112017). 30 August-12 September 2017; 2018. 111 p. http://www.aoml. noaa.gov/hrd/Storm\_pages/irma2017/

[27] Willoughby HE, Clos JA,

Shoreibah MG. Concentric eye walls,

18–26 September 2005. National

tcr/AL182005\_Rita.pdf

dropwindsonde wind profiles in hurricanes and their operational implications. Weather and Forecasting.

2003;**18**:32-44

index.html

**101**

[19] Wendell HE. Meteorology-

Longley RW); 2007. 468 p

[21] Powell MD, Vickery PJ,

p.[in Russian]

**19**(41):210

[11] Yurchak BS. Description of cloudrain bands in a tropical cyclone by a hyperbolic-logarithmic spiral. Russian Meteorology and Hydrology. 2007; **32**(1):8-18

[12] Yurchak BS. Formula for spiral cloud-rain bands of a tropical cyclone. In: Proceedings of the 28th Conference on Hurricanes and Tropical Meteorology. Orlando, FL; 2008, 2008. 5 p. Available online: http://ams.confex. com/ams/28Hurricanes/techprogram/ programexpanded\_471.htm

[13] Yurchak BS. Estimation of tropical cyclone intensity from the satellite infrared images of its spiral cloud bands. Russian Meteorology and Hydrology. 2018;**43**(9):581-590

[14] Yurchak BS. An estimate of the hurricane's intensity from radar data using hyperbolic-logarithmic approximation. International Journal of Remote Sensing. 2019. DOI: 10.1080/ 01431161.2019.1635288

[15] Willoughby HE, Marks FD, Feinberg RJ. Stationary and moving convective bands in hurricanes. Journal of the Atmospheric Sciences. 1984; **41**(22):3189-3211

[16] Willoughby HE. The dynamics of the tropical cyclone core. Australian Meteorological Magazine. 1988;**36**:183-191

[17] Batchelor GK. An Introduction to Fluid Dynamics. Cambridge: Cambridge University Press; 1967. 634 p

[18] Guralnik II, Dubinskii GP, Mamikonova SV. The Meteorology. Handbook for Colleges.

*The Use of a Spiral Band Model to Estimate Tropical Cyclone Intensity DOI: http://dx.doi.org/10.5772/intechopen.88683*

Gidrometeoizdat, Leningrad, 1972. 416 p.[in Russian]

**References**

287-298

821-844

1977. 140 p

[1] Dvorak VF. Tropical cyclone intensity analysis and forecasting from satellite imagery. Monthly Weather

*Current Topics in Tropical Cyclone Research*

[2] Olander TL, Velden CS. The

advanced Dvorak technique: Continued development of objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Weather and Forecasting. 2007;**22**:

Analysis of Tropical Cyclones. Report Number TCP-52. Honolulu, Hawaii,

Organization; 2011, 2011. pp. 65-67

[12] Yurchak BS. Formula for spiral cloud-rain bands of a tropical cyclone. In: Proceedings of the 28th Conference

Meteorology. Orlando, FL; 2008, 2008. 5 p. Available online: http://ams.confex. com/ams/28Hurricanes/techprogram/

[13] Yurchak BS. Estimation of tropical cyclone intensity from the satellite infrared images of its spiral cloud bands. Russian Meteorology and Hydrology.

[14] Yurchak BS. An estimate of the hurricane's intensity from radar data

approximation. International Journal of Remote Sensing. 2019. DOI: 10.1080/

[16] Willoughby HE. The dynamics of the

Meteorological Magazine. 1988;**36**:183-191

[17] Batchelor GK. An Introduction to Fluid Dynamics. Cambridge: Cambridge

using hyperbolic-logarithmic

[15] Willoughby HE, Marks FD, Feinberg RJ. Stationary and moving convective bands in hurricanes. Journal of the Atmospheric Sciences. 1984;

tropical cyclone core. Australian

University Press; 1967. 634 p

[18] Guralnik II, Dubinskii GP, Mamikonova SV. The Meteorology.

Handbook for Colleges.

01431161.2019.1635288

**41**(22):3189-3211

on Hurricanes and Tropical

programexpanded\_471.htm

2018;**43**(9):581-590

[11] Yurchak BS. Description of cloudrain bands in a tropical cyclone by a hyperbolic-logarithmic spiral. Russian Meteorology and Hydrology. 2007;

USA: World Meteorological

**32**(1):8-18

[3] Wexler H. Structure of hurricanes as determined by radar. Annals of the New York Academy of Sciences. 1947;**48**:

[4] Senn HV, Hiser HW, Bourret RC. Studies of Hurricane Spiral Bands as Observed on Radar. National Hurricane Research Project. U.S. Department of Commerce. Report No. 12; 1957. 13 p

[5] Fung IY. The organization of spiral rain bands in a hurricane. Doctoral dissertation thesis. Massachusetts: Massachusetts Institute of Technology;

[6] Fernandez W. Organization and motion of the spiral rainbands in hurricanes: A review. Ciencia y Tecnología. 1982;**6**(1–2):49-98

[7] Houze RA Jr. Clouds in tropical cyclones. Review. Monthly Weather

[8] Wang Y. Recent research progress on tropical cyclone structure and intensity. Tropical Cyclone Research and Review.

[9] Lahiri A. A study of cloud spirals of tropical cyclones. Mausam. 1981;**32**(2):

[10] Burton A, Velden C. Curved band patterns. In: Proceedings of the International Workshop on Satellite

Review. 2010;**138**:293-344

2012;**1**(2):254-275

155-158

**100**

Review. 1975;**103**:420-430

[19] Wendell HE. Meteorology-Theoretical and Applied. Kennelly Press. (Copyright, 1944 by Hewson EW and Longley RW); 2007. 468 p

[20] Anthes RA. Tropical cyclones. Their evolution, structure and effects. AMS, Meteorological Monographs. 1982; **19**(41):210

[21] Powell MD, Vickery PJ, Reinhold TA. Reduced drag coefficient for high wind speeds in tropical cyclones. Nature. 2003;**422**:279-283

[22] Frank WM. The structure and energetics of the tropical cyclone I. storm structure. Monthly Weather Review. 1977;**105**:1119-1135

[23] Franklin JL, Lord SJ, Feuer SE, Marks FD Jr. The kinematic structure of hurricane gloria (1985) determined from nested analysis of dropwindsonde and doppler radar data. Monthly Weather Review. 1993;**121**:2433-2450

[24] Franklin JL, Black ML, Valde K. GPS dropwindsonde wind profiles in hurricanes and their operational implications. Weather and Forecasting. 2003;**18**:32-44

[25] Knabb KD, Brown DP, Rhome JR. Tropical Cyclone Report Hurricane Rita, 18–26 September 2005. National Hurricane Center; 2006. 36 p. Available online: https://www.nhc.noaa.gov/data/ tcr/AL182005\_Rita.pdf

[26] Cangialosi JP, Latto AS, Berg R. National Hurricane Center Tropical Cyclone Report. Hurricane Irma (AL112017). 30 August-12 September 2017; 2018. 111 p. http://www.aoml. noaa.gov/hrd/Storm\_pages/irma2017/ index.html

[27] Willoughby HE, Clos JA, Shoreibah MG. Concentric eye walls, secondary wind maxima, and the evolution of the hurricane vortex. Journal of the Atmospheric Sciences. 1982;**39**(2):395-411

[28] Samsury CE, Zipser EJ. Secondary wind maxima in hurricanes: Airflow and relationship to rainbands. Monthly Weather Review. 1995;**123**:3502-3517

**103**

Section 3

Remote Sensing

and Modeling

Section 3

Remote Sensing and Modeling

**105**

**Chapter 6**

**Abstract**

and discuss future plans.

**1. Introduction**

**Keywords:** satellites, models, data, data services, NASA

weather and climate on different scales.

NASA Global Satellite and Model

Data Products and Services for

*Zhong Liu, David Meyer, Chung-Lin Shie and Angela Li*

The lack of observations over vast tropical oceans is a major challenge for tropical cyclone research. Satellite observations and model reanalysis data play an important role in filling these gaps. Established in the mid-1980s, the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA data centers, archives and distributes data from several Earth science disciplines such as precipitation, atmospheric dynamics, atmospheric composition, and hydrology, including well-known NASA satellite missions (e.g., TRMM, GPM) and model assimilation projects (MERRA-2). Acquiring datasets suitable for tropical cyclone research in a large data archive is a challenge for many, especially for those who are not familiar with satellite or model data. Over the years, the GES DISC has developed user-friendly data services. For example, Giovanni is an online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data and software. In this chapter, we will describe data and services at the GES DISC with emphasis on tropical cyclone research. We will also present two case studies

Tropical cyclones form over vast tropical oceans where in situ observations are sparse and discontinuous. The lack of observational data over these areas historically has been a major obstacle for tropical cyclone research and other weather and climate-related studies. Understanding the complex atmospheric and oceanic processes, and their interactions at multiple scales (e.g., convective, synoptic) over the life cycle of tropical cyclones, requires multiscale, multi-platform observational networks. It has been a great challenge to design, deploy, and maintain such networks without interruption, particularly in the harsh environments imposed by these extreme phenomena. Since the satellite era began, data collected from satellites, along with model reanalysis data, have played an important role in providing continuous global observations, filling in data gaps, and enabling research on

The concept of using satellites to observe Earth's weather and climate was developed as early as 1946 [1]. NASA launched the first successful, weather satellite,

Tropical Cyclone Research

## **Chapter 6**

## NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research

*Zhong Liu, David Meyer, Chung-Lin Shie and Angela Li*

## **Abstract**

The lack of observations over vast tropical oceans is a major challenge for tropical cyclone research. Satellite observations and model reanalysis data play an important role in filling these gaps. Established in the mid-1980s, the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA data centers, archives and distributes data from several Earth science disciplines such as precipitation, atmospheric dynamics, atmospheric composition, and hydrology, including well-known NASA satellite missions (e.g., TRMM, GPM) and model assimilation projects (MERRA-2). Acquiring datasets suitable for tropical cyclone research in a large data archive is a challenge for many, especially for those who are not familiar with satellite or model data. Over the years, the GES DISC has developed user-friendly data services. For example, Giovanni is an online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data and software. In this chapter, we will describe data and services at the GES DISC with emphasis on tropical cyclone research. We will also present two case studies and discuss future plans.

**Keywords:** satellites, models, data, data services, NASA

## **1. Introduction**

Tropical cyclones form over vast tropical oceans where in situ observations are sparse and discontinuous. The lack of observational data over these areas historically has been a major obstacle for tropical cyclone research and other weather and climate-related studies. Understanding the complex atmospheric and oceanic processes, and their interactions at multiple scales (e.g., convective, synoptic) over the life cycle of tropical cyclones, requires multiscale, multi-platform observational networks. It has been a great challenge to design, deploy, and maintain such networks without interruption, particularly in the harsh environments imposed by these extreme phenomena. Since the satellite era began, data collected from satellites, along with model reanalysis data, have played an important role in providing continuous global observations, filling in data gaps, and enabling research on weather and climate on different scales.

The concept of using satellites to observe Earth's weather and climate was developed as early as 1946 [1]. NASA launched the first successful, weather satellite, TIROS-1 (Television InfraRed Observational Satellite) on April 1, 1960 [1, 2]. In 1964, the Nimbus project was initiated and a total of 7 experimental meteorological satellites were launched over a 14-year time period (1964–1978) [1, 2]. Since then, weather and climate research have come to rely heavily on long-term, consistent satellite observations from multiple operational space-borne platforms to continuously observe the Earth's atmospheric and surface conditions.

The Earth-observing satellite era began in earnest after NASA transferred the technology to the National Oceanic and Atmospheric Administration (NOAA) in the 1970s. This was followed by several operational weather satellites series, including the Polar-orbiting Operational Environmental Satellites (POES) and the Geostationary Operational Environmental Satellites (GOES), to provide continuous global weather observations. Meanwhile, the Defense Meteorological Satellite Program (DMSP), also launched in the 1970s, provided additional observations of global weather events. These series evolved into the constellation of weather satellites operating today, using research and operational satellites from domestic and international organizations to provide the frequent, global observations necessary for improved understanding and forecasting of Earth's complex weather systems.

Atmospheric 3-D winds, air and sea surface temperatures, pressure, precipitation, water vapor, aerosols, etc. are among the fundamental variables for tropical cyclone research and applications. As aforementioned, few in situ observations are available over vast and remote tropical oceans. Direct measurements of these variables are difficult both from surface and space. Over the years, satellite-based algorithms have been developed and improved to derive these essential variables from few key measurements such as radiances observed onboard satellites, and their datasets are archived and distributed to support tropical cyclone research.

Established in the mid-1980s (**Table 1**), the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA Distributed Active Archive Centers (DAACs), archives and distributes satellite and model data for a range of Earth science disciplines [3], such as precipitation, atmospheric dynamics, atmospheric composition, and hydrology, derived from well-known NASA Earth's satellite missions (e.g., the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM)) as well as model assimilation projects (MERRA-2, NLDAS). These data have been widely used in tropical cyclone research.

To facilitate data access, the GES DISC has developed user-friendly data services for researchers around the world (**Figure 1**). For example, the Geospatial


**107**

**Figure 1.**

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) [4–7] is a powerful online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data or software. Locating datasets suitable for tropical cyclone research in a large data archive is a challenge for many users, especially those who are not familiar with satellite data. For example, a search for "precipitation" in the GES DISC Web site [3] can return over 400 results. To facilitate data access, the GES DISC has recently developed a "data list" (also known as "variable set") concept that groups relevant variables from different datasets together to serve specific research needs. A prototype data list targeting hurricane study has been implemented. The chapter is organized as follows: first, we give a brief overview of NASA satellite and model data at GES DISC, followed by introducing datasets for tropical cyclone studies, data services, case studies, and summary with future directions.

*The GES DISC Web site. This all-in-one design allows search for dataset and information at GES DISC. Users* 

*can access the latest news, projects, missions, tools, resources, and more in this Web site.*

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

• Ocean Biology DAAC—ocean biology focus

<sup>•</sup> Mid-1980s—one of two original DAACs (with NASA's Langley Research Center)—"Goddard DAAC"

### *NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*

Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) [4–7] is a powerful online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data or software. Locating datasets suitable for tropical cyclone research in a large data archive is a challenge for many users, especially those who are not familiar with satellite data. For example, a search for "precipitation" in the GES DISC Web site [3] can return over 400 results. To facilitate data access, the GES DISC has recently developed a "data list" (also known as "variable set") concept that groups relevant variables from different datasets together to serve specific research needs. A prototype data list targeting hurricane study has been implemented.

The chapter is organized as follows: first, we give a brief overview of NASA satellite and model data at GES DISC, followed by introducing datasets for tropical cyclone studies, data services, case studies, and summary with future directions.

#### **Figure 1.**

*The GES DISC Web site. This all-in-one design allows search for dataset and information at GES DISC. Users can access the latest news, projects, missions, tools, resources, and more in this Web site.*

*Current Topics in Tropical Cyclone Research*

TIROS-1 (Television InfraRed Observational Satellite) on April 1, 1960 [1, 2]. In 1964, the Nimbus project was initiated and a total of 7 experimental meteorological satellites were launched over a 14-year time period (1964–1978) [1, 2]. Since then, weather and climate research have come to rely heavily on long-term, consistent satellite observations from multiple operational space-borne platforms to continu-

The Earth-observing satellite era began in earnest after NASA transferred the technology to the National Oceanic and Atmospheric Administration (NOAA) in the 1970s. This was followed by several operational weather satellites series, including the Polar-orbiting Operational Environmental Satellites (POES) and the Geostationary Operational Environmental Satellites (GOES), to provide continuous global weather observations. Meanwhile, the Defense Meteorological Satellite Program (DMSP), also launched in the 1970s, provided additional observations of global weather events. These series evolved into the constellation of weather satellites operating today, using research and operational satellites from domestic and international organizations to provide the frequent, global observations necessary for improved understanding and forecasting of Earth's complex weather systems. Atmospheric 3-D winds, air and sea surface temperatures, pressure, precipitation, water vapor, aerosols, etc. are among the fundamental variables for tropical cyclone research and applications. As aforementioned, few in situ observations are available over vast and remote tropical oceans. Direct measurements of these variables are difficult both from surface and space. Over the years, satellite-based algorithms have been developed and improved to derive these essential variables from few key measurements such as radiances observed onboard satellites, and their datasets are archived and distributed to support tropical cyclone research. Established in the mid-1980s (**Table 1**), the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA Distributed Active Archive Centers (DAACs), archives and distributes satellite and model data for a range of Earth science disciplines [3], such as precipitation, atmospheric dynamics, atmospheric composition, and hydrology, derived from well-known NASA Earth's satellite missions (e.g., the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM)) as well as model assimilation projects (MERRA-2, NLDAS). These data have been widely used in tropical cyclone research. To facilitate data access, the GES DISC has developed user-friendly data services for researchers around the world (**Figure 1**). For example, the Geospatial

• Mid-1980s—one of two original DAACs (with NASA's Langley Research Center)—"Goddard DAAC"

• AVHRR (Advanced Very High-Resolution Radiometer) pathfinder

• 2000/2002—Terra/Aqua MODIS (Moderate-Resolution Imaging Spectroradiometer)

• Level-1 and Atmospheres Archive Distribution System (LAADS)—MODIS instrument focus

• TOVS (TIROS Operational Vertical Sounder) pathfinder • SeaWIFS (Sea-viewing Wide Field-of-view Sensor) • UARS (Upper Atmosphere Research Satellite)

• 2005/2006—EOSDIS evolution—split Goddard DAAC: • GES DISC—atmosphere/hydrology/climate focus

• Ocean Biology DAAC—ocean biology focus

ously observe the Earth's atmospheric and surface conditions.

**106**

**Table 1.**

• 1990s Version 0 era

• 1997—TRMM (first EOS launch)

*A brief history of the GES DISC.*

## **2. Overview of NASA satellite mission and model data collections at GES DISC**

The GES DISC archives and distributes data from a range of satellite observations, models, ground measurements, and field campaigns in multiple Earth science disciplines including global precipitation, atmospheric dynamics, hydrology, and atmospheric composition with a total volume of 2.3+ Petabytes consisting of 100+ million data files covering 3000+ public and restricted collections. Over 1200 data collections are being curated at GES DISC. **Table 2** lists their satellite missions

**Atmospheric composition missions:** • Nimbus 1–7\* BUV, SBUV, TOMS • Shuttle SBUV\* • UARS\* • Aqua AIRS • Aura HIRDLS\* , OMI, MLS • ACOS\* • SNPP Sounder, OMPS • JPSS-1 Sounder, OMPS • OCO-2 • Copernicus Sentinel 5P • TOVS Pathfinder\* **Water cycle/precipitation missions:** • TRMM\* • GPM • SMERGE **Climate variability/solar missions:** • SORCE • TCTE • TSIS • CAR **Future assigned missions:** • OCO-3 • TROPICS • Copernicus Sentinel 6 • GeoCarb **Model projects:** • MERRA\* /MERRA-2 • NLDAS, GLDAS, FLDAS, NCA-LDAS **Other projects:** • MEaSUREs: Making Earth System Data Records for Use in Research Environments • CMS

#### *\* End-of-mission/project.*

#### **Table 2.**

*Past, current, and future NASA satellite missions that are associated with their data products curated at GES DISC.*

**109**

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

• Includes recommended dataset citation, hosting of dataset landing pages, documentation

• Publication of data **distribution metrics** to the EOSDIS Metrics System (EMS)

*Web-based discovery and access to products (value-added services on data):*

• Collaboration with science team subject matter experts (third tier)

• Workshops and webinars on the use of data and relevant services

• Generation of metadata records, publication to the EOSDIS Common Metadata Repository (CMR)

including the past, current, and future satellite missions. **Table 3** lists basic data services and user support. More details about data services are described in Section 4. The GES DISC is a certified trusted repository of Earth science data. Increasingly, funding organizations and publishers require data to be published to certified data repositories adhering to FAIR principles—(Findability, Accessibility, Interoperability, and Reusability). The GES DISC is a regular member of the International Council for Science (ICSU) World Data System (WDS). Established to archive and distribute data from the 1957–1958 International Geophysical Year, WDS spans a range of scientific disciplines data at 52 centers in 12 countries who adhere to the established principles. The GES DISC is also registered as a scientific data repository through re3data.org and meets the repository criteria including DOI assignments, dataset landing pages, dataset documentation, redundant archive (backups), data integrity checks, and user services. This registry is used by high-

• Applied Remote Sensing Training Group (ARSET), Disasters Working Group, Heath and Air Quality Applied Sciences Team (HAQAST), Land and Atmospheres near-real-time Capabilities for EOS

There are many research areas associated with tropical cyclones such as cyclone genesis, intensification, track forecasting, rainfall amounts, etc. While GES DISC archives many variables required to conduct such research, other variables may be located at other NASA DAACs (such as sea surface state and temperature). It can be challenging enough to find relevant variables for a specific research area from a large collection of satellite observations from a single data archive—locating relevant datasets from multiple data centers is even more challenging. Due to the

impact journals such as Nature Scientific Data [8].

**3. Datasets for tropical cyclone research**

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

**Data services and support at GES DISC** *Metadata support, documentation, metrics*:

• Subsetting, reformatting and regridding • Access protocols (e.g., OPeNDAP)

• GES DISC User Services (first tier)

*Community Engagement*:

(LANCE).

**Table 3.**

*User services—provide tiered support in data access and use*:

• Conference participation, publications, news releases

• GES DISC science data specialist (second tier)

• Engagement with Applications Community

*A list of data services and support at GES DISC.*

• Assignment of DOIs

• Giovanni

#### **Data services and support at GES DISC**

*Metadata support, documentation, metrics*:


*Web-based discovery and access to products (value-added services on data):*

• Giovanni

*Current Topics in Tropical Cyclone Research*

**Atmospheric composition missions:**

BUV, SBUV, TOMS

, OMI, MLS

**at GES DISC**

• Nimbus 1–7\*

• ACOS\*

• OCO-2

• TRMM\* • GPM • SMERGE

• SORCE • TCTE • TSIS • CAR

• OCO-3 • TROPICS

• GeoCarb **Model projects:** • MERRA\*

**Other projects:**

*End-of-mission/project.*

• CMS

• Shuttle SBUV\* • UARS\* • Aqua AIRS • Aura HIRDLS\*

• SNPP Sounder, OMPS • JPSS-1 Sounder, OMPS

• Copernicus Sentinel 5P • TOVS Pathfinder\*

**Water cycle/precipitation missions:**

**Climate variability/solar missions:**

**Future assigned missions:**

• Copernicus Sentinel 6

/MERRA-2 • NLDAS, GLDAS, FLDAS, NCA-LDAS

• MEaSUREs: Making Earth System Data Records for Use in Research Environments

*Past, current, and future NASA satellite missions that are associated with their data products curated at GES* 

**2. Overview of NASA satellite mission and model data collections** 

The GES DISC archives and distributes data from a range of satellite observations, models, ground measurements, and field campaigns in multiple Earth science disciplines including global precipitation, atmospheric dynamics, hydrology, and atmospheric composition with a total volume of 2.3+ Petabytes consisting of 100+ million data files covering 3000+ public and restricted collections. Over 1200 data collections are being curated at GES DISC. **Table 2** lists their satellite missions

**108**

*\**

**Table 2.**

*DISC.*


*User services—provide tiered support in data access and use*:


#### **Table 3.**

*A list of data services and support at GES DISC.*

including the past, current, and future satellite missions. **Table 3** lists basic data services and user support. More details about data services are described in Section 4.

The GES DISC is a certified trusted repository of Earth science data. Increasingly, funding organizations and publishers require data to be published to certified data repositories adhering to FAIR principles—(Findability, Accessibility, Interoperability, and Reusability). The GES DISC is a regular member of the International Council for Science (ICSU) World Data System (WDS). Established to archive and distribute data from the 1957–1958 International Geophysical Year, WDS spans a range of scientific disciplines data at 52 centers in 12 countries who adhere to the established principles. The GES DISC is also registered as a scientific data repository through re3data.org and meets the repository criteria including DOI assignments, dataset landing pages, dataset documentation, redundant archive (backups), data integrity checks, and user services. This registry is used by highimpact journals such as Nature Scientific Data [8].

#### **3. Datasets for tropical cyclone research**

There are many research areas associated with tropical cyclones such as cyclone genesis, intensification, track forecasting, rainfall amounts, etc. While GES DISC archives many variables required to conduct such research, other variables may be located at other NASA DAACs (such as sea surface state and temperature). It can be challenging enough to find relevant variables for a specific research area from a large collection of satellite observations from a single data archive—locating relevant datasets from multiple data centers is even more challenging. Due to the

page limit, in this section, we can only present a brief overview of several key data collections at GES DISC for tropical cyclone research.

## **3.1 Brightness temperature collection**

Brightness temperature, derived from radiance measured from satellite instruments, is a fundamental physical variable in satellite meteorology. Infrared satellite images have been available since the Nimbus era to support weather analysis and forecast. For example, infrared images are used in tropical cyclone monitoring and forecast operations at the NOAA National Hurricane Center and the U.S. Navy Joint Typhoon Warning Center. Animations made from infrared images are frequently used in daily local TV weather news, online weather news, and scientific presentations. The GES DISC archives brightness temperatures from infrared instruments from the Nimbus era up to more recent and current passive microwave satellite instruments from domestic and international research and operational satellites.

Datasets from the Nimbus data rescue project [9] consists of digitized black-andwhite film images (**Figure 2**) and radiance data obtained by the Nimbus satellites during the 1960s, 70s, and 80s [10]. Related instruments onboard the Nimbus satellites

**Figure 2.** *A sample of HRIR/Nimbus-1 images of nighttime brightness temperature on 70 mm film.*

**111**

**Table 4.**

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

over this time period are listed in **Table 4**. Negatives of photo facsimile 70-mm file strips were scanned and saved as JPEG 2000 digital files. There are 20 datasets from the Nimbus satellites (1–7) beginning on Aug. 28, 1964 and ending on May 9, 1985. Tropical cyclone research requires frequent and continuous observations of the Earth's atmosphere to analyze and understand event development and processes. Operational geostationary satellites make such observations from cloud tops possible, although it is still challenging to continuously observe changes inside a weather system. The first geostationary satellite in operation is GOES 1, which was launched on October 16, 1975 [1]. As more operational geostationary satellites were added by different international agencies, infrared data from these satellites can be stitched and provide a near-global (60° N-S) coverage of the Earth's atmosphere [11, 12].

With support from the NASA Global Precipitation Climatology Project (GPCP)

Currently in operation, the GPM Microwave Imager (GMI) [15] is used as the reference standard to generate Level-1C common calibrated brightness temperature products from the GPM constellation consisting of both domestic and international satellites, based on the algorithms developed by the GPM intercalibration (X-CAL) working group [16]. These Level-1C products are transformed from their equivalent Level-1B radiance data. There are many applications of these passive microwave brightness products. For example, the GPM profiling algorithm (GPROF) uses these Level-1C products to generate hydrometeor profiles and surface precipitation

Launched in November 1997, the TRMM satellite (40° N-S), a joint mission between NASA and JAXA (the Japan Aerospace Exploration Agency), carried

and by the Tropical Rainfall Measuring Mission (TRMM), the NOAA National Weather Service (NWS) Climate Prediction Center (CPC) has developed a globally merged (60° N-S) pixel-resolution IR brightness temperature dataset (equivalent blackbody temperatures), merged from all available domestic and international geostationary satellites [11, 12]. This half-hourly and 4 km x 4 km resolution dataset is also called the merged IR and is available at the GES DISC from 2000 onward [12]. **Figure 3** is a sample of the dataset, showing two tropical cyclones (Cilida and Kenanga) on December 20, 2018. In addition to tropical cyclone and other research, the merged IR dataset has been an important input for a number of algorithms that derive near-global IR-based precipitation estimates in several well-known satellitebased global precipitation products [13, 14] such as the Integrated Multi-satEllite

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

Retrievals for GPM or IMERG [13].

estimates (**Figure 4**) used as input data in IMERG.

**3.2 TRMM and GPM precipitation dataset collection**

• The High-Resolution Infrared Radiometer (HRIR) (Numbus-1, 2, 3) • The Medium-Resolution Infrared Radiometer (MRIR) (Nimbus-3)

• The Nimbus-4 Selective Chopper Radiometer (SCR) (Nimbus-4, 5) • The Infrared Interferometer Spectrometer (IRIS) (Nimbus-4)

• The Temperature-Humidity Infrared Radiometer (THIR) (Nimbus-4, 5, 6, 7)

• The Electrically Scanning Microwave Radiometer (ESMR) (Nimbus-5) • The High-Resolution Infrared Radiometer (HIRS) (Nimbus-6)

• The Satellite Infrared Spectrometer (SIRS) (Nimbus-3)

• The Satellite Infrared Spectrometer (SIRS) (Nimbus-4)

*Instruments onboard the Nimbus satellites.*

#### *NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*

over this time period are listed in **Table 4**. Negatives of photo facsimile 70-mm file strips were scanned and saved as JPEG 2000 digital files. There are 20 datasets from the Nimbus satellites (1–7) beginning on Aug. 28, 1964 and ending on May 9, 1985.

Tropical cyclone research requires frequent and continuous observations of the Earth's atmosphere to analyze and understand event development and processes. Operational geostationary satellites make such observations from cloud tops possible, although it is still challenging to continuously observe changes inside a weather system. The first geostationary satellite in operation is GOES 1, which was launched on October 16, 1975 [1]. As more operational geostationary satellites were added by different international agencies, infrared data from these satellites can be stitched and provide a near-global (60° N-S) coverage of the Earth's atmosphere [11, 12].

With support from the NASA Global Precipitation Climatology Project (GPCP) and by the Tropical Rainfall Measuring Mission (TRMM), the NOAA National Weather Service (NWS) Climate Prediction Center (CPC) has developed a globally merged (60° N-S) pixel-resolution IR brightness temperature dataset (equivalent blackbody temperatures), merged from all available domestic and international geostationary satellites [11, 12]. This half-hourly and 4 km x 4 km resolution dataset is also called the merged IR and is available at the GES DISC from 2000 onward [12]. **Figure 3** is a sample of the dataset, showing two tropical cyclones (Cilida and Kenanga) on December 20, 2018. In addition to tropical cyclone and other research, the merged IR dataset has been an important input for a number of algorithms that derive near-global IR-based precipitation estimates in several well-known satellitebased global precipitation products [13, 14] such as the Integrated Multi-satEllite Retrievals for GPM or IMERG [13].

Currently in operation, the GPM Microwave Imager (GMI) [15] is used as the reference standard to generate Level-1C common calibrated brightness temperature products from the GPM constellation consisting of both domestic and international satellites, based on the algorithms developed by the GPM intercalibration (X-CAL) working group [16]. These Level-1C products are transformed from their equivalent Level-1B radiance data. There are many applications of these passive microwave brightness products. For example, the GPM profiling algorithm (GPROF) uses these Level-1C products to generate hydrometeor profiles and surface precipitation estimates (**Figure 4**) used as input data in IMERG.

### **3.2 TRMM and GPM precipitation dataset collection**

Launched in November 1997, the TRMM satellite (40° N-S), a joint mission between NASA and JAXA (the Japan Aerospace Exploration Agency), carried


#### **Table 4.** *Instruments onboard the Nimbus satellites.*

*Current Topics in Tropical Cyclone Research*

**3.1 Brightness temperature collection**

collections at GES DISC for tropical cyclone research.

page limit, in this section, we can only present a brief overview of several key data

Brightness temperature, derived from radiance measured from satellite instruments, is a fundamental physical variable in satellite meteorology. Infrared satellite images have been available since the Nimbus era to support weather analysis and forecast. For example, infrared images are used in tropical cyclone monitoring and forecast operations at the NOAA National Hurricane Center and the U.S. Navy Joint Typhoon Warning Center. Animations made from infrared images are frequently used in daily local TV weather news, online weather news, and scientific presentations. The GES DISC archives brightness temperatures from infrared instruments from the Nimbus era up to more recent and current passive microwave satellite instruments from domestic and international research and operational satellites. Datasets from the Nimbus data rescue project [9] consists of digitized black-andwhite film images (**Figure 2**) and radiance data obtained by the Nimbus satellites during the 1960s, 70s, and 80s [10]. Related instruments onboard the Nimbus satellites

**110**

**Figure 2.**

*A sample of HRIR/Nimbus-1 images of nighttime brightness temperature on 70 mm film.*

#### **Figure 3.**

*Two tropical cyclones (Cilida on the left and Kenanga on the right) are seen from the NCEP/CPC merged IR dataset on December 20, 2018. The map was generated with the NASA GISS Panoply.*

#### **Figure 4.**

*GPM GMI surface precipitation from tropical cyclone Kenanga over the Indian Ocean on December 20, 2018. The data were generated by the GES DISC Level-2 subsetter and the map created with NASA GISS Panoply.*

several precipitation-related instruments, including the first spaced-borne Ku-band precipitation radar (PR), a passive TRMM microwave imager (TMI), a visible and infrared scanner (VIRS), and a lightning imaging sensor (LIS) [17]. TRMM ended in April 2015. Over a 17-year period, TRMM provided observations that are used to produce groundbreaking 3-D images of rain and storms over vast and remote tropical oceans and continents. TRMM provides opportunities for researchers to understand characteristics of atmospheric systems through instantaneous measurements in different wavelengths from the onboard instruments.

TRMM data available at GES DISC [18] are listed in **Table 5**. They can be categorized in different processing levels, ranging from Level 1 to 3 [19]. Level-1 TRMM datasets consist of reconstructed and unprocessed instrument data at full-resolution data at Level-1, 1A, 1B, and 1C [19] from the three TRMM

**113**

**Table 5.**

*available in each dataset landing page.*

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

instruments. Level-2 TRMM datasets are derived geophysical variables at the same resolution and location as Level-1 source data such as the GPROF hydrometeor profiles and surface precipitation estimates. Level-3 TRMM datasets are Level-2 variables that are mapped on uniform space-time grid scales, ranging from 3 hourly to monthly. For example, the TRMM Multi-satellite Precipitation Analysis (TMPA) datasets [20–22] are Level-3 products, including both nearreal-time and research grade products. The TMPA datasets have been widely used in research and applications around the world, especially in gauge sparse regions. TRMM products processed with GPM algorithms are also available [3]. Their data format and naming convections are consistent with those of GPM [3]. TRMM LIS data are archived at the Global Hydrology Resource Center (GHRC) [23]. Studies to investigate the relationship between lightning and precipitation have been

Built on the success of TRMM, GPM [15] is another joint mission between NASA and JAXA to continue key measurements after the TRMM era. The main concept of GPM is to form an international constellation of research and operational satellites and use GPM as a core satellite that carries advanced radar and passive microwave radiometer instruments to measure precipitation from space as well as serve as a reference standard to unify precipitation measurements from other domestic and international satellites in the constellation [15].

**Dataset Name Resolution**

5 ×5 km—16 orbits per day

5×5 km—16 orbits per day

• 3A11: 5°, monthly • 3A12: 0.5°, monthly • 3A25: 0.5°, 5°, monthly • 3A26: 5°, monthly • 3A46: 1°, monthly • 3B31: 5°, monthly • 3B42RT: 0.25°, 3 hourly • 3B42RT daily: 0.25°, daily • 3B42: 0.25°, 3 hourly • 3B42 daily: 0.25°, daily • 3B43: 0.25°, monthly

• 1B11: Passive microwave brightness temperature

• 2A21: Precipitation radar surface cross section • 2A23: Precipitation radar rain characteristics • 2A25: Precipitation radar rainfall rate and profile • 2B31: Combined rainfall profile (PR, TMI)

• 3A12: Mean 2A12, profile and surface rainfall

• 3B42RT: 3-hour real-time TRMM Multi-satellite

*A list of TRMM datasets at GES DISC. TRMM products processed with GPM algorithms are also available [3]. Their data format and naming convections are consistent with those of GPM. More information is* 

• 3B42RT daily: 3B42RT derived daily product

• 3A25: Spaceborne radar rainfall • 3A26: Surface rain total • 3A46: SSM/I rain • 3B31: Combined rainfall

Precipitation Analysis (TMPA)

• 3B42: Research version of TMPA • 3B42 daily: 3B42 derived daily product • 3B43: Multi-satellite precipitation

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

reported (e.g., [24]).

**Processing Level**

Level-1 • 1B01: Visible and infrared radiance

Level-2 • 2A12: TMI hydrometeor profile

Level-3 • 3A11: Oceanic rainfall

• 1B21: Precipitation radar power • 1C21: Precipitation radar reflectivity

### *NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*

instruments. Level-2 TRMM datasets are derived geophysical variables at the same resolution and location as Level-1 source data such as the GPROF hydrometeor profiles and surface precipitation estimates. Level-3 TRMM datasets are Level-2 variables that are mapped on uniform space-time grid scales, ranging from 3 hourly to monthly. For example, the TRMM Multi-satellite Precipitation Analysis (TMPA) datasets [20–22] are Level-3 products, including both nearreal-time and research grade products. The TMPA datasets have been widely used in research and applications around the world, especially in gauge sparse regions. TRMM products processed with GPM algorithms are also available [3]. Their data format and naming convections are consistent with those of GPM [3]. TRMM LIS data are archived at the Global Hydrology Resource Center (GHRC) [23]. Studies to investigate the relationship between lightning and precipitation have been reported (e.g., [24]).

Built on the success of TRMM, GPM [15] is another joint mission between NASA and JAXA to continue key measurements after the TRMM era. The main concept of GPM is to form an international constellation of research and operational satellites and use GPM as a core satellite that carries advanced radar and passive microwave radiometer instruments to measure precipitation from space as well as serve as a reference standard to unify precipitation measurements from other domestic and international satellites in the constellation [15].


#### **Table 5.**

*Current Topics in Tropical Cyclone Research*

**112**

**Figure 3.**

**Figure 4.**

several precipitation-related instruments, including the first spaced-borne Ku-band precipitation radar (PR), a passive TRMM microwave imager (TMI), a visible and infrared scanner (VIRS), and a lightning imaging sensor (LIS) [17]. TRMM ended in April 2015. Over a 17-year period, TRMM provided observations that are used to produce groundbreaking 3-D images of rain and storms over vast and remote tropical oceans and continents. TRMM provides opportunities for researchers to understand characteristics of atmospheric systems through instantaneous measure-

*GPM GMI surface precipitation from tropical cyclone Kenanga over the Indian Ocean on December 20, 2018. The data were generated by the GES DISC Level-2 subsetter and the map created with NASA GISS Panoply.*

*Two tropical cyclones (Cilida on the left and Kenanga on the right) are seen from the NCEP/CPC merged IR* 

*dataset on December 20, 2018. The map was generated with the NASA GISS Panoply.*

TRMM data available at GES DISC [18] are listed in **Table 5**. They can be categorized in different processing levels, ranging from Level 1 to 3 [19]. Level-1 TRMM datasets consist of reconstructed and unprocessed instrument data at full-resolution data at Level-1, 1A, 1B, and 1C [19] from the three TRMM

ments in different wavelengths from the onboard instruments.

*A list of TRMM datasets at GES DISC. TRMM products processed with GPM algorithms are also available [3]. Their data format and naming convections are consistent with those of GPM. More information is available in each dataset landing page.*

In addition to the passive GPM microwave imager or GMI, a dual-frequency precipitation radar (DPR) has been added in GPM (**Figure 5a**). A new frequency (Ka-band) in the DPR is capable to detect light rain from space, which is one of challenges in satellite precipitation retrieval algorithms. The GMI carries four additional high frequency channels for measuring falling snow, compared to the TMI. The GMI's spatial resolution is improved significantly with a 1.2 m diameter antenna [15].

#### **Figure 5.**

*(a): Near surface precipitation from the GPM DPR matched scans (MS), showing super typhoon Meranti on September 12, 2016 before impacting the Philippines, Taiwan, and Fujian Province. The data were generated with the Level-2 subsetter and the map with NASA GISS Panoply. (b): Three spatial subsetting options (box, circle, and point) in the Level-2 subsetter.*

**115**

**Table 6.**

August 24–31, 2017.

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

**Dataset Name Resolution**

• 1A-GMI: 8 km x 15 km (varies based on scan position), 16 orbits per day • 1B-GMI: Varies by Channel—16 orbits

• 1C-GMI: Varies by Channel—16 orbits

• 1C-R: Varies by Channel—16 orbits

• 1C-constellation: Varies by satellite

• 2A-GPROF-GMI: 8 km x 15 km (varies based on scan position), 16 orbits

• 2A-GPROF-constellation: Varies by

• 2A-DPR: 5.2 km x 125 m—16 orbits

• 2A-Ka: 5.2 km x 125 m—16 orbits per

• 2A-Ku: 5.2 km x 125 m—16 orbits per

• 2B-CMB: 5 km—16 orbits per day

• 3-GPROF: 0.25°, daily and monthly • 3-GPROF Constellation: 0.25°, daily

• 3-DPR: 0.25°, daily and monthly • 3-CMB: 0.25° and 5°, daily and

• IMERG: 0.1°, 30 minute, daily, and

per day

per day

per day

per day

satellite

per day

day

day

and monthly

monthly

monthly

GPM data products at GES DISC [25] are categorized also in three processing levels (**Table 6**). Like the TRMM era, a new multi-satellite, multi-retrieval product suite (IMERG) has been developed, with significant improvements in both spatial (0.1 x 0.1 deg.) and temporal (half hourly) resolutions over TMPA. There are three dataset categories in IMERG, Early, Late, and Final. The IMERG-Early provides near-real-time (latency: ~4 hours) global precipitation estimates, which are suitable for various research and applications such as flood watching. As more data are available, IMERG-Late (latency: ~12 hours) provides better estimates on precipitation than the Early. The IMERG-Final (latency: ~3.5 months) is a research-grade dataset that is bias corrected with ground gauge data from the Global Precipitation Climatology Centre (GPCC). **Figure 6** is an example of IMERG-Final, showing the accumulated rainfall of Hurricane Harvey during

*A list of GPM datasets at GES DISC. More information is available in each dataset landing page.*

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

the satellite

temperatures

estimates

estimates

estimates

estimates

averages

Final)

rainfall estimates

the GPM constellation • 3-DPR: DPR rainfall averages

Level-3 • 3-GPROF: GMI rainfall averages

Level-1 • 1A-GMI: GMI packet data transmitted by

temperatures collocated

Level-2 • 2A-GPROF-GMI: GMI single-orbit rainfall

• 1B-GMI: GMI brightness temperatures • 1C-GMI: Calibrated GMI brightness

• 1C-R: Common calibrated brightness

• 1C-constellation: Calibrated brightness temperatures for each passive-microwave instrument in the GPM constellation

• 2A-GPROF-constellation: Single-orbit rainfall estimates from each passive-microwave instrument in the GPM constellation • 2A-DPR: DPR Ka&Ku single orbit rainfall

• 2A-Ka: DPR Ka-only single orbit rainfall

• 2A-Ku: DPR Ku-only single orbit rainfall

• 2B-CMB: Combined GMI + DPR single orbit

• 3-GPROF Constellation: Gridded rainfall estimates from each microwave imager in

• 3-CMB: Combined GMI + DPR rainfall

• IMERG: Rainfall estimates combining data from all passive-microwave instruments in the GPM Constellation (Early, Late, and

**Processing Level**

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*


#### **Table 6.**

*Current Topics in Tropical Cyclone Research*

eter antenna [15].

In addition to the passive GPM microwave imager or GMI, a dual-frequency precipitation radar (DPR) has been added in GPM (**Figure 5a**). A new frequency (Ka-band) in the DPR is capable to detect light rain from space, which is one of challenges in satellite precipitation retrieval algorithms. The GMI carries four additional high frequency channels for measuring falling snow, compared to the TMI. The GMI's spatial resolution is improved significantly with a 1.2 m diam-

*(a): Near surface precipitation from the GPM DPR matched scans (MS), showing super typhoon Meranti on September 12, 2016 before impacting the Philippines, Taiwan, and Fujian Province. The data were generated with the Level-2 subsetter and the map with NASA GISS Panoply. (b): Three spatial subsetting options (box,* 

**114**

**Figure 5.**

*circle, and point) in the Level-2 subsetter.*

*A list of GPM datasets at GES DISC. More information is available in each dataset landing page.*

GPM data products at GES DISC [25] are categorized also in three processing levels (**Table 6**). Like the TRMM era, a new multi-satellite, multi-retrieval product suite (IMERG) has been developed, with significant improvements in both spatial (0.1 x 0.1 deg.) and temporal (half hourly) resolutions over TMPA. There are three dataset categories in IMERG, Early, Late, and Final. The IMERG-Early provides near-real-time (latency: ~4 hours) global precipitation estimates, which are suitable for various research and applications such as flood watching. As more data are available, IMERG-Late (latency: ~12 hours) provides better estimates on precipitation than the Early. The IMERG-Final (latency: ~3.5 months) is a research-grade dataset that is bias corrected with ground gauge data from the Global Precipitation Climatology Centre (GPCC). **Figure 6** is an example of IMERG-Final, showing the accumulated rainfall of Hurricane Harvey during August 24–31, 2017.

**Figure 6.**

*Accumulated rainfall during August 24–31, 2017 from Hurricane Harvey. The map was generated with the GPM IMERG—Final daily dataset and Giovanni.*

#### **3.3 MERRA-2 dataset collection**

Datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) are developed by the NASA Global Modeling and Assimilation Office (GMAO) to place NASA's Earth Observing System (EOS) satellite observations in a climate context and to improve the representation of the atmospheric branch in the hydrological cycle from previous reanalysis or MERRA [26]. MERRA-2, available from 1980 onward, also includes the first long-term global aerosol reanalysis through assimilating satellite-based observations and representing their interactions with other physical processes in the Earth's climate system [26]. There are 95 product groups, and the file format is in NetCDF-4. Key meteorological variables in MERRA-2 for tropical cyclone studies such as wind, air temperature, and geopotential height are available at GES DISC. The spatial resolution is about 0.5 deg. x 0.625 deg. in the latitudinal and longitudinal directions, respectively, with 42 pressure levels and 72 model levels. MERRA-2 temporal resolutions range from hourly, 3 hourly, daily to monthly. Initial evaluation of MERRA-2 has been done and is available on the GMAO Web site [27].

## **4. Tools and data services at GES DISC**

#### **4.1 Tools**

In research, data evaluation is often the first step to examine and understand a new physical dataset. Due to the complexity of satellite-based datasets, it is not an easy task to conduct such a task, especially for those without some prior knowledge about the dataset. Over the years, many tools have been developed by different

**117**

resolutions, wavelengths, etc.

around the world.

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

organizations to facilitate such tasks. In this chapter, we describe two popular tools,

Giovanni [4–7, 28] provides a simple and easy way to analyze and visualize more than 2000 satellite- and model-based physical parameters archived at GES DISC without downloading data and software. Variables of many well-known satellite missions and projects mentioned earlier such as TRMM, GPM, MERRA2, etc. are

Giovanni was first developed at the beginning of the TRMM era when TRMM TMPA datasets were available to the public [6]. Precipitation is a very popular variable and is used in many disciplines such as hydrology and agriculture. At that time, standard archived TMPA files were written in HDF4, a format that was not well known outside the remote sensing community. As a result, many TRMM users had difficulties to handle such format, which was a major barrier for TMPA data access and utilization. Recognizing this problem, scientists and software engineers at GES DISC worked closely with the TMPA product provider and developed a Web-based tool, the TRMM Online Visualization and Analysis System (TOVAS) [6]. With a Web browser, one can obtain average and accumulated rainfall maps as well as time series plots and Hovmöller diagrams in their areas of interest. Users can download results in several commonly used formats such as ASCII, which can be directly imported into Microsoft Excel for further processing. Later, several MODIS atmospheric products (e.g., aerosols, atmospheric water vapor) were added to TOVAS. To meet an increasing demand for adding more analytical functions and variables, GES DISC developed Giovanni, allowing functions and variables to be added through a Web-based interface [4, 5]. In current version of Giovanni [7], more advanced information technologies have been implemented in the development, such as having all variables accessible in one Web interface, facet searching, sorting, provenance, etc. As of this writing, over 2000 variables from different Earth science disciplines are available and searchable in Giovanni, including datasets curated through other DAACs. More than 1300 referral research papers have been published by users around the world, with help from Giovanni. In short, Giovanni provides an

easy way to evaluate and explore Earth science data at GES DISC.

With over 2000 variables in Giovanni, it is necessary to provide flexible search capabilities. Frist, users can type in key words such as IMERG and see variables only related to the key words. Often results from a search can contain many variables. For example, a search for precipitation returns 143 variables. To locate those of interest, one can sort the results based on source, spatial and temporal resolutions, begin or end dates. Facets have been developed to help narrow down search results, including disciplines, measurements, platform/instrument, spatial resolutions, temporal

Giovanni provides an interface for selecting date range. Users can either pick a date from the Web interface or type in their own date information. Likewise, users can draw their region of interest or type in the longitude and latitude coordinates in the interface, or they can select predefined shape files from a list including countries, land/sea masks, U.S. states, and major hydrological or watershed basins

There are 22 built-in analytical functions that are grouped into 5 categories based on their analysis types such as maps, time series, and comparisons. Once all the selections are done in the Web interface, a click on the "Plot Data" button will direct the user to the visualization result page where the user can find different options to fine tune their maps or plots. The output page provides a browse history

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

*4.1.1 GES DISC Giovanni*

included in Giovanni.

GES DISC Giovanni and NASA GISS Panoply.

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*

organizations to facilitate such tasks. In this chapter, we describe two popular tools, GES DISC Giovanni and NASA GISS Panoply.

### *4.1.1 GES DISC Giovanni*

*Current Topics in Tropical Cyclone Research*

**3.3 MERRA-2 dataset collection**

*GPM IMERG—Final daily dataset and Giovanni.*

Datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) are developed by the NASA Global Modeling and Assimilation Office (GMAO) to place NASA's Earth Observing System (EOS) satellite observations in a climate context and to improve the representation of the atmospheric branch in the hydrological cycle from previous reanalysis or MERRA [26]. MERRA-2, available from 1980 onward, also includes the first long-term global aerosol reanalysis through assimilating satellite-based observations and representing their interactions with other physical processes in the Earth's climate system [26]. There are 95 product groups, and the file format is in NetCDF-4. Key meteorological variables in MERRA-2 for tropical cyclone studies such as wind, air temperature, and geopotential height are available at GES DISC. The spatial resolution is about 0.5 deg. x 0.625 deg. in the latitudinal and longitudinal directions, respectively, with 42 pressure levels and 72 model levels. MERRA-2 temporal resolutions range from hourly, 3 hourly, daily to monthly. Initial evaluation of

*Accumulated rainfall during August 24–31, 2017 from Hurricane Harvey. The map was generated with the* 

MERRA-2 has been done and is available on the GMAO Web site [27].

In research, data evaluation is often the first step to examine and understand a new physical dataset. Due to the complexity of satellite-based datasets, it is not an easy task to conduct such a task, especially for those without some prior knowledge about the dataset. Over the years, many tools have been developed by different

**4. Tools and data services at GES DISC**

**116**

**4.1 Tools**

**Figure 6.**

Giovanni [4–7, 28] provides a simple and easy way to analyze and visualize more than 2000 satellite- and model-based physical parameters archived at GES DISC without downloading data and software. Variables of many well-known satellite missions and projects mentioned earlier such as TRMM, GPM, MERRA2, etc. are included in Giovanni.

Giovanni was first developed at the beginning of the TRMM era when TRMM TMPA datasets were available to the public [6]. Precipitation is a very popular variable and is used in many disciplines such as hydrology and agriculture. At that time, standard archived TMPA files were written in HDF4, a format that was not well known outside the remote sensing community. As a result, many TRMM users had difficulties to handle such format, which was a major barrier for TMPA data access and utilization. Recognizing this problem, scientists and software engineers at GES DISC worked closely with the TMPA product provider and developed a Web-based tool, the TRMM Online Visualization and Analysis System (TOVAS) [6]. With a Web browser, one can obtain average and accumulated rainfall maps as well as time series plots and Hovmöller diagrams in their areas of interest. Users can download results in several commonly used formats such as ASCII, which can be directly imported into Microsoft Excel for further processing. Later, several MODIS atmospheric products (e.g., aerosols, atmospheric water vapor) were added to TOVAS.

To meet an increasing demand for adding more analytical functions and variables, GES DISC developed Giovanni, allowing functions and variables to be added through a Web-based interface [4, 5]. In current version of Giovanni [7], more advanced information technologies have been implemented in the development, such as having all variables accessible in one Web interface, facet searching, sorting, provenance, etc. As of this writing, over 2000 variables from different Earth science disciplines are available and searchable in Giovanni, including datasets curated through other DAACs. More than 1300 referral research papers have been published by users around the world, with help from Giovanni. In short, Giovanni provides an easy way to evaluate and explore Earth science data at GES DISC.

With over 2000 variables in Giovanni, it is necessary to provide flexible search capabilities. Frist, users can type in key words such as IMERG and see variables only related to the key words. Often results from a search can contain many variables. For example, a search for precipitation returns 143 variables. To locate those of interest, one can sort the results based on source, spatial and temporal resolutions, begin or end dates. Facets have been developed to help narrow down search results, including disciplines, measurements, platform/instrument, spatial resolutions, temporal resolutions, wavelengths, etc.

Giovanni provides an interface for selecting date range. Users can either pick a date from the Web interface or type in their own date information. Likewise, users can draw their region of interest or type in the longitude and latitude coordinates in the interface, or they can select predefined shape files from a list including countries, land/sea masks, U.S. states, and major hydrological or watershed basins around the world.

There are 22 built-in analytical functions that are grouped into 5 categories based on their analysis types such as maps, time series, and comparisons. Once all the selections are done in the Web interface, a click on the "Plot Data" button will direct the user to the visualization result page where the user can find different options to fine tune their maps or plots. The output page provides a browse history in which users can return back to the input Web interface, download the results in graphic or NetCDF formats, or visit the lineage to see the provenance information or download data in each process. **Figure 6** is an example of using Giovanni to generate the accumulated rainfall from Hurricane Harvey during August 24–31, 2017.

Giovanni training materials have been developed over the years. The Giovanni user guide [29] is available through the help button along with release notes, browser compatibility, and known issues. Users can also visit YouTube for Giovanni-related How-to videos [30]. The NASA Applied Remote Sensing Training (ARSET) project also provides materials for Giovanni online training [31], used in live webinars that are free of charge for users around the world. If users have questions or suggestions about Giovanni, they can submit them thorough the feedback button in the landing page and a staff member at GES DISC will provide assistance. Acknowledgment policy is also available at the bottom of the output page.

#### *4.1.2 NASA GISS Panoply*

Although users can access over 2000 variables through Giovanni, all these variables are in Level-3. While adding data variables from other levels in Giovanni are being considered, users can use Panoply [32], developed by NASA Goddard GISS, to view Level-2 and Level-3 data. Panoply is another powerful tool for viewing NASA data. Panoply can be installed in several platforms and operating systems (e.g., macOS, Windows), requiring Java 8 or later version installed in their systems. Most datasets archived at GES DISC can be viewed by Panoply. Once Panoply is installed, there are several ways to import data to Panoply. If NetCDF-3 or NetCDF-4 is the default setting for opening files in the system, Panoply will automatically open the file when you download it from GES DISC. Popular Level-2 and Level-3 datasets are often available in OPeNDAP and GrADS Data Server (GDS). Users can directly use their dataset links in Panoply to visualize the variables. Although analytical functions in Panoply are quite limited, it is so far an easy way to visualize datasets that are currently not available in Giovanni (**Figures 3, 4** and **5a**).

#### **4.2 Data services**

Data services are essential for data archive centers. The totally redesigned GES DISC Web site [3] (**Figure 1**) makes datasets, documents, and help information easy to find. Due to a large volume of datasets and information archived at GES DISC, a search capability is necessary to facilitate dataset discovery and exploration. From the search box (**Figure 1**), users can search datasets and information in the following categories: data collections, data documentation, alerts, FAQs, glossary, How-to's, image gallery, news, and tools. The category for data collections is the default since many users come to GES DISC for datasets. Users can also browse data by category, including subject, measurement, source, processing level, project, temporal resolution, and spatial resolution. As of this writing, the GES DISC archives more than ~2.3 PB data with over 117 million files. Over 2.4 billion files have been distributed with data volume over 23 PB.

After a user types in a key word in the search box (**Figure 1**), the search results are listed. Faceting and sorting are available for identifying datasets of interest, similar to those in Giovanni. For example, a search for IMERG returns 15 datasets and users can use Version to sort different versions and find out the datasets of the latest version. "Get Data" or "Subset/Get Data" right below a dataset name provides a direct link to the data download interface. A click on a dataset name leads to the dataset landing page with more information on dataset summary, data citation, documentation, and more data access methods including links to online archive,

**119**

validation or evaluation.

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

Giovanni, Web services such as OPeNDAP, GDS, and THREDDS (the Thematic Real-time Environmental Distributed Data Services) available for some popular

handled by many software packages or tools such as Panoply, ArcGIS.

Most NASA datasets are global coverage. Many study areas are either local or regional; therefore, spatial subsetting is important to reduce download volumes, permitting the user more time to do research. Spatial subsetting is available for a large number of datasets at GES DISC. Users can use a computer mouse to drag an area of interest or type in the geolocation coordinates in the subsetter interface. Some subsetters can do more than spatial subsetting such as parameter subsetting, i.e., selecting wanted variables from a list, which can also help reduce data to be downloaded. For MERRA-2 data, the subsetter can also regrid the original MERRA-2 data into different grid structures with a list of interpolation methods (e.g., bilinear, bicubic). Furthermore, the MERRA-2 subsetter can subset data at pressure levels. NetCDF-4 is available for many datasets at GES DISC, which can be

Level-2 data subsetting is available for popular and high-volume datasets such as the Level-2 GPM dual-frequency product from DPR (2ADPR) that provides general characteristics of precipitation, correction for attenuation, profiles of precipitation water content, rain rate, as well as particle size distributions of rain and snow. The dual-frequency observations from DPR provide better estimates of rainfall and snowfall rates than the single-frequency TRMM PR [33] with additional information for particle size and melting layer height from the Ka band. Variables in 2ADPR are available in all three scan modes of DPR: a) normal scans (NS), b) matched scans (MS) (**Figure 5a**), c) and high sensitivity (HS), and their swath sizes range from 120 km to 245 km. Each file contains over 400 variables with size close to 300 MB. The subsetting service, developed at GES DISC, provides both variable and spatial subsetting capabilities, which help reducing the file size by several orders of magnitude, depending on selections. Three spatial subsetting capabilities (**Figure 5b**) are currently available: a rectangular latitude/longitude box, a circle, and a point. Users can pick one of them in the interactive subsetting Web interface and create an area or point of interest. These spatial and parameter subsetting capabilities facilitate ground validation and evaluation activities. For example, users can pull DPR data over a time period for a location where a rain gauge is located for

For decades, the GES DISC has archived and distributed a large amount of Earth science data, information, and services to diverse communities including the tropical cyclone community. From searching, discovering to assessing such "Big Data," i.e., heterogeneous and immense scientific data (particularly, satellite or model products) in order to timely and properly examine and assess those natural devastating weather events with an imminent goal for better understanding their natures and reducing the resultant disaster risk, it has, nonetheless, become a daunting task

Aiming to substantially assist our users in their online effectively (i.e., quickly and properly) acquiring the data they want and/or need a "one-stop shop" with a minimum effort from our large data collection for their investigating and assessing the targeted disastrous weather such as hurricanes, the GES DISC has recently developed a value-added and knowledge-based data service prototype by preparing/presenting the "List" of relevant data and the pertinent resources accordingly. Such a data service framework, termed as "Datalist" (currently containing "Hurricane Datalist" only), which basically consists of suites of annotated Web addresses (URLs) that point to the proper and relevant data and resources. **Figure 7** (concept based on [34, 35]) shows a basic workflow of how a user can online, via accessing Hurricane Datalist, acquire and assess the respective datasets (down to the

for science researchers and application users (and decision makers).

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

datasets.

Giovanni, Web services such as OPeNDAP, GDS, and THREDDS (the Thematic Real-time Environmental Distributed Data Services) available for some popular datasets.

Most NASA datasets are global coverage. Many study areas are either local or regional; therefore, spatial subsetting is important to reduce download volumes, permitting the user more time to do research. Spatial subsetting is available for a large number of datasets at GES DISC. Users can use a computer mouse to drag an area of interest or type in the geolocation coordinates in the subsetter interface. Some subsetters can do more than spatial subsetting such as parameter subsetting, i.e., selecting wanted variables from a list, which can also help reduce data to be downloaded. For MERRA-2 data, the subsetter can also regrid the original MERRA-2 data into different grid structures with a list of interpolation methods (e.g., bilinear, bicubic). Furthermore, the MERRA-2 subsetter can subset data at pressure levels. NetCDF-4 is available for many datasets at GES DISC, which can be handled by many software packages or tools such as Panoply, ArcGIS.

Level-2 data subsetting is available for popular and high-volume datasets such as the Level-2 GPM dual-frequency product from DPR (2ADPR) that provides general characteristics of precipitation, correction for attenuation, profiles of precipitation water content, rain rate, as well as particle size distributions of rain and snow. The dual-frequency observations from DPR provide better estimates of rainfall and snowfall rates than the single-frequency TRMM PR [33] with additional information for particle size and melting layer height from the Ka band. Variables in 2ADPR are available in all three scan modes of DPR: a) normal scans (NS), b) matched scans (MS) (**Figure 5a**), c) and high sensitivity (HS), and their swath sizes range from 120 km to 245 km. Each file contains over 400 variables with size close to 300 MB. The subsetting service, developed at GES DISC, provides both variable and spatial subsetting capabilities, which help reducing the file size by several orders of magnitude, depending on selections. Three spatial subsetting capabilities (**Figure 5b**) are currently available: a rectangular latitude/longitude box, a circle, and a point. Users can pick one of them in the interactive subsetting Web interface and create an area or point of interest. These spatial and parameter subsetting capabilities facilitate ground validation and evaluation activities. For example, users can pull DPR data over a time period for a location where a rain gauge is located for validation or evaluation.

For decades, the GES DISC has archived and distributed a large amount of Earth science data, information, and services to diverse communities including the tropical cyclone community. From searching, discovering to assessing such "Big Data," i.e., heterogeneous and immense scientific data (particularly, satellite or model products) in order to timely and properly examine and assess those natural devastating weather events with an imminent goal for better understanding their natures and reducing the resultant disaster risk, it has, nonetheless, become a daunting task for science researchers and application users (and decision makers).

Aiming to substantially assist our users in their online effectively (i.e., quickly and properly) acquiring the data they want and/or need a "one-stop shop" with a minimum effort from our large data collection for their investigating and assessing the targeted disastrous weather such as hurricanes, the GES DISC has recently developed a value-added and knowledge-based data service prototype by preparing/presenting the "List" of relevant data and the pertinent resources accordingly. Such a data service framework, termed as "Datalist" (currently containing "Hurricane Datalist" only), which basically consists of suites of annotated Web addresses (URLs) that point to the proper and relevant data and resources. **Figure 7** (concept based on [34, 35]) shows a basic workflow of how a user can online, via accessing Hurricane Datalist, acquire and assess the respective datasets (down to the

*Current Topics in Tropical Cyclone Research*

*4.1.2 NASA GISS Panoply*

**4.2 Data services**

in which users can return back to the input Web interface, download the results in graphic or NetCDF formats, or visit the lineage to see the provenance information or download data in each process. **Figure 6** is an example of using Giovanni to generate the accumulated rainfall from Hurricane Harvey during August 24–31, 2017. Giovanni training materials have been developed over the years. The Giovanni

Giovanni-related How-to videos [30]. The NASA Applied Remote Sensing Training (ARSET) project also provides materials for Giovanni online training [31], used in live webinars that are free of charge for users around the world. If users have questions or suggestions about Giovanni, they can submit them thorough the feedback button in the landing page and a staff member at GES DISC will provide assistance.

Although users can access over 2000 variables through Giovanni, all these variables are in Level-3. While adding data variables from other levels in Giovanni are being considered, users can use Panoply [32], developed by NASA Goddard GISS, to view Level-2 and Level-3 data. Panoply is another powerful tool for viewing NASA data. Panoply can be installed in several platforms and operating systems (e.g., macOS, Windows), requiring Java 8 or later version installed in their systems. Most datasets archived at GES DISC can be viewed by Panoply. Once Panoply is installed, there are several ways to import data to Panoply. If NetCDF-3 or NetCDF-4 is the default setting for opening files in the system, Panoply will automatically open the file when you download it from GES DISC. Popular Level-2 and Level-3 datasets are often available in OPeNDAP and GrADS Data Server (GDS). Users can directly use their dataset links in Panoply to visualize the variables. Although analytical functions in Panoply are quite limited, it is so far an easy way to visualize datasets that

Data services are essential for data archive centers. The totally redesigned GES DISC Web site [3] (**Figure 1**) makes datasets, documents, and help information easy to find. Due to a large volume of datasets and information archived at GES DISC, a search capability is necessary to facilitate dataset discovery and exploration. From the search box (**Figure 1**), users can search datasets and information in the following categories: data collections, data documentation, alerts, FAQs, glossary, How-to's, image gallery, news, and tools. The category for data collections is the default since many users come to GES DISC for datasets. Users can also browse data by category, including subject, measurement, source, processing level, project, temporal resolution, and spatial resolution. As of this writing, the GES DISC archives more than ~2.3 PB data with over 117 million files. Over 2.4 billion files

After a user types in a key word in the search box (**Figure 1**), the search results are listed. Faceting and sorting are available for identifying datasets of interest, similar to those in Giovanni. For example, a search for IMERG returns 15 datasets and users can use Version to sort different versions and find out the datasets of the latest version. "Get Data" or "Subset/Get Data" right below a dataset name provides a direct link to the data download interface. A click on a dataset name leads to the dataset landing page with more information on dataset summary, data citation, documentation, and more data access methods including links to online archive,

user guide [29] is available through the help button along with release notes, browser compatibility, and known issues. Users can also visit YouTube for

Acknowledgment policy is also available at the bottom of the output page.

are currently not available in Giovanni (**Figures 3, 4** and **5a**).

have been distributed with data volume over 23 PB.

**118**

variable level such as wind and air temperature, etc.) or services (such as Subsetting and Giovanni) they want or need relevant to their targeted hurricane event, e.g., Hurricane Sandy (October 22–29, 2012) over the US continent and coast area at one stop. Basically, through visiting the Hurricane Datalist page (**Figure 7**), users can readily choose and apply those handy "Subsetting" options of (1) refining data temporal range; (2) selecting spatial domain; (3) choosing targeted variables; and (4) acquiring and downloading the data they want and/or need. Moreover, a useful "window shopping" service is offered to users, allowing them to utilize Giovanni

#### **Figure 7.**

*Flow chart of discovering and accessing data sets and variables, e.g., hurricane Sandy (October 22–29, 2012) via hurricane Datalist.*

#### **Figure 8.**

*(a) MERRA-2 wind speeds, (b) AIRS air temperature during October 28–29, 2012 involving hurricane Sandy (October 22–29, 2012).*

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**Figure 9.**

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

to plot and view their interested variables pre-"data downloading" that help them make proper decisions. **Figure 8** shows two maps produced with Giovanni for the two sampled data variables, i.e., the MERRA-2 wind speeds (**Figure 8a**) and the

Users must register the NASA Earthdata login system before downloading data from any NASA data center (including GES DISC). A help feature [3] is available to assist users when they have questions about data and services, which can be very helpful for those who are not familiar with NASA satellite datasets and may not know where to begin. Using this feature, GES DISC staff and supporting scientists can guide users regarding questions related to datasets, tools, documentation, and

services. FAQs and How-To recipes are also available and searchable.

**5.1 Case 1: Evaluation of MERRA-2 precipitation during hurricane** 

that propagate into land surface hydrological fields and beyond [38].

Evaluation (e.g., comparison) of a dataset prior to download is important to understand (for example) any biases or systematic differences in datasets, which is quite common for remote sensing and model datasets. Over oceans, few in situ observations are available, especially for precipitation, making it very difficult to assess their biases. MERRA-2 datasets provide over 39 years of continuous analysis ranging from hourly to monthly, as mentioned earlier, and can be used to study events and environmental changes (e.g., trends) in tropical oceans and other

There are two types of precipitation parameters in MERRA-2: a) precipitation from the atmospheric model (variable PRECTOT in the MERRA-2 dataset collection) and b) observation-corrected precipitation (variable PRECTOTCORR) [36, 37]. Observational data are introduced in the latter parameter due to considerable errors

Bosilovich et al. [37] have conducted a general evaluation of MERRA precipitation estimates, including precipitation climatology, interannual variability, diurnal cycle, Madden-Julian Oscillation (MJO) events, global water cycle, and U.S. summertime variability. Although the preliminary evaluation provides a basic understanding of the MERRA-2 precipitation products, evaluation for extreme weather events is still needed to better understand MERRA-2 precipitation behavior and

*Daily precipitation total (in mm) during hurricane Katrina landfall on August 29, 2005 from (a) MERRA-2* 

*modeled precipitation; (b) observation-corrected precipitation; and (c) TMPA 3B42.*

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

AIRS air temperature (**Figure 8b**).

**5. Case studies**

regions as well.

characteristics.

**Katrina landfall**

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*

to plot and view their interested variables pre-"data downloading" that help them make proper decisions. **Figure 8** shows two maps produced with Giovanni for the two sampled data variables, i.e., the MERRA-2 wind speeds (**Figure 8a**) and the AIRS air temperature (**Figure 8b**).

Users must register the NASA Earthdata login system before downloading data from any NASA data center (including GES DISC). A help feature [3] is available to assist users when they have questions about data and services, which can be very helpful for those who are not familiar with NASA satellite datasets and may not know where to begin. Using this feature, GES DISC staff and supporting scientists can guide users regarding questions related to datasets, tools, documentation, and services. FAQs and How-To recipes are also available and searchable.

#### **5. Case studies**

*Current Topics in Tropical Cyclone Research*

variable level such as wind and air temperature, etc.) or services (such as Subsetting and Giovanni) they want or need relevant to their targeted hurricane event, e.g., Hurricane Sandy (October 22–29, 2012) over the US continent and coast area at one stop. Basically, through visiting the Hurricane Datalist page (**Figure 7**), users can readily choose and apply those handy "Subsetting" options of (1) refining data temporal range; (2) selecting spatial domain; (3) choosing targeted variables; and (4) acquiring and downloading the data they want and/or need. Moreover, a useful "window shopping" service is offered to users, allowing them to utilize Giovanni

*Flow chart of discovering and accessing data sets and variables, e.g., hurricane Sandy (October 22–29, 2012)* 

*(a) MERRA-2 wind speeds, (b) AIRS air temperature during October 28–29, 2012 involving hurricane Sandy* 

**120**

**Figure 8.**

*(October 22–29, 2012).*

**Figure 7.**

*via hurricane Datalist.*

## **5.1 Case 1: Evaluation of MERRA-2 precipitation during hurricane Katrina landfall**

Evaluation (e.g., comparison) of a dataset prior to download is important to understand (for example) any biases or systematic differences in datasets, which is quite common for remote sensing and model datasets. Over oceans, few in situ observations are available, especially for precipitation, making it very difficult to assess their biases. MERRA-2 datasets provide over 39 years of continuous analysis ranging from hourly to monthly, as mentioned earlier, and can be used to study events and environmental changes (e.g., trends) in tropical oceans and other regions as well.

There are two types of precipitation parameters in MERRA-2: a) precipitation from the atmospheric model (variable PRECTOT in the MERRA-2 dataset collection) and b) observation-corrected precipitation (variable PRECTOTCORR) [36, 37]. Observational data are introduced in the latter parameter due to considerable errors that propagate into land surface hydrological fields and beyond [38].

Bosilovich et al. [37] have conducted a general evaluation of MERRA precipitation estimates, including precipitation climatology, interannual variability, diurnal cycle, Madden-Julian Oscillation (MJO) events, global water cycle, and U.S. summertime variability. Although the preliminary evaluation provides a basic understanding of the MERRA-2 precipitation products, evaluation for extreme weather events is still needed to better understand MERRA-2 precipitation behavior and characteristics.

#### **Figure 9.**

*Daily precipitation total (in mm) during hurricane Katrina landfall on August 29, 2005 from (a) MERRA-2 modeled precipitation; (b) observation-corrected precipitation; and (c) TMPA 3B42.*

**Figure 9** shows 3 daily precipitation maps from the MERRA-2 modeled, observation-corrected, and bias-corrected TMPA 3B42 precipitation products on Aug. 29, 2005, when the deadly Hurricane Katrina, as currently ranked as the 3rd most intense landfalling hurricane in the U.S. history, made a landfall near New Orleans, Louisiana. Katrina claimed at least 1245 lives, making it the deadliest U.S. hurricane since the Hurricane Okeechobee in 1928. Apart from the obvious difference in the dataset spatial resolutions (~0.5° in MERRA vs. 0.25° in 3B42), it is seen that large differences exist among three precipitation products (**Figure 9**). **Figure 9a** shows that the modeled precipitation has the largest systematic differences against TMPA 3B42 (**Figure 9c**) in terms of intensity and structure. Significant differences still exist even

#### **Figure 10.**

*Sample images of hurricane Maria at 12Z September 19, 2017 from different datasets and services: (a) true color image from Suomi NPP in NASA Worldview; (b) NOAA/CPC merged IR from the GES DISC archive; (c) MERRA-2 cloud top temperature; (d) MERRA-2 surface wind speed; (e) MERRA-2 total column ozone; and (f) MERRA-2 surface specific humidity.*

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*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

after observational data were applied for bias correction (**Figure 9b** and **c**). More importantly, the centers of heavy rainfall are mismatched in all datasets. In TMPA, there are two centers and all are over the ocean (**Figure 9c**). By contrast, the center from the modeled is located on land (**Figure 9a**). The center from the observationcorrected is between the modeled and TMPA (**Figure 9b**). Also, it seems that both MERRA-2 precipitation products do not do well in the light rain regions in **Figure 9**. The overestimation issue in this case seems to be consistent with the evaluation conducted by Bosilovich et al. [37]; however, other important issues (e.g., the heavy

**5.2 Case 2: Acquiring Earth science data measurements all at once to study** 

Collecting and evaluating data are important activities for tropical cyclone research. A one-stop shop for acquiring these activities can save time and is very desirable. However, such system is still being developed and there are many obstacles to overcome. This case study is to demonstrate, with existing tools, one

With Giovanni, users can explore over 2000 satellite- and model-based variables. For example, Hurricane Maria is a deadly Category-5 hurricane and caused a heavy damage on local economy in island countries in the Caribbean. Before using Giovanni, one can obtain the true color satellite image from NASA Worldview (**Figure 10a**) and the IR image from the merged IR dataset at the GES DISC and Panoply (**Figure 10b**). The latter provides uninterrupted IR data at 4-km spatial resolution available every 30 minutes, which is very helpful for tracking the hurricane evolution. There are many variables in Giovanni from TRMM, GPM, MERRA-2, etc. Once the beginning and ending times as well as the geolocation are decided, one can input such information in Giovanni. The next step is to select variables of interest. In this case, four MERRA-2 variables are selected: (a) cloud top temperature (**Figure 10c**); (b) surface wind speed (**Figure 10d**); (c) total column ozone (**Figure 10e**); and (d) surface specific humidity (**Figure 10f**). In **Figure 10b** and **c**, large differences in cloud top temperatures exist between the merged IR dataset and MERRA-2. For example, the MERRA-2 cloud top temperatures appear to be cooler than those of the merged IR north of the coast of Venezuela. High wind speeds are found near the hurricane center (**Figure 10d**) where low total column ozone (**Figure 10e**) and high surface specific humidity (**Figure 10f**) are located.

In this chapter, we present an overview of basic datasets and services at GES DISC for tropical cyclone research. The collection at GES DISC includes datasets from major NASA satellite missions (e.g., TRMM, GPM) and projects (e.g., MERRA-2, GPCP) with emphasis on precipitation, hydrology, atmospheric composition, atmospheric dynamics, etc. The GES DISC provides user-friendly data services to facilitate data evaluation and download, including a) Giovanni, an online visualization and analysis tool for access over 2000 variables without downloading data and software; b) data subsetting services that allow spatial and variable subsetting of Level-2 and Level-3 datasets; c) different data access methods (online archive, OPeNDAP, GDS, THREDDS) and data formats for a wide variety of users with various technical expertise in handling complex remote sensing datasets; d) information about documentation and data citation; and e) user services to

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

rain centers in wrong places) do exist.

**hurricane events**

can conduct such activities.

**6. Summary and future plans**

answer data or service-related questions.

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*

after observational data were applied for bias correction (**Figure 9b** and **c**). More importantly, the centers of heavy rainfall are mismatched in all datasets. In TMPA, there are two centers and all are over the ocean (**Figure 9c**). By contrast, the center from the modeled is located on land (**Figure 9a**). The center from the observationcorrected is between the modeled and TMPA (**Figure 9b**). Also, it seems that both MERRA-2 precipitation products do not do well in the light rain regions in **Figure 9**. The overestimation issue in this case seems to be consistent with the evaluation conducted by Bosilovich et al. [37]; however, other important issues (e.g., the heavy rain centers in wrong places) do exist.

#### **5.2 Case 2: Acquiring Earth science data measurements all at once to study hurricane events**

Collecting and evaluating data are important activities for tropical cyclone research. A one-stop shop for acquiring these activities can save time and is very desirable. However, such system is still being developed and there are many obstacles to overcome. This case study is to demonstrate, with existing tools, one can conduct such activities.

With Giovanni, users can explore over 2000 satellite- and model-based variables. For example, Hurricane Maria is a deadly Category-5 hurricane and caused a heavy damage on local economy in island countries in the Caribbean. Before using Giovanni, one can obtain the true color satellite image from NASA Worldview (**Figure 10a**) and the IR image from the merged IR dataset at the GES DISC and Panoply (**Figure 10b**). The latter provides uninterrupted IR data at 4-km spatial resolution available every 30 minutes, which is very helpful for tracking the hurricane evolution. There are many variables in Giovanni from TRMM, GPM, MERRA-2, etc. Once the beginning and ending times as well as the geolocation are decided, one can input such information in Giovanni. The next step is to select variables of interest. In this case, four MERRA-2 variables are selected: (a) cloud top temperature (**Figure 10c**); (b) surface wind speed (**Figure 10d**); (c) total column ozone (**Figure 10e**); and (d) surface specific humidity (**Figure 10f**). In **Figure 10b** and **c**, large differences in cloud top temperatures exist between the merged IR dataset and MERRA-2. For example, the MERRA-2 cloud top temperatures appear to be cooler than those of the merged IR north of the coast of Venezuela. High wind speeds are found near the hurricane center (**Figure 10d**) where low total column ozone (**Figure 10e**) and high surface specific humidity (**Figure 10f**) are located.

## **6. Summary and future plans**

In this chapter, we present an overview of basic datasets and services at GES DISC for tropical cyclone research. The collection at GES DISC includes datasets from major NASA satellite missions (e.g., TRMM, GPM) and projects (e.g., MERRA-2, GPCP) with emphasis on precipitation, hydrology, atmospheric composition, atmospheric dynamics, etc. The GES DISC provides user-friendly data services to facilitate data evaluation and download, including a) Giovanni, an online visualization and analysis tool for access over 2000 variables without downloading data and software; b) data subsetting services that allow spatial and variable subsetting of Level-2 and Level-3 datasets; c) different data access methods (online archive, OPeNDAP, GDS, THREDDS) and data formats for a wide variety of users with various technical expertise in handling complex remote sensing datasets; d) information about documentation and data citation; and e) user services to answer data or service-related questions.

*Current Topics in Tropical Cyclone Research*

**Figure 9** shows 3 daily precipitation maps from the MERRA-2 modeled, observation-corrected, and bias-corrected TMPA 3B42 precipitation products on Aug. 29, 2005, when the deadly Hurricane Katrina, as currently ranked as the 3rd most intense landfalling hurricane in the U.S. history, made a landfall near New Orleans, Louisiana. Katrina claimed at least 1245 lives, making it the deadliest U.S. hurricane since the Hurricane Okeechobee in 1928. Apart from the obvious difference in the dataset spatial resolutions (~0.5° in MERRA vs. 0.25° in 3B42), it is seen that large differences exist among three precipitation products (**Figure 9**). **Figure 9a** shows that the modeled precipitation has the largest systematic differences against TMPA 3B42 (**Figure 9c**) in terms of intensity and structure. Significant differences still exist even

*Sample images of hurricane Maria at 12Z September 19, 2017 from different datasets and services: (a) true color image from Suomi NPP in NASA Worldview; (b) NOAA/CPC merged IR from the GES DISC archive; (c) MERRA-2 cloud top temperature; (d) MERRA-2 surface wind speed; (e) MERRA-2 total column ozone;* 

**122**

**Figure 10.**

*and (f) MERRA-2 surface specific humidity.*

We present two case studies to show how our datasets can be used in tropical cyclone research. In the first case, three different precipitation datasets were compared during the landfall of Hurricane Katrina. Results show that large differences exist among the three datasets. The MERRA-2 modeled precipitation has a large wet bias compared to the other two datasets. The areas of heavy precipitation for both modeled and observation-corrected are different from those of 3B42. The results suggest that these differences need to be considered when using the model precipitation products. The second case is an example of exploring different satellite- and model-based variables from existing data services and tools such as Worldview, Panoply, and Giovanni.

Two main areas are focused in future plans: datasets and services. First, as mentioned earlier, NASA Earth data are archived at 12 discipline-oriented data centers across the United States. Datasets at other NASA data centers are also important for tropical cyclone research. For example, the Physical Oceanography DAAC at JPL archives key measurements for tropical cyclone research such as satellite-based ocean surface wind, sea surface temperature, etc. NASA airborne and field campaign datasets for hurricane research, archived at the Global Hydrology Resource Center, play an important role in product validation and case studies. One challenge is to facilitate data discovery and access across the DAACs because relevant datasets are located in different or multiple centers and each center has its unique Web interface for ordering data as well as tools for customized analysis and visualization, which may create problems to some users. It would be more user friendly and efficient to have a one-stop Web interface with data services for acquiring datasets from different data centers. Prototypes have been developed specifically for tropical cyclone research, but they have not been fully integrated into operation and only limited datasets are available; therefore, the datasets can be incomplete. Currently, only NASA Earthdata allows searching datasets from the 12 NASA data centers with very limited data services available. As well, tools can be consolidated and further developed to facilitate access to datasets at different data centers. More on this is to be elaborated in the services.

Analysis-ready data can save time and expedite research and discovery because data are pre-processed at a data center based on research needs. These data are friendly to user's written analysis software or publicly available software packages or tools. For example, time series data subsetting can be a challenging issue for some communities (e.g., hydrology). NASA data are file based, one-time step per file with complex data structures containing few or more variables, which is not optimal for time series data access [39]. To make data analysis ready, data may need to be re-organized for efficient access, such as is demonstrated by the data rods concept [39]. Increasingly, machine learning (ML) and artificial intelligence (AI) algorithms are being used in many areas including tropical cyclone research. Training data play an important role in both ML and AI development and applications. Making training data from collections at DAACs analysis ready (e.g., providing event-based data subsets) can save time for downloading data and processing.

The NASA's Earth Observing System Data and Information System (EOSDIS) cloud evolution [40] is a project to deploy NASA Earth science data and services into a commercial cloud environment to improve data accessibility and serviceability across all NASA DAACs. Prototypes are currently in development to demonstrate cross-DAAC data discovery, access, and servicing. Some datasets are moving natively and operationally into cloud environments as of this writing, such as those from the upcoming Surface Water and Ocean Topography (SWOT). Selected GES DISC datasets are scheduled to be deployed into a cloud environment in 2020,

**125**

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

including the IMERG and MERRA-2 collections. Placing the EOSDIS archive collectively in the cloud will, for the first time, place NASA Earth Observation (EO) data "close to compute" and improve management and accessibility of these data while also expediting science discovery for data users. This will also enable largescale data analytics for data users, especially allow more efficient use data from

As mentioned earlier, NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns are large, complex, and evolving. It can be a daunting task to obtain data from different centers with different interfaces or tools. Data services increasingly play an important role to facilitate data discovery, access, and exploration. Data services at GES DISC are evolving as well. For example, our currently predefined Datalist by no means can contain variables in all possible research topics in hurricane research. User-defined Datalist would allow users to define their own Datalist is the next thing to be developed. The user-defined Datalist not only can save time for dataset search but also can be

Since tropical cyclone research can be categorized based on different spatial and temporal scales. For mesoscale or synoptic scale, event case studies are heavily conducted in tropical cyclone research, requiring that an event information can be easily retrieved from a database (e.g., Hurricane best track data or HURDAT) in order to locate relevant datasets for subsetting, analysis, and visualization with that information (spatial and temporal constrains). Furthermore, datasets can also be searched and located based on other information such as track, intensity, or criteria set by users. At present, neither Giovanni nor the GES DISC Web interface has such capabilities. Adding such event databases can also create data subsets for other research activities such as wild fires, volcanic eruptions, heat waves, snow storms,

Integration, analysis, and visualization for NASA Level-2 and airborne products are challenging because there are a lot of data-related issues such as formats, structures, terminology, etc. Furthermore, integration of Level-2 and Level-3 products is also needed since model and multi-satellite products (MERRA-2, IMERG) are gridded Level-3 products. The first challenge is to be able to locate (then collocate) available datasets (e.g., swath) for an event and subset the data for the area of interest. The latter work has been done for some GPM products at GES DISC, as mentioned earlier. Customized development is necessary to deal with different data structures from different satellite missions as well as airborne and field campaigns, requiring a close collaboration among data centers. At present, there is no Level-2 dataset in Giovanni. Adding Level-2 datasets and airborne data in Giovanni is important to significantly expand the capabilities of Giovanni in tropical cyclone research because observations are very limited over vast tropical oceans, and all these available observations are important for a wide variety of research activities, regardless. Participation and feedback from users or stakeholders always play a key

For climate scale, data services need to provide essential information and datasets including climatological datasets (e.g., sea surface temperature, ocean surface wind speed), anomalies, trends, etc. to help researchers to understand changes and trends in environmental conditions over tropical oceans where tropical cyclones are born and developing. Long-term datasets are important, such as MERRA-2 datasets provide over 39 years of global assimilation analysis (1980–present), which is suitable for generating climatological datasets. Giovanni provides on-the-fly generation of climatology and time series plots for several key datasets, such as MERRA-2,

role to ensure development results to be user friendly and useful.

TMPA, and IMERG, for tropical cyclone research.

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

sharable to other collaborators or scientists.

multiple DAACs.

and nor'easters.

#### *NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*

including the IMERG and MERRA-2 collections. Placing the EOSDIS archive collectively in the cloud will, for the first time, place NASA Earth Observation (EO) data "close to compute" and improve management and accessibility of these data while also expediting science discovery for data users. This will also enable largescale data analytics for data users, especially allow more efficient use data from multiple DAACs.

As mentioned earlier, NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns are large, complex, and evolving. It can be a daunting task to obtain data from different centers with different interfaces or tools. Data services increasingly play an important role to facilitate data discovery, access, and exploration. Data services at GES DISC are evolving as well. For example, our currently predefined Datalist by no means can contain variables in all possible research topics in hurricane research. User-defined Datalist would allow users to define their own Datalist is the next thing to be developed. The user-defined Datalist not only can save time for dataset search but also can be sharable to other collaborators or scientists.

Since tropical cyclone research can be categorized based on different spatial and temporal scales. For mesoscale or synoptic scale, event case studies are heavily conducted in tropical cyclone research, requiring that an event information can be easily retrieved from a database (e.g., Hurricane best track data or HURDAT) in order to locate relevant datasets for subsetting, analysis, and visualization with that information (spatial and temporal constrains). Furthermore, datasets can also be searched and located based on other information such as track, intensity, or criteria set by users. At present, neither Giovanni nor the GES DISC Web interface has such capabilities. Adding such event databases can also create data subsets for other research activities such as wild fires, volcanic eruptions, heat waves, snow storms, and nor'easters.

Integration, analysis, and visualization for NASA Level-2 and airborne products are challenging because there are a lot of data-related issues such as formats, structures, terminology, etc. Furthermore, integration of Level-2 and Level-3 products is also needed since model and multi-satellite products (MERRA-2, IMERG) are gridded Level-3 products. The first challenge is to be able to locate (then collocate) available datasets (e.g., swath) for an event and subset the data for the area of interest. The latter work has been done for some GPM products at GES DISC, as mentioned earlier. Customized development is necessary to deal with different data structures from different satellite missions as well as airborne and field campaigns, requiring a close collaboration among data centers. At present, there is no Level-2 dataset in Giovanni. Adding Level-2 datasets and airborne data in Giovanni is important to significantly expand the capabilities of Giovanni in tropical cyclone research because observations are very limited over vast tropical oceans, and all these available observations are important for a wide variety of research activities, regardless. Participation and feedback from users or stakeholders always play a key role to ensure development results to be user friendly and useful.

For climate scale, data services need to provide essential information and datasets including climatological datasets (e.g., sea surface temperature, ocean surface wind speed), anomalies, trends, etc. to help researchers to understand changes and trends in environmental conditions over tropical oceans where tropical cyclones are born and developing. Long-term datasets are important, such as MERRA-2 datasets provide over 39 years of global assimilation analysis (1980–present), which is suitable for generating climatological datasets. Giovanni provides on-the-fly generation of climatology and time series plots for several key datasets, such as MERRA-2, TMPA, and IMERG, for tropical cyclone research.

*Current Topics in Tropical Cyclone Research*

Panoply, and Giovanni.

be elaborated in the services.

We present two case studies to show how our datasets can be used in tropical cyclone research. In the first case, three different precipitation datasets were compared during the landfall of Hurricane Katrina. Results show that large differences exist among the three datasets. The MERRA-2 modeled precipitation has a large wet bias compared to the other two datasets. The areas of heavy precipitation for both modeled and observation-corrected are different from those of 3B42. The results suggest that these differences need to be considered when using the model precipitation products. The second case is an example of exploring different satellite- and model-based variables from existing data services and tools such as Worldview,

Two main areas are focused in future plans: datasets and services. First, as mentioned earlier, NASA Earth data are archived at 12 discipline-oriented data centers across the United States. Datasets at other NASA data centers are also important for tropical cyclone research. For example, the Physical Oceanography DAAC at JPL archives key measurements for tropical cyclone research such as satellite-based ocean surface wind, sea surface temperature, etc. NASA airborne and field campaign datasets for hurricane research, archived at the Global Hydrology Resource Center, play an important role in product validation and case studies. One challenge is to facilitate data discovery and access across the DAACs because relevant datasets are located in different or multiple centers and each center has its unique Web interface for ordering data as well as tools for customized analysis and visualization, which may create problems to some users. It would be more user friendly and efficient to have a one-stop Web interface with data services for acquiring datasets from different data centers. Prototypes have been developed specifically for tropical cyclone research, but they have not been fully integrated into operation and only limited datasets are available; therefore, the datasets can be incomplete. Currently, only NASA Earthdata allows searching datasets from the 12 NASA data centers with very limited data services available. As well, tools can be consolidated and further developed to facilitate access to datasets at different data centers. More on this is to

Analysis-ready data can save time and expedite research and discovery because data are pre-processed at a data center based on research needs. These data are friendly to user's written analysis software or publicly available software packages or tools. For example, time series data subsetting can be a challenging issue for some communities (e.g., hydrology). NASA data are file based, one-time step per file with complex data structures containing few or more variables, which is not optimal for time series data access [39]. To make data analysis ready, data may need to be re-organized for efficient access, such as is demonstrated by the data rods concept [39]. Increasingly, machine learning (ML) and artificial intelligence (AI) algorithms are being used in many areas including tropical cyclone research. Training data play an important role in both ML and AI development and applications. Making training data from collections at DAACs analysis ready (e.g., providing event-based data subsets) can save time for downloading data and

The NASA's Earth Observing System Data and Information System (EOSDIS) cloud evolution [40] is a project to deploy NASA Earth science data and services into a commercial cloud environment to improve data accessibility and serviceability across all NASA DAACs. Prototypes are currently in development to demonstrate cross-DAAC data discovery, access, and servicing. Some datasets are moving natively and operationally into cloud environments as of this writing, such as those from the upcoming Surface Water and Ocean Topography (SWOT). Selected GES DISC datasets are scheduled to be deployed into a cloud environment in 2020,

**124**

processing.

## **Acknowledgements**

We thank scientists and engineers at GES DISC for their contributions to data management, distribution, and development of data services. We also thank scientific investigators and many users for their feedback and suggestions that improve our data services. GES DISC is funded by NASA's Science Mission Directorate.

## **Author details**

Zhong Liu1,2\*, David Meyer1 , Chung-Lin Shie1,3 and Angela Li1

1 NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), USA

2 George Mason University, USA

3 University of Maryland Baltimore County, USA

\*Address all correspondence to: zhong.liu@nasa.gov

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**127**

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research*

2019]

from: https://earthdata.nasa.gov/ new-data-from-old-satellites-a-nimbussuccess-story [Accessed: 11 August

[10] NASA NSIDC, The Nimbus Data Rescue Project. 2019, Available from: https://nsidc.org/data/nimbus

[11] Janowiak JE, Joyce RJ, Yarosh Y. A real-time global half-hourly pixelresolution infrared dataset and its applications. Bulletin of the American Meteorological Society.

[12] NASA GES DISC, NOAA/NCEP/ CPC Half Hourly 4km Global (60S - 60N) Merged IR. 2019. Available from: https://disc.gsfc.nasa.gov/datasets/ GPM\_MERGIR\_V1/summary [Accessed:

Braithwaite D, Hsu K, Joyce R, Kidd C, et al., IMERG Algorithm Theoretical Basis Document (ATBD) Version 06. 2019. Available from: https://pmm.nasa. gov/sites/default/files/document\_files/ IMERG\_ATBD\_V06.pdf [Accessed:

Arkin PA, Xie P. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology. 2004;**5**:487-503

[Accessed: 11 August 2019]

2001;**82**(3):205-217

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[15] Hou AY et al. The global

BAMS-D-13-00164.1

precipitation measurement mission. Bulletin of the American Meteorological Society. 2014;**95**:701-722. DOI: 10.1175/

[16] NASA GSFC PPS and X-Cal

Working Group. Algorithm Theoretical Basis Document (ATBD) NASA Global Precipitation Measurement Level 1C

*DOI: http://dx.doi.org/10.5772/intechopen.89720*

[1] Kidder SQ, von der Haar TH. Satellite Meteorology - an Introduction. San Diego, CA, USA: Academic Press; 1995,

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[5] Berrick SW et al. Giovanni: A web service workflow-based data visualization and analysis system. IEEE Transactions on Geoscience and Remote Sensing. 2009;**47**(1):106-113. DOI: 10.1109/TGRS.2008.2003183

[6] Liu Z et al. Online visualization and analysis: A new avenue to use satellite data for weather, climate, and interdisciplinary research and applications. In: Levizzani V et al., editors. Measuring Precipitation from Space: EURAINSAT and the Future, Adv. Global Change Res. Ser. Vol. 28. New York: Springer; 2007. pp. 549-558. DOI: 10.1007/978-1-4020-5835-6

[7] Liu Z, Acker J. Giovanni: The Bridge Between Data and Science, Eos, 98. 2017, https://doi. org/10.1029/2017EO079299. Published

[8] Nature. Scientific Data. 2019. Available from: https://www.nature. com/sdata/ [Accessed: 11 August 2019]

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[2] Datta A. 2019, A Brief History of Weather Satellites. Available from: https://www.geospatialworld.net/blogs/ a-brief-history-of-weather-satellites/

[3] NASA GES DISC. The NASA GES DISC. 2019. Available from: https://disc. gsfc.nasa.gov/ [Accessed: 11 August

[4] Acker JG, Leptoukh G. Online analysis enhances use of NASA earth science data. Eos Transaction AGU. 2007;**88**(2):14-17.

*NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research DOI: http://dx.doi.org/10.5772/intechopen.89720*

## **References**

*Current Topics in Tropical Cyclone Research*

**Acknowledgements**

**126**

**Author details**

(GES DISC), USA

Zhong Liu1,2\*, David Meyer1

2 George Mason University, USA

3 University of Maryland Baltimore County, USA

provided the original work is properly cited.

\*Address all correspondence to: zhong.liu@nasa.gov

, Chung-Lin Shie1,3 and Angela Li1

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

We thank scientists and engineers at GES DISC for their contributions to data management, distribution, and development of data services. We also thank scientific investigators and many users for their feedback and suggestions that improve our data services. GES DISC is funded by NASA's Science Mission Directorate.

1 NASA Goddard Earth Sciences Data and Information Services Center

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[6] Liu Z et al. Online visualization and analysis: A new avenue to use satellite data for weather, climate, and interdisciplinary research and applications. In: Levizzani V et al., editors. Measuring Precipitation from Space: EURAINSAT and the Future, Adv. Global Change Res. Ser. Vol. 28. New York: Springer; 2007. pp. 549-558. DOI: 10.1007/978-1-4020-5835-6

[7] Liu Z, Acker J. Giovanni: The Bridge Between Data and Science, Eos, 98. 2017, https://doi. org/10.1029/2017EO079299. Published on 24 August 2017

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[10] NASA NSIDC, The Nimbus Data Rescue Project. 2019, Available from: https://nsidc.org/data/nimbus [Accessed: 11 August 2019]

[11] Janowiak JE, Joyce RJ, Yarosh Y. A real-time global half-hourly pixelresolution infrared dataset and its applications. Bulletin of the American Meteorological Society. 2001;**82**(3):205-217

[12] NASA GES DISC, NOAA/NCEP/ CPC Half Hourly 4km Global (60S - 60N) Merged IR. 2019. Available from: https://disc.gsfc.nasa.gov/datasets/ GPM\_MERGIR\_V1/summary [Accessed: 11 August 2019]

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Instruments and Applications. Dordrecht. ISBN: 978-1-4020-9078-3: Springer; 2009. DOI: 10.1007/978-1-4020-9079-0\_20

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[26] Gelaro R et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate. 2017;**30**:5419-5454.

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[22] Huffman GJ, Bolvin DT. TRMM and Other Data Precipitation Data Set Documentation. 2013. Available from: ftp://meso-a.gsfc.nasa.gov/ pub/trmmdocs/3B42\_3B43\_doc.pdf

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**131**

**Chapter 7**

**Abstract**

by other studies.

cyclones, CMIP6

**1. Introduction**

decades to centuries.

Climate Models Accumulated

Looking at the connection between tropical cyclones and climate changes due to anthropogenic and natural effects, this work aims for information on understanding and how physical aspects of tropical cyclones may change, with a focus on accumulated cyclone energy (ACE), in a global warming scenario. In the present climate evaluation, reasonable results were obtained for the ACE index; the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with lower horizontal and vertical resolution showed more difficulties in representing the index, while Max Planck Institute model demonstrated ability to simulate the climate with more accurate, presenting values of both ACE and maximum temperature close to NCEP Reanalysis 2. The MPI-ESM1-2-HR projections suggest that the seasons and their interannual variations in cyclonic activity will be affected by the forcing on the climate system, in this case, under the scenario of high GHG emissions and high challenges to mitigation SSP585. The results indicate to a future with more chances of facing more tropical cyclone activity, plus the mean increase of 3.1°C in maximum daily temperatures, and more heavy cyclones and stronger storms with more frequency over the North Atlantic Ocean may be experimented, as indicated

**Keywords:** climate change, accumulated cyclone energy, SSP585 scenario, tropical

The challenge of connecting climate change to tropical cyclones (TCs) lies in determining that a change has occurred given natural variability whether by significant changes in climate forcing such as greenhouse gases (GHGs) or aerosols

Tropical cyclone activity has complex characteristics that make it difficult to achieve robust future projections. The onset, duration, intensity, and phenomenology associated with these storms carry many uncertainties in numerical modeling, due to limitations of models to represent local/micro-scale physical processes and tangents to the computational aspect in simulating the climate of long periods, from

Changes in natural variability, volcanic emissions, and solar activity have made a small contribution to the changes in climate over the last century [1, 2]. The natural cycles observed in climate records do not explain the increases in the heat content of

or by the sum of both natural and anthropogenic factors.

the atmosphere, ocean, or cryosphere since the industrial age [3–6].

Cyclone Energy Analysis

*Sullyandro Oliveira Guimarães*

## **Chapter 7**

## Climate Models Accumulated Cyclone Energy Analysis

*Sullyandro Oliveira Guimarães*

## **Abstract**

Looking at the connection between tropical cyclones and climate changes due to anthropogenic and natural effects, this work aims for information on understanding and how physical aspects of tropical cyclones may change, with a focus on accumulated cyclone energy (ACE), in a global warming scenario. In the present climate evaluation, reasonable results were obtained for the ACE index; the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with lower horizontal and vertical resolution showed more difficulties in representing the index, while Max Planck Institute model demonstrated ability to simulate the climate with more accurate, presenting values of both ACE and maximum temperature close to NCEP Reanalysis 2. The MPI-ESM1-2-HR projections suggest that the seasons and their interannual variations in cyclonic activity will be affected by the forcing on the climate system, in this case, under the scenario of high GHG emissions and high challenges to mitigation SSP585. The results indicate to a future with more chances of facing more tropical cyclone activity, plus the mean increase of 3.1°C in maximum daily temperatures, and more heavy cyclones and stronger storms with more frequency over the North Atlantic Ocean may be experimented, as indicated by other studies.

**Keywords:** climate change, accumulated cyclone energy, SSP585 scenario, tropical cyclones, CMIP6

## **1. Introduction**

The challenge of connecting climate change to tropical cyclones (TCs) lies in determining that a change has occurred given natural variability whether by significant changes in climate forcing such as greenhouse gases (GHGs) or aerosols or by the sum of both natural and anthropogenic factors.

Tropical cyclone activity has complex characteristics that make it difficult to achieve robust future projections. The onset, duration, intensity, and phenomenology associated with these storms carry many uncertainties in numerical modeling, due to limitations of models to represent local/micro-scale physical processes and tangents to the computational aspect in simulating the climate of long periods, from decades to centuries.

Changes in natural variability, volcanic emissions, and solar activity have made a small contribution to the changes in climate over the last century [1, 2]. The natural cycles observed in climate records do not explain the increases in the heat content of the atmosphere, ocean, or cryosphere since the industrial age [3–6].

Earth's climate has been affected by changes in factors that control the amount of energy entering and leaving the atmosphere. These factors, known as radiative forcings, include changes in albedo through land use and cover, greenhouse gases, and aerosols. The increase in the concentration of greenhouse gases by emissions from human activities is the largest of these radiative forcings. By absorbing longwave radiation emitted by Earth and redirecting it equally in all directions, greenhouse gases increase the amount of heat retained in the climate system, warming the planet [2, 7–9].

A comparison of a model's simulation of internal multidecadal climate variability with the observed increase in an Atlantic hurricane rapid intensification metric (1982–2009) finds a highly unusual behavior in the metric result and is consistent with the long-term response sign expected by the model to anthropogenic forcing [10]. In the same direction, the 2018 US National Climate Change Assessment reports that decreases in air pollution and increases in GHGs have contributed to increases in Atlantic hurricane activity since 1970 [11].

There is growing evidence of a significant increase in the TC's proportion that become major hurricanes, although the frequency of TCs has remained roughly constant in recent decades [12–17]. A recent study showed that in the central and eastern tropical Atlantic basin during 1986–2015, the 95th percentile of 24 h intensity changes increased significantly [18]. The intensification rate of intensifying storms, another metric that is not dependent on TC frequency, exhibited significant growth during 1977–2013 in the West Pacific basin [19]. In both studies, the largescale environment became more conducive to TC intensification over time. Areas with increases in potential intensities [20] and the largest increase in sea surface temperatures (SSTs) seem to be located with the largest positive changes in intensification rates.

How future anthropogenic warming can affect TC is an important issue, mainly due to the large social impacts they can cause [21], as discussed in previous reports of the Intergovernmental Panel on Climate Change (IPCC) [22] and World Meteorological Organization (WMO) [23].

The IPCC-AR5 [24] concludes for a 2°C global warming that there is more than 66% likelihood to the TC rainfall rates increase in the future and accompanying increase in atmospheric moisture content. Modeling studies on average indicate increase rainfall rates averaged within about 100 km of the storm by 10–15%. The TC intensities increase on average (1–10%), which would imply an even larger increase of percentage in the destructive potential per storm, assuming no reduction in storm size (responses to anthropogenic warming are uncertain).

The future projection for the global number of Category 4 and 5 storms is likely to increase due to anthropogenic warming over the twenty-first, but there is less confidence since most modeling studies project a decrease (or little change) in the overall frequency of all combined TC [24].

Links between climate and tropical cyclones were analyzed in [25], with a good understanding of the relationship at various time scales, with significant trends observed for cyclone intensity and frequency over the past decades over Atlantic. Most climate models simulate fewer tropical cyclones and stronger storms, with increase in precipitation rates. Further sea level rise is likely to increase storm threats, with studies of combined effects of floods and storms projecting that increases are due to global warming [26].

Given the importance of tropical cyclone study, and how changes induced by human actions in the terrestrial system may affect such phenomena, the aim of this study is to evaluate simulations of global numerical models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) [27], by representing the recent

**133**

**Figure 1.**

*13 N to 25 N.*

*Climate Models Accumulated Cyclone Energy Analysis DOI: http://dx.doi.org/10.5772/intechopen.91268*

**2. Accumulated cyclone energy (ACE) approach**

changes in cyclone events.

some interval of time.

the discussion ahead.

from models, setting a related ACE:

speed in knots, with ACE units being 10−1 *knots*<sup>2</sup>

past, and thus access future projections that may occur and indicate trends of

ACE uses the maximum wind speed over time to quantify hurricane activity by season, defined as the sum of the squares of the maximum wind speeds at 6-h intervals, considering the time while the hurricane is at tropical storm strength or greater [28]. As kinetic energy is proportional to the square of velocity, ACE is a value proportional to the energy of the system, by adding together the energy per

A review by [29] evaluates different hurricane indexes, indicating ACE as a valuable metric for quantifying the overall impact of tropical cyclones on the Earth's

The ACE definition given by [28, 29] was adapted to use the monthly output

*ACE* = 10−1 *Vmax*<sup>2</sup> (1)

where *Vmax* applied to this work was the monthly mean of maximum daily wind

The primary energy source for TC is the heat from the evaporation that comes from the warmed ocean surface; several studies showed the correlation between sea surface temperature and TC [21, 23, 30, 31]. Additionally, the increase in precipitation rates is largely based on the Clausius-Clapeyron ratio, which produces about a 7% increase in water vapor in the atmosphere by 1°C warming [32, 33]. Thus, the maximum near-surface air temperature at 2 m (TASMAX) expresses a direct physical relationship with the TC occurrence, used here to be an auxiliary proxy to help

*Tropical cyclone tracks map (adapted from [34]) with the region delimitation for this study: 75 W to 45 W and* 

.

climate, classifying this index as a duration-based integral of a time series.

*Current Topics in Tropical Cyclone Research*

increases in Atlantic hurricane activity since 1970 [11].

Meteorological Organization (WMO) [23].

overall frequency of all combined TC [24].

increases are due to global warming [26].

ing the planet [2, 7–9].

fication rates.

Earth's climate has been affected by changes in factors that control the amount of energy entering and leaving the atmosphere. These factors, known as radiative forcings, include changes in albedo through land use and cover, greenhouse gases, and aerosols. The increase in the concentration of greenhouse gases by emissions from human activities is the largest of these radiative forcings. By absorbing longwave radiation emitted by Earth and redirecting it equally in all directions, greenhouse gases increase the amount of heat retained in the climate system, warm-

A comparison of a model's simulation of internal multidecadal climate variability with the observed increase in an Atlantic hurricane rapid intensification metric (1982–2009) finds a highly unusual behavior in the metric result and is consistent with the long-term response sign expected by the model to anthropogenic forcing [10]. In the same direction, the 2018 US National Climate Change Assessment reports that decreases in air pollution and increases in GHGs have contributed to

There is growing evidence of a significant increase in the TC's proportion that become major hurricanes, although the frequency of TCs has remained roughly constant in recent decades [12–17]. A recent study showed that in the central and eastern tropical Atlantic basin during 1986–2015, the 95th percentile of 24 h intensity changes increased significantly [18]. The intensification rate of intensifying storms, another metric that is not dependent on TC frequency, exhibited significant growth during 1977–2013 in the West Pacific basin [19]. In both studies, the largescale environment became more conducive to TC intensification over time. Areas with increases in potential intensities [20] and the largest increase in sea surface temperatures (SSTs) seem to be located with the largest positive changes in intensi-

How future anthropogenic warming can affect TC is an important issue, mainly due to the large social impacts they can cause [21], as discussed in previous reports of the Intergovernmental Panel on Climate Change (IPCC) [22] and World

tion in storm size (responses to anthropogenic warming are uncertain).

The IPCC-AR5 [24] concludes for a 2°C global warming that there is more than 66% likelihood to the TC rainfall rates increase in the future and accompanying increase in atmospheric moisture content. Modeling studies on average indicate increase rainfall rates averaged within about 100 km of the storm by 10–15%. The TC intensities increase on average (1–10%), which would imply an even larger increase of percentage in the destructive potential per storm, assuming no reduc-

The future projection for the global number of Category 4 and 5 storms is likely to increase due to anthropogenic warming over the twenty-first, but there is less confidence since most modeling studies project a decrease (or little change) in the

Links between climate and tropical cyclones were analyzed in [25], with a good understanding of the relationship at various time scales, with significant trends observed for cyclone intensity and frequency over the past decades over Atlantic. Most climate models simulate fewer tropical cyclones and stronger storms, with increase in precipitation rates. Further sea level rise is likely to increase storm threats, with studies of combined effects of floods and storms projecting that

Given the importance of tropical cyclone study, and how changes induced by human actions in the terrestrial system may affect such phenomena, the aim of this study is to evaluate simulations of global numerical models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) [27], by representing the recent

**132**

past, and thus access future projections that may occur and indicate trends of changes in cyclone events.

## **2. Accumulated cyclone energy (ACE) approach**

ACE uses the maximum wind speed over time to quantify hurricane activity by season, defined as the sum of the squares of the maximum wind speeds at 6-h intervals, considering the time while the hurricane is at tropical storm strength or greater [28]. As kinetic energy is proportional to the square of velocity, ACE is a value proportional to the energy of the system, by adding together the energy per some interval of time.

A review by [29] evaluates different hurricane indexes, indicating ACE as a valuable metric for quantifying the overall impact of tropical cyclones on the Earth's climate, classifying this index as a duration-based integral of a time series.

The ACE definition given by [28, 29] was adapted to use the monthly output from models, setting a related ACE:

$$\text{ACE} = \text{10}^{-1} \text{V}\_{\text{max}}^{-2} \tag{1}$$

where *Vmax* applied to this work was the monthly mean of maximum daily wind speed in knots, with ACE units being 10−1 *knots*<sup>2</sup> .

The primary energy source for TC is the heat from the evaporation that comes from the warmed ocean surface; several studies showed the correlation between sea surface temperature and TC [21, 23, 30, 31]. Additionally, the increase in precipitation rates is largely based on the Clausius-Clapeyron ratio, which produces about a 7% increase in water vapor in the atmosphere by 1°C warming [32, 33]. Thus, the maximum near-surface air temperature at 2 m (TASMAX) expresses a direct physical relationship with the TC occurrence, used here to be an auxiliary proxy to help the discussion ahead.

#### **Figure 1.**

*Tropical cyclone tracks map (adapted from [34]) with the region delimitation for this study: 75 W to 45 W and 13 N to 25 N.*

Most tropical cyclones are formed in the intertropical convergence zone (ITCZ). Tropical waves are another important source of atmospheric instability, contributing to the development of about 85% of cyclones over the Atlantic Ocean [35, 36]. TC rarely forms or moves around 5° from the equator where the Coriolis effect is more weak, with most of them appearing between 10 and 30° latitude away from the equator [37]. Thus, the delimited area in the central region in **Figure 1** was chosen as representative to develop the objective of this work.

## **3. Climate data overview**

Climate models have been used to understand how the climate has changed in the past and may change in the future. These models simulate the physics, chemistry, and biology of the atmosphere, land, and oceans, now called Earth system models, and require supercomputers to generate their climate projections.

A set of standard experiments was designed for CMIP, allowing results to be comparable across different model simulations, to see where models agree and disagree on past and future scenarios [38].

CMIP6 historical experiment covers the period 1850–2014, forced by datasets that are largely based on observations, used as an important benchmark for assessing performance through evaluation against observations, and are well suited for quantifying and understanding important climate change response characteristics [38, 27]. The characteristics and forcings included in historical were described in [27]:


Shared socioeconomic pathway (SSP) scenarios are part of a framework designed to span a range of futures in terms of the socioeconomic challenges that they imply for mitigating and adapting to climate change. In short they are:


**135**

**Figure 2.**

*shown in [40]).*

*Climate Models Accumulated Cyclone Energy Analysis DOI: http://dx.doi.org/10.5772/intechopen.91268*

scenarios for CMIP6 in terms of radiative forcing.

**3.1 NCEP-DOE AMIP-II reanalysis**

evaluate the recent past simulations.

**3.2 CMIP6 historical and SSP585 simulations**

access the datasets is https://esgf-node.llnl.gov/search/cmip6/.

cal levels [42].

SSP585 results of a complementary effort by SSP narrative and the

sions pathway is high enough to produce a radiative forcing of 8.5 W/m2

detailed approaches, and then having just observation data [41, 42].

Representative Concentration Pathways (RCPs), representing the high end of the range of future pathways. SSP5 was chosen for its forcing pathway because its emis-

of the century, updating RCP8.5 [39, 40]. **Figure 2** summarizes all the current SSP

Climate reanalysis aims to assimilate historical observational data with numerical models to generate consistent time series of multiple climate variables. These are a comprehensive description of the observed climate as it has evolved during recent decades, providing global datasets at sub-daily intervals, turning possible more

NCEP-DOE Reanalysis 2 project performs data assimilation using past data from

The zonal and meridional wind components at 2 m and 6-6 hs data were used to compute monthly maximum wind speed. The ACE index was obtained by applying this monthly maximum wind speed in Eq. 1. Similarly, the monthly maximum temperature (TASMAX) was calculated through the daily maximums obtained with 6-6hs data. These variables provided by NCEP reanalysis were used as a reference to

CMIP6 simulation outputs are available in the Earth System Grid Federation (ESGF), through a distributed data archive developed. The data are hosted on a collection of nodes across the world by modeling centers [43]. The main portal to

*Anthropogenic radiative forcing for the twenty-first-century scenarios in the ScenarioMIP design (from [39],* 

1979 through the present. The data is available at PSD portal (https://www.esrl. noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html) in its original four times daily format and as daily averages. The horizontal resolution is 210 km and 28 verti-

by the end

*Current Topics in Tropical Cyclone Research*

**3. Climate data overview**

were described in [27]:

• Solar forcing

• GHG concentrations

disagree on past and future scenarios [38].

Most tropical cyclones are formed in the intertropical convergence zone (ITCZ). Tropical waves are another important source of atmospheric instability, contributing to the development of about 85% of cyclones over the Atlantic Ocean [35, 36]. TC rarely forms or moves around 5° from the equator where the Coriolis effect is more weak, with most of them appearing between 10 and 30° latitude away from the equator [37]. Thus, the delimited area in the central region in **Figure 1** was

Climate models have been used to understand how the climate has changed in the past and may change in the future. These models simulate the physics, chemistry, and biology of the atmosphere, land, and oceans, now called Earth system models, and require supercomputers to generate their climate projections.

A set of standard experiments was designed for CMIP, allowing results to be comparable across different model simulations, to see where models agree and

CMIP6 historical experiment covers the period 1850–2014, forced by datasets

that are largely based on observations, used as an important benchmark for assessing performance through evaluation against observations, and are well suited for quantifying and understanding important climate change response characteristics [38, 27]. The characteristics and forcings included in historical

chosen as representative to develop the objective of this work.

• Emissions of short-lived species and long-lived GHGs

• AMIP sea surface temperatures and sea ice concentrations (SICs)

radius to provide a more consistent representation of aerosol forcing

Shared socioeconomic pathway (SSP) scenarios are part of a framework designed to span a range of futures in terms of the socioeconomic challenges that they imply for mitigating and adapting to climate change. In short they are:

SSP4 - Low challenges to mitigation and high challenges to adaptation.

SSP5 - High challenges to mitigation and low challenges to adaptation [39, 40].

SSP1 - Low challenges to mitigation and adaptation.

SSP3 - High challenges to mitigation and adaptation.

SSP2 - Intermediate challenges to adaptation and mitigation.

• For simulations with prescribed aerosols, a new approach to prescribe aerosols in terms of optical properties and fractional change in cloud droplet effective

• For models without ozone chemistry, time-varying gridded ozone concentra-

• Global gridded land use forcing datasets

• Stratospheric aerosol dataset (volcanoes)

tions and nitrogen deposition

**134**

SSP585 results of a complementary effort by SSP narrative and the Representative Concentration Pathways (RCPs), representing the high end of the range of future pathways. SSP5 was chosen for its forcing pathway because its emissions pathway is high enough to produce a radiative forcing of 8.5 W/m2 by the end of the century, updating RCP8.5 [39, 40]. **Figure 2** summarizes all the current SSP scenarios for CMIP6 in terms of radiative forcing.

### **3.1 NCEP-DOE AMIP-II reanalysis**

Climate reanalysis aims to assimilate historical observational data with numerical models to generate consistent time series of multiple climate variables. These are a comprehensive description of the observed climate as it has evolved during recent decades, providing global datasets at sub-daily intervals, turning possible more detailed approaches, and then having just observation data [41, 42].

NCEP-DOE Reanalysis 2 project performs data assimilation using past data from 1979 through the present. The data is available at PSD portal (https://www.esrl. noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html) in its original four times daily format and as daily averages. The horizontal resolution is 210 km and 28 vertical levels [42].

The zonal and meridional wind components at 2 m and 6-6 hs data were used to compute monthly maximum wind speed. The ACE index was obtained by applying this monthly maximum wind speed in Eq. 1. Similarly, the monthly maximum temperature (TASMAX) was calculated through the daily maximums obtained with 6-6hs data. These variables provided by NCEP reanalysis were used as a reference to evaluate the recent past simulations.

## **3.2 CMIP6 historical and SSP585 simulations**

CMIP6 simulation outputs are available in the Earth System Grid Federation (ESGF), through a distributed data archive developed. The data are hosted on a collection of nodes across the world by modeling centers [43]. The main portal to access the datasets is https://esgf-node.llnl.gov/search/cmip6/.

#### **Figure 2.**

*Anthropogenic radiative forcing for the twenty-first-century scenarios in the ScenarioMIP design (from [39], shown in [40]).*


#### **Table 1.**

*CMIP6 global models and their physical and numerical characteristics.*

The complexity of the models, experiments, and methodologies makes it hard for modeling centers to complete the entire archive to participate in CMIP6. Thus, at this time the available datasets to use in this work, for both historical and SSP585, are listed in **Table 1**.

#### **4. Analysis**

The models simulate their own climate, with no obligation to get it right exactly when specific events have occurred in relation to observational data. On the other hand, they should be able to represent global or large-scale phenomena such as El Niño, La Niña, ITCZ, and ocean circulation. Thus, the models are expected to represent the average climate of the recent past, as well as to simulate the future in the same direction.

Thus, the regional annual cycle for variables with approximately linear behavior, such as temperature, should be easier to represent. Episodic variables such as precipitation and local wind speed are more difficult to model numerically, given the randomness of events. But it is expected that for long periods, good results will be obtained from the models on average terms, as suggested by the WMO to use at least 30 years for climate studies.

The ACE, because it depends directly on the wind, can be assumed to present results that are less well behaved concerning the reference data than the temperature. This occurs in the results obtained here through the CMIP6 models; the ACE index shows similarities for the monthly climate average through the annual cycle (**Figure 3**), although with a discrepancy between the models higher than the maximum temperature (**Figure 7**). In addition to the nonlinearity involved, the models themselves have their limitations, which may be due to the physical, numerical, or computational approach. Model scaling errors for the ACE index are in the order of −12%. Among the models, MPI-ESM1-2-HR performed better in representing the annual ACE cycle, with a good approximation of the mean monthly values compared to reanalysis, with a correlation of 0.93 and bias error − 1.28%.

**137**

**Figure 3.**

*Climate Models Accumulated Cyclone Energy Analysis DOI: http://dx.doi.org/10.5772/intechopen.91268*

The French model IPSL-CM6A-LR has the highest percentage error among the others for ACE, at 22.70% of the reanalysis (**Figure 3**). On the other hand, this same model obtained a better representation of seasonal variability (0.85 correlation) than the two CNRM models, which presented smaller errors (−11%) but with lower correlations, 0.76 (CNRM-CM6-1) and 0.74 (CNRM-ESM2-1). The critical value of the sample correlation, for 95% significance (n-2 degrees of freedom), is 0.576,

The variation coefficient (VC), defined as the ratio of standard deviation by the mean, represents the relative standard deviation, used here to assess whether the models have significant monthly interannual variability or whether they represent

The months with the highest percentage variation range from December to May (**Figure 4**), where there is a relative skill of the models, VC values not exceeding 5% from NCEP-DOE Reanalysis 2. In the months from June to November, models have more difficulties to simulate the maximum wind speeds, possibly resulting from the higher activity of the ITCZ in the region selected for the study and being also the months with the high temperatures of the year (**Figure 7**). The MPI-ESM1-2-HR model best quantified the interannual ACE variations for the months with the highest CT activity, followed by the IPSL model, erring only in magnitude, hitting the

The polynomial curve fitting creates an approximating function that attempts to capture important patterns in the data while leaving out noise or other fine-scale structures/rapid phenomena. This method can aid in data analysis by being able to extract more information from the data as long as the assumption of smoothing is

The first-degree coefficient represents the linear trend of the data, and, as shown in **Figure 5**, the NCEP reanalysis has a small negative trend in annual ACE over the recent past (1979–2014). With the same trend signal, IPSL-CM6A-LR follows the observation pathway, while the other three models simulate a positive

reasonable and to provide analysis that is both flexible and robust.

with all model results performing significant correlations.

climate more closely than stationarity.

*Annual cycle of the study region for ACE.*

temporal evolution in most months.

*Current Topics in Tropical Cyclone Research*

**Model Run Nominal** 

CNRM-CM6-1 CNRM (Centre National de Recherches

IPSL-CM6A-LR Institut Pierre Simon Laplace, Paris 75,252,

MPI-ESM1-2-HR Max Planck Institute for Meteorology, Hamburg

*CMIP6 global models and their physical and numerical characteristics.*

are listed in **Table 1**.

the same direction.

least 30 years for climate studies.

**4. Analysis**

**Table 1.**

The complexity of the models, experiments, and methodologies makes it hard for modeling centers to complete the entire archive to participate in CMIP6. Thus, at this time the available datasets to use in this work, for both historical and SSP585,

**resolution**

CNRM-ESM2-1 r1i1p1f2 250 km 91 AOGCM/BGC/AER/CHEM IPSL-CM6A-LR r1i1p1f1 250 km 79 AOGCM/BGC MPI-ESM1-2-HR r1i1p1f1 100 km 95 AOGCM

CNRM-CM6-1 r1i1p1f2 250 km 91 AOGCM

Meteorologiques, Toulouse 31,057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31,057, France)

France

20,146, Germany

CNRM-ESM2-1 [45]

**Vertical levels**

**Institution/Center Reference**

**Components**

[44]

[46]

[47]

The models simulate their own climate, with no obligation to get it right exactly when specific events have occurred in relation to observational data. On the other hand, they should be able to represent global or large-scale phenomena such as El Niño, La Niña, ITCZ, and ocean circulation. Thus, the models are expected to represent the average climate of the recent past, as well as to simulate the future in

Thus, the regional annual cycle for variables with approximately linear behavior, such as temperature, should be easier to represent. Episodic variables such as precipitation and local wind speed are more difficult to model numerically, given the randomness of events. But it is expected that for long periods, good results will be obtained from the models on average terms, as suggested by the WMO to use at

The ACE, because it depends directly on the wind, can be assumed to present

results that are less well behaved concerning the reference data than the temperature. This occurs in the results obtained here through the CMIP6 models; the ACE index shows similarities for the monthly climate average through the annual cycle (**Figure 3**), although with a discrepancy between the models higher than the maximum temperature (**Figure 7**). In addition to the nonlinearity involved, the models themselves have their limitations, which may be due to the physical, numerical, or computational approach. Model scaling errors for the ACE index are in the order of −12%. Among the models, MPI-ESM1-2-HR performed better in representing the annual ACE cycle, with a good approximation of the mean monthly values compared to reanalysis, with a correlation of 0.93 and bias

**136**

error − 1.28%.

The French model IPSL-CM6A-LR has the highest percentage error among the others for ACE, at 22.70% of the reanalysis (**Figure 3**). On the other hand, this same model obtained a better representation of seasonal variability (0.85 correlation) than the two CNRM models, which presented smaller errors (−11%) but with lower correlations, 0.76 (CNRM-CM6-1) and 0.74 (CNRM-ESM2-1). The critical value of the sample correlation, for 95% significance (n-2 degrees of freedom), is 0.576, with all model results performing significant correlations.

The variation coefficient (VC), defined as the ratio of standard deviation by the mean, represents the relative standard deviation, used here to assess whether the models have significant monthly interannual variability or whether they represent climate more closely than stationarity.

The months with the highest percentage variation range from December to May (**Figure 4**), where there is a relative skill of the models, VC values not exceeding 5% from NCEP-DOE Reanalysis 2. In the months from June to November, models have more difficulties to simulate the maximum wind speeds, possibly resulting from the higher activity of the ITCZ in the region selected for the study and being also the months with the high temperatures of the year (**Figure 7**). The MPI-ESM1-2-HR model best quantified the interannual ACE variations for the months with the highest CT activity, followed by the IPSL model, erring only in magnitude, hitting the temporal evolution in most months.

The polynomial curve fitting creates an approximating function that attempts to capture important patterns in the data while leaving out noise or other fine-scale structures/rapid phenomena. This method can aid in data analysis by being able to extract more information from the data as long as the assumption of smoothing is reasonable and to provide analysis that is both flexible and robust.

The first-degree coefficient represents the linear trend of the data, and, as shown in **Figure 5**, the NCEP reanalysis has a small negative trend in annual ACE over the recent past (1979–2014). With the same trend signal, IPSL-CM6A-LR follows the observation pathway, while the other three models simulate a positive

**Figure 3.** *Annual cycle of the study region for ACE.*

**Figure 4.** *Percentage variation coefficient for ACE monthly values.*

**Figure 5.** *Polynomial adjustment coefficients for annual ACE of the study region.*

trend. From the second-degree coefficient to further ahead, the adjustments are related to patterns with more oscillatory rates, and in the present analysis, this type of signal has no significance. Thus, it can be assumed that models with coefficient values close to reanalysis, in modulus, should have a similar pattern of variability in different modes. The German model was the most difficult to obtain

**139**

**Figure 6.**

*Annual ACE time series for recent past and future simulation under SSP585.*

*Climate Models Accumulated Cyclone Energy Analysis DOI: http://dx.doi.org/10.5772/intechopen.91268*

distant than the obtained for the reanalysis.

NCEP reanalysis climatology (**Figure 7**).

emissions.

projection.

CM6A-LR and CNRM-CM6-1, with no majority agreement.

the adjustment, probably because it has a higher horizontal resolution, making it possible to discretize more climate phenomena, which has coefficient values more

The projection of annual ACE for the twenty-first century (**Figure 6**) has a similar average behavior among models, without abrupt trend changes, presenting modes of variation not far from the simulated for the recent past. The long-term trend for the period 2065–2100 is an increase in the average annual ACE values for the CNRM-ESM2-1 and MPI-ESM1-2-HR models and a reduction for the IPSL-

The TASMAX annual cycle has a good performance by the models; in terms of seasonality, all models show suitable patterns, with low errors in representing the evolution of the monthly cycle. The bias error is a problematic aspect, the three French models have sub estimate ~2°C, while MPI-ESM1-2-HR fits almost the entire

The annual TASMAX projections for the future (**Figure 8**) are similar to that described in the IPCC Special Report on the impacts of global warming of 1.5°C above preindustrial levels and related global greenhouse gas emission pathways [48], in which there is a high confidence that the estimated anthropogenic global warming is currently increasing at 0.2°C per decade due to past and ongoing

The mid- and long-term ACE future projections for most models analyzed indicate to the increase of the index and just the MPI-ESM1-2-HR follows a different pathway (**Figure 9**). The approaches used in the results shown in **Figures 9** and **10** consist of calculating the future percentage change over the periods and applying this change to the reanalysis recent past value. This way, the projection has no bias error associated to it, bringing the right value expected in the future for the

#### *Climate Models Accumulated Cyclone Energy Analysis DOI: http://dx.doi.org/10.5772/intechopen.91268*

*Current Topics in Tropical Cyclone Research*

*Percentage variation coefficient for ACE monthly values.*

**138**

**Figure 5.**

**Figure 4.**

trend. From the second-degree coefficient to further ahead, the adjustments are related to patterns with more oscillatory rates, and in the present analysis, this type of signal has no significance. Thus, it can be assumed that models with coefficient values close to reanalysis, in modulus, should have a similar pattern of variability in different modes. The German model was the most difficult to obtain

*Polynomial adjustment coefficients for annual ACE of the study region.*

the adjustment, probably because it has a higher horizontal resolution, making it possible to discretize more climate phenomena, which has coefficient values more distant than the obtained for the reanalysis.

The projection of annual ACE for the twenty-first century (**Figure 6**) has a similar average behavior among models, without abrupt trend changes, presenting modes of variation not far from the simulated for the recent past. The long-term trend for the period 2065–2100 is an increase in the average annual ACE values for the CNRM-ESM2-1 and MPI-ESM1-2-HR models and a reduction for the IPSL-CM6A-LR and CNRM-CM6-1, with no majority agreement.

The TASMAX annual cycle has a good performance by the models; in terms of seasonality, all models show suitable patterns, with low errors in representing the evolution of the monthly cycle. The bias error is a problematic aspect, the three French models have sub estimate ~2°C, while MPI-ESM1-2-HR fits almost the entire NCEP reanalysis climatology (**Figure 7**).

The annual TASMAX projections for the future (**Figure 8**) are similar to that described in the IPCC Special Report on the impacts of global warming of 1.5°C above preindustrial levels and related global greenhouse gas emission pathways [48], in which there is a high confidence that the estimated anthropogenic global warming is currently increasing at 0.2°C per decade due to past and ongoing emissions.

The mid- and long-term ACE future projections for most models analyzed indicate to the increase of the index and just the MPI-ESM1-2-HR follows a different pathway (**Figure 9**). The approaches used in the results shown in **Figures 9** and **10** consist of calculating the future percentage change over the periods and applying this change to the reanalysis recent past value. This way, the projection has no bias error associated to it, bringing the right value expected in the future for the projection.

**Figure 6.** *Annual ACE time series for recent past and future simulation under SSP585.*

**Figure 7.** *Annual cycle of mean over the study region for TASMAX.*

**Figure 8.** *Annual TASMAX time series for recent past and future simulation under SSP585.*

The model with better results, MPI-ESM1-2-HR, trends to increase annual ACE under the projection period, but points the opposite to mid- and long-term mean (**Figure 9**). One of the changes in the annual cycle is an increase in the index in months where TC activity is not intense, as in the months of the beginning and end of the year, in which there is also an increase in VC. These factors suggest that the

**141**

**5. Conclusions**

**Figure 9.**

**Figure 10.**

monthly data directly.

*Climate Models Accumulated Cyclone Energy Analysis DOI: http://dx.doi.org/10.5772/intechopen.91268*

seasons and their interannual variations in cyclonic activity will be affected by the forcing on the climate system, in this case, under the scenario of high GHG emis-

*Future TASMAX projection under SSP585 for mid (2020–2055) and long (2065–2100) terms.*

The MODELS-MEAN projection (**Figures 9** and **10**) was computed by the weight mean, considering the annual cycle correlation value as the weight for each model. Thus, MODELS-MEAN performs a more confident projection. The results for that concern to a future with more chances of facing more tropical cyclone activity, plus the huge long-term TASMAX increase of 3.1°C (**Figure 10**); the twenty-first century may experiment more heavy cyclones and stronger storms

The accumulated cyclone energy index adapted for this work has made it simpler to assess the recent past and to obtain projections of CMIP6 models, given the use of

In the present climate evaluation (1979–2014), reasonable results were obtained for the ACE index; the French models of lower horizontal and vertical resolution

with more frequency, as indicated by other studies [21, 23, 25, 26].

*Future ACE projection under SSP585 for mid (2020–2055) and long (2065–2100) terms.*

sions and high challenges to mitigation SSP585.

#### **Figure 9.**

*Current Topics in Tropical Cyclone Research*

*Annual cycle of mean over the study region for TASMAX.*

**140**

**Figure 8.**

**Figure 7.**

The model with better results, MPI-ESM1-2-HR, trends to increase annual ACE under the projection period, but points the opposite to mid- and long-term mean (**Figure 9**). One of the changes in the annual cycle is an increase in the index in months where TC activity is not intense, as in the months of the beginning and end of the year, in which there is also an increase in VC. These factors suggest that the

*Annual TASMAX time series for recent past and future simulation under SSP585.*

*Future ACE projection under SSP585 for mid (2020–2055) and long (2065–2100) terms.*

**Figure 10.**

*Future TASMAX projection under SSP585 for mid (2020–2055) and long (2065–2100) terms.*

seasons and their interannual variations in cyclonic activity will be affected by the forcing on the climate system, in this case, under the scenario of high GHG emissions and high challenges to mitigation SSP585.

The MODELS-MEAN projection (**Figures 9** and **10**) was computed by the weight mean, considering the annual cycle correlation value as the weight for each model. Thus, MODELS-MEAN performs a more confident projection. The results for that concern to a future with more chances of facing more tropical cyclone activity, plus the huge long-term TASMAX increase of 3.1°C (**Figure 10**); the twenty-first century may experiment more heavy cyclones and stronger storms with more frequency, as indicated by other studies [21, 23, 25, 26].

## **5. Conclusions**

The accumulated cyclone energy index adapted for this work has made it simpler to assess the recent past and to obtain projections of CMIP6 models, given the use of monthly data directly.

In the present climate evaluation (1979–2014), reasonable results were obtained for the ACE index; the French models of lower horizontal and vertical resolution

showed more difficulties to represent the index, while the Max Planck Institute model demonstrated ability to simulate the climate with more accuracy than the others, presenting values of both ACE and TASMAX very close to NCEP Reanalysis 2.

TASMAX was already expected to obtain good results numerically; in terms of seasonality all models show suitable patterns, with low errors in representing the evolution of the monthly annual cycle.

The annual ACE projection has a similar average behavior among models in the recent past, without abrupt trend changes, but with no major agreement to increase or reduce trend. The mid- and long-term mean for most models analyzed shows an increase in ACE.

The MPI-ESM1-2-HR projections suggest that the seasons and their interannual variations in cyclonic activity will be affected by the forcing on the climate system, in this case, under the scenario of high GHG emissions and high challenges to mitigation SSP585.

The results indicate to a future with more chances of facing more tropical cyclone activity, plus the mean increase of 3.1°C in maximum daily temperatures, and more heavy cyclones and stronger storms with more frequency may be experimented, as indicated by other studies [21, 23, 25, 26].

The study needs to be expanded, including more models, to increase the range of results and to narrow down potential trends that may occur in ensemble analysis.

## **Author details**

Sullyandro Oliveira Guimarães Federal University of Ceará, Fortaleza, Ceará, Brazil

\*Address all correspondence to: sullyandro@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**143**

ngeo1797

*Climate Models Accumulated Cyclone Energy Analysis DOI: http://dx.doi.org/10.5772/intechopen.91268*

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2011;**38**(18):L18601. DOI: 10.1029/2011GL048794

DOI: 10.7930/J0513WCR

J08S4N35

[8] Wuebbles DJ, Easterling DR, Hayhoe K, Knutson T, Kopp RE, Kossin JP, et al. Our globally changing climate. Climate science special report: Fourth National Climate Assessment, volume I. In: Wuebbles DJ, Fahey DW, Hibbard KA, Dokken DJ, Stewart BC, Maycock TK, editors. U.S. Global Change Research Program. Washington, DC, USA; 2017. pp. 35-72. DOI: 10.7930/

[9] Anderson BT, Knight JR, Ringer MA, Yoon J-H, Cherchi A. Testing for the possible influence of unknown climate forcings upon global temperature increases from 1950 to 2000. Journal of Climate. 2012;**25**(20):7163-7172. DOI:

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Easterling DR, Fahey DW, Doherty S, Kossin J, et al. Our changing climate. Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II. In: Reidmiller DR, Avery CW, Easterling DR, Kunkel KE, Lewis KLM, Maycock TK, Stewart BC, editors. U.S. Global Change Research Program.

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The annual ACE projection has a similar average behavior among models in the recent past, without abrupt trend changes, but with no major agreement to increase or reduce trend. The mid- and long-term mean for most models analyzed shows an

The MPI-ESM1-2-HR projections suggest that the seasons and their interannual variations in cyclonic activity will be affected by the forcing on the climate system, in this case, under the scenario of high GHG emissions and high challenges to

The study needs to be expanded, including more models, to increase the range of results and to narrow down potential trends that may occur in ensemble analysis.

The results indicate to a future with more chances of facing more tropical cyclone activity, plus the mean increase of 3.1°C in maximum daily temperatures, and more heavy cyclones and stronger storms with more frequency may be experi-

**142**

**Author details**

Sullyandro Oliveira Guimarães

Federal University of Ceará, Fortaleza, Ceará, Brazil

provided the original work is properly cited.

\*Address all correspondence to: sullyandro@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

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**146**

## *Edited by Anthony Lupo*

This book highlights some of the most recent research in the climatological behavior of tropical cyclones as well as the dynamics, predictability, and character of these storms as derived using remote sensing techniques. Also included in this book is a review of the interaction between tropical cyclones and coastal ocean dynamics in the Northwest Pacific and an evaluation of the performance of CMIP6 models in replicating the current climate using accumulated cyclone energy. The latter demonstrates how the climate may change in the future. This book can be a useful resource for those studying the character of these storms, especially those with the goal of anticipating their future occurrence in both the short and climatological range and their associated hazards.

Published in London, UK © 2020 IntechOpen © tuaindeed / iStock

Current Topics in Tropical Cyclone Research

Current Topics in Tropical

Cyclone Research

*Edited by Anthony Lupo*