4. Retrieving of wind speed

Retrieving wind speed is an inversion problem of remote sensing, of which the key is to find and extract the sensitivity parameters to wind speed from the basic observables of the remote sensing sensors. In fact, L-band signals are less optimal frequency for the measurement of wind speed compared to scatterometers, however, are not significantly attenuated by rainfall so that they are available to observe wind speed in tropical cyclones. As mentioned in Section 1, at present, three types of retrieving approaches for spaceborne GNSS-R have been proposed. Here, the approaches based on the direct mapping from DDM metrics to wind speed are discussed.

#### 4.1. Retrieving method

An expression for the distribution of DDM in delay-Doppler domain has been given in (15), in which the parameter related to sea wind speed is bi-static scattering coefficient σ<sup>2</sup> <sup>0</sup>. Therefore, it is able to observe wind speed using bi-static scattering coefficient derived from the DDM. As bi-static scattering coefficient is ununiform over the sea surface, the integrated area used in calculating bi-static scattering coefficient should be chosen to optimize the accuracy of calculation. The ideal area in which bi-static scattering coefficient could be assumed as the constant is a small region around the specular point. The corresponding power which usually is nearby the peak DDM is expressed as [30, 39]

$$\left\langle \left| Y(\hat{\boldsymbol{\tau}}, \hat{f}) \right|^2 \right\rangle = A\_R^2 T\_I^2 \sigma\_0^2 \iint \frac{G^2(\mathbf{p}) \Lambda^2(\hat{\boldsymbol{\tau}} - \boldsymbol{\tau}(\mathbf{p})) \cdot \text{sinc}^2(\hat{f} - f(\mathbf{p}))}{4\pi D(\mathbf{p})^2 d(\mathbf{p})^2} d^2 \mathbf{p} \tag{25}$$

The expression computing bi-static scattering coefficient is derived as

$$\sigma\_0^2 = \frac{\left\langle \left| Y(\hat{\boldsymbol{\tau}}, \hat{\boldsymbol{\beta}}) \right|^2 \right\rangle}{\left[ A\_R^2 T\_I^2 \iint \frac{G^2(\mathbf{p} \mid \boldsymbol{\Lambda}^2 \left( \hat{\boldsymbol{\tau}} - \boldsymbol{\tau}(\mathbf{p} \mid \boldsymbol{\tau}) \right) \text{sinc}^2 \left( \hat{\boldsymbol{\zeta}} - \boldsymbol{f}(\mathbf{p} \mid \boldsymbol{\tau}) \right)}{4\pi D(\mathbf{p} \mid \boldsymbol{\tau})^2 d(\mathbf{p} \mid \boldsymbol{\tau})^2} d^2 \mathbf{p} \right]} \tag{26}$$

delay-Doppler interval in expression (28) determines the geometric resolution of spaceborne

<sup>Π</sup> <sup>τ</sup>ð Þ� <sup>r</sup> <sup>τ</sup><sup>L</sup> <sup>=</sup><sup>2</sup> τL

The gain modes of RF front-end in SGR-ReSI have unmonitoring AGC and fixed gain. After June 2015, SGR-ReSI was configured as the fixed gain of 36 dB. The UK-TDS-1 Level 1 data collected from November 26, 2015 to April 19, 2016 are used to demonstrate the feasibility of retrieving wind speed using the approach above. Wind speed monitored by ASCAT-A/B which have a resolution of 25 km are selected as in-situ data. The matchup between the specular pointing of UK-TDS-1 and the ground track of ASCAT-A/B are set to allow a maximum space and time separation of 0.25� and 1 h. To improve the accuracy of retrieved wind speed, it is necessary to select high-quality L1 data to retrieve wind speed. The first is spatial selection that the latitude range of the specular point is limited from �50�S to �50�N to remove the data reflected off the sea ice, and the distances between the specular pointing and the coastline are controlled to be over 0.5� to remove the impact of the land on the DDM. The second one is the selection based on the antenna gain at the specular point. The nadir-looking antenna gains of SGR-ReSI rapidly attenuate when the incident angles of incoming signal are over 40�. Therefore, the incident angles of GNSS signal are limited within the range of 30�. The reflected GNSS signal arriving at the nadir-pointing antenna is exceedingly weak. The coherent integration of 1 ms and incoherent averaging of 1000 significantly improve the signal-tonoise ratio (SNR); however, some DDMs still are buried in the noise floor. In addition, the SNR is one of the factors influencing on the accuracy of wind speed. Therefore, it is needed to

where τ<sup>L</sup> and f <sup>M</sup> are the maximum delay and Doppler frequency in expression (28).

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

� �∙<sup>Π</sup> <sup>f</sup>ð Þ <sup>r</sup>

2f <sup>M</sup> � �d<sup>r</sup>

GNSS Application in Retrieving Sea Wind Speed http://dx.doi.org/10.5772/intechopen.74149

(29)

109

Rgeo ¼

4.2. Demonstration of UK-TDS-1 and ASCAT data

ð

s

remove DDMs with the low SNR. The SNR of DDM could be defined as

averaging the DDM in the region where reflected signal is absent.

SNR <sup>¼</sup> <sup>P</sup>peak Nfloor

where Ppeak is the peak DDM; and Nfloor is the noise floor of DDM, which can be computed by

After the DDMs are selected, the delay and Doppler interval in expression (28) should be determined. The delay and Doppler interval are limited by the geometric resolution. Figure 14 shows the relationship between the geometric resolution and the incident angle for different delay interval when Doppler interval is [�1, 1] KHz, from which it is seen that when delay interval is [�0.25, 0.25] chips, the geometric resolution is lower than 25 km which is required resolution of the traditional spaceborne remote sensing. Based on the discussion, the delay and Doppler interval in expression (28) are configured as [�0.25, 0.25] chips and [�1, 1] KHz.

� 1 (30)

GNSS-R as [40]

In expression (26), AR is an unknown parameter related to the transmit power and antenna gain of GNSS satellite, the atmospheric loss. Generally, direct signal and some atmospheric attenuation models are used to estimate it. In addition, the integral operation in expression (26) is time-consuming; therefore, the expression (26) is further simplified by assuming the changes of Gð Þ r , τð Þ r , and fð Þ r in the small region around the specular pointings as unity. The simplified expression of σ<sup>2</sup> <sup>0</sup> could be given as

$$\hat{\sigma}\_0^2 = \frac{4\pi D^2 d^2 \left\langle \left| Y\left(\hat{\tau}, \hat{f}\right) \right|^2 \right\rangle}{A\_R^2 T\_I^2 G\_{\text{SP}} A\_0} \tag{27}$$

where GSP is the gain of the nadir-looking antenna at the specular point; and A<sup>0</sup> is the size of scattering area around the specular point. To reduce the influence of the noise, <sup>Y</sup> <sup>b</sup>τ;b<sup>f</sup> � � � � � � � � <sup>2</sup> � � in expressions (26) and (27) could be replaced by the averaged DDM around the peak as

$$P\_{\text{avg}} = \frac{1}{\text{MN}} \sum\_{i=1}^{M} \sum\_{j=1}^{N} \left\langle \left| Y(\tau\_i, f\_j) \right|^2 \right\rangle \tag{28}$$

As illustrated in Figure 5, the DDM not only gives the distribution of correlation power in delay-Doppler domain, but also presents the spatial distribution of power. The given delay-Doppler interval in expression (28) determines the geometric resolution of spaceborne GNSS-R as [40]

$$R\_{\rm geo} = \sqrt{\int \Pi \left(\frac{\tau(\mathfrak{p}) - \tau\_L/\_2}{\tau\_L}\right) \cdot \Pi \left(\frac{f(\mathfrak{p})}{2f\_M}\right) d\mathfrak{p}}\tag{29}$$

where τ<sup>L</sup> and f <sup>M</sup> are the maximum delay and Doppler frequency in expression (28).

#### 4.2. Demonstration of UK-TDS-1 and ASCAT data

4.1. Retrieving method

108 Multifunctional Operation and Application of GPS

the peak DDM is expressed as [30, 39]

<sup>Y</sup> <sup>b</sup>τ;b<sup>f</sup> � � � � �

simplified expression of σ<sup>2</sup>

<sup>2</sup> � �

� � �

> σ2 <sup>0</sup> ¼

<sup>¼</sup> <sup>A</sup><sup>2</sup> RT<sup>2</sup> I σ2 0 ðð <sup>G</sup><sup>2</sup>

The expression computing bi-static scattering coefficient is derived as

<sup>0</sup> could be given as

σb2 <sup>0</sup> ¼

<sup>P</sup>avg <sup>¼</sup> <sup>1</sup> MN X M

4πD<sup>2</sup>

A2 RT<sup>2</sup> <sup>I</sup> GSPA<sup>0</sup>

scattering area around the specular point. To reduce the influence of the noise, <sup>Y</sup> <sup>b</sup>τ;b<sup>f</sup>

expressions (26) and (27) could be replaced by the averaged DDM around the peak as

i¼1

where GSP is the gain of the nadir-looking antenna at the specular point; and A<sup>0</sup> is the size of

X N

j¼1

As illustrated in Figure 5, the DDM not only gives the distribution of correlation power in delay-Doppler domain, but also presents the spatial distribution of power. The given

A2 RT<sup>2</sup> I ÐÐ

2 4

An expression for the distribution of DDM in delay-Doppler domain has been given in (15), in

is able to observe wind speed using bi-static scattering coefficient derived from the DDM. As bi-static scattering coefficient is ununiform over the sea surface, the integrated area used in calculating bi-static scattering coefficient should be chosen to optimize the accuracy of calculation. The ideal area in which bi-static scattering coefficient could be assumed as the constant is a small region around the specular point. The corresponding power which usually is nearby

ð Þ <sup>r</sup> <sup>Λ</sup><sup>2</sup>

<sup>Y</sup> <sup>b</sup>τ;b<sup>f</sup> � � � � �

<sup>4</sup>πDð Þ <sup>r</sup> <sup>2</sup>

<sup>d</sup><sup>2</sup> <sup>Y</sup> <sup>b</sup>τ;b<sup>f</sup> � � � � �

<sup>2</sup> � �

� � �

Y τi; f <sup>j</sup> � � � � �

<sup>2</sup> � �

� � �

In expression (26), AR is an unknown parameter related to the transmit power and antenna gain of GNSS satellite, the atmospheric loss. Generally, direct signal and some atmospheric attenuation models are used to estimate it. In addition, the integral operation in expression (26) is time-consuming; therefore, the expression (26) is further simplified by assuming the changes of Gð Þ r , τð Þ r , and fð Þ r in the small region around the specular pointings as unity. The

<sup>G</sup>2ð Þ <sup>r</sup> <sup>Λ</sup><sup>2</sup> <sup>b</sup><sup>τ</sup>�τð Þ <sup>r</sup>

<sup>2</sup> � �

ð Þ� <sup>b</sup><sup>τ</sup> � <sup>τ</sup>ð Þ <sup>r</sup> sinc<sup>2</sup> <sup>b</sup><sup>f</sup> � <sup>f</sup>ð Þ <sup>r</sup>

� �

r

3 5

<sup>d</sup>ð Þ <sup>r</sup> <sup>2</sup> <sup>d</sup><sup>2</sup>

4πDð Þ r 2 dð Þ r

> � � �

� ��sinc<sup>2</sup> <sup>b</sup><sup>f</sup> �fð Þ <sup>r</sup>

� �

<sup>2</sup> <sup>d</sup><sup>2</sup>

<sup>0</sup>. Therefore, it

r (25)

(26)

(27)

in

(28)

� � � � �

<sup>2</sup> � �

� � �

which the parameter related to sea wind speed is bi-static scattering coefficient σ<sup>2</sup>

The gain modes of RF front-end in SGR-ReSI have unmonitoring AGC and fixed gain. After June 2015, SGR-ReSI was configured as the fixed gain of 36 dB. The UK-TDS-1 Level 1 data collected from November 26, 2015 to April 19, 2016 are used to demonstrate the feasibility of retrieving wind speed using the approach above. Wind speed monitored by ASCAT-A/B which have a resolution of 25 km are selected as in-situ data. The matchup between the specular pointing of UK-TDS-1 and the ground track of ASCAT-A/B are set to allow a maximum space and time separation of 0.25� and 1 h. To improve the accuracy of retrieved wind speed, it is necessary to select high-quality L1 data to retrieve wind speed. The first is spatial selection that the latitude range of the specular point is limited from �50�S to �50�N to remove the data reflected off the sea ice, and the distances between the specular pointing and the coastline are controlled to be over 0.5� to remove the impact of the land on the DDM. The second one is the selection based on the antenna gain at the specular point. The nadir-looking antenna gains of SGR-ReSI rapidly attenuate when the incident angles of incoming signal are over 40�. Therefore, the incident angles of GNSS signal are limited within the range of 30�. The reflected GNSS signal arriving at the nadir-pointing antenna is exceedingly weak. The coherent integration of 1 ms and incoherent averaging of 1000 significantly improve the signal-tonoise ratio (SNR); however, some DDMs still are buried in the noise floor. In addition, the SNR is one of the factors influencing on the accuracy of wind speed. Therefore, it is needed to remove DDMs with the low SNR. The SNR of DDM could be defined as

$$\text{SNR} = \frac{P\_{\text{peak}}}{N\_{\text{floor}}} - 1 \tag{30}$$

where Ppeak is the peak DDM; and Nfloor is the noise floor of DDM, which can be computed by averaging the DDM in the region where reflected signal is absent.

After the DDMs are selected, the delay and Doppler interval in expression (28) should be determined. The delay and Doppler interval are limited by the geometric resolution. Figure 14 shows the relationship between the geometric resolution and the incident angle for different delay interval when Doppler interval is [�1, 1] KHz, from which it is seen that when delay interval is [�0.25, 0.25] chips, the geometric resolution is lower than 25 km which is required resolution of the traditional spaceborne remote sensing. Based on the discussion, the delay and Doppler interval in expression (28) are configured as [�0.25, 0.25] chips and [�1, 1] KHz.

Figure 14. Relationship between geometric resolution and incident angle.

The retrieval model is the mapping linking wind speed with the bi-static scattering coefficient computed by expression (26) or (27). Note that because of the unknown transmit power and antenna gain, the influence of A<sup>2</sup> <sup>R</sup> on the bi-static scattering coefficient ignored by assuming them as the constants. Moreover, when the incident angle of GNSS signal is lower than 35�, 1=cos<sup>2</sup>θ could be used to replace A<sup>0</sup> in the expression (27) because of the proportional relationship between them [41]. Therefore, the observable used to develop the link with wind speed is not a true bi-static scattering coefficient. The selected data are divided into two groups: train and test set. The relationship between the observable and wind speed is fitted as

$$\mathcal{U}I\_{10} = A e^{\mathcal{S}v\_{\text{obs}}^2} + \mathcal{C} \tag{31}$$

The SNR of DDM has an important impact on retrieving result of wind speed as shown in Figure 18. The accuracy of retrieved wind speed is improved by selecting the DDMs with higher SNR. However, it should be noted that the larger the threshold of the SNR is, the lesser

A B C Bias (m/s) RMSE (m/s)

GNSS Application in Retrieving Sea Wind Speed http://dx.doi.org/10.5772/intechopen.74149 111

Figure 15. Scatter plot of observable computed by expression (a) (26) and (b) (27) vs. wind speed.

Figure 16. Scatter plot of ASCAT-A/B and retrieved wind speed.

Observable Train set Test set

Expression (26) <sup>3</sup>:<sup>506</sup> 1022 0.237 0.0115 0.024 2.11 Expression (27) <sup>4</sup>:<sup>299</sup> <sup>10</sup><sup>27</sup> 0.2177 0.0665 0.021 2.15

Table 2. Trained parameters of Eq. (31) using train set and corresponding test performance using test set.

where σ<sup>2</sup> obs is the obervable computed by expressions (26) and (27) when <sup>A</sup><sup>2</sup> <sup>R</sup> are ignored and A<sup>0</sup> is replaced using 1=cos2θ; and A, B, and C are fitted parameters. Figure 15 shows the inversely proportional relationship between the observable computed by expressions (26) and (27) and wind speed. The fitted parameters in expression (31) are obtained by fitting the observables of train set with wind speed measured by ASCAT-A/B. The compared results between ASCAT-A/B-retrieved wind speed using test set are presented in Figure 16 and Table 2. The biases between ASCAT-A/B and retrieved wind speed are ignorable, 0.024 m/s and 0.021 m/s, for the observables derived by expressions (26) and (27). The RMSEs are 2.11 m/s and 2.15 m/s, respectively. In addition, it is clear that the retrieving accuracy for high wind speed is larger than the low wind speed as illustrated in Figure 17, in which when wind speed is lower than 11 m/s, the RMSE is less than 2 m/s. The reasons of bad RMSE for high wind speed are that (1) the low percentage of high wind speed in train set causes the poor fitting of expression (31); and (2) the sensitivity of the observable on wind speed decreases as wind speed increases. Moreover, it is pointed that the approach based on the expression (27) can take less time about 3000 times than the approach based on the expression (26).

Figure 15. Scatter plot of observable computed by expression (a) (26) and (b) (27) vs. wind speed.

Figure 16. Scatter plot of ASCAT-A/B and retrieved wind speed.

The retrieval model is the mapping linking wind speed with the bi-static scattering coefficient computed by expression (26) or (27). Note that because of the unknown transmit power and

them as the constants. Moreover, when the incident angle of GNSS signal is lower than 35�, 1=cos<sup>2</sup>θ could be used to replace A<sup>0</sup> in the expression (27) because of the proportional relationship between them [41]. Therefore, the observable used to develop the link with wind speed is not a true bi-static scattering coefficient. The selected data are divided into two groups: train

and test set. The relationship between the observable and wind speed is fitted as

obs is the obervable computed by expressions (26) and (27) when <sup>A</sup><sup>2</sup>

3000 times than the approach based on the expression (26).

<sup>U</sup><sup>10</sup> <sup>¼</sup> Ae<sup>B</sup>σ<sup>2</sup>

is replaced using 1=cos2θ; and A, B, and C are fitted parameters. Figure 15 shows the inversely proportional relationship between the observable computed by expressions (26) and (27) and wind speed. The fitted parameters in expression (31) are obtained by fitting the observables of train set with wind speed measured by ASCAT-A/B. The compared results between ASCAT-A/B-retrieved wind speed using test set are presented in Figure 16 and Table 2. The biases between ASCAT-A/B and retrieved wind speed are ignorable, 0.024 m/s and 0.021 m/s, for the observables derived by expressions (26) and (27). The RMSEs are 2.11 m/s and 2.15 m/s, respectively. In addition, it is clear that the retrieving accuracy for high wind speed is larger than the low wind speed as illustrated in Figure 17, in which when wind speed is lower than 11 m/s, the RMSE is less than 2 m/s. The reasons of bad RMSE for high wind speed are that (1) the low percentage of high wind speed in train set causes the poor fitting of expression (31); and (2) the sensitivity of the observable on wind speed decreases as wind speed increases. Moreover, it is pointed that the approach based on the expression (27) can take less time about

<sup>R</sup> on the bi-static scattering coefficient ignored by assuming

obs þ C (31)

<sup>R</sup> are ignored and A<sup>0</sup>

antenna gain, the influence of A<sup>2</sup>

110 Multifunctional Operation and Application of GPS

Figure 14. Relationship between geometric resolution and incident angle.

where σ<sup>2</sup>


Table 2. Trained parameters of Eq. (31) using train set and corresponding test performance using test set.

The SNR of DDM has an important impact on retrieving result of wind speed as shown in Figure 18. The accuracy of retrieved wind speed is improved by selecting the DDMs with higher SNR. However, it should be noted that the larger the threshold of the SNR is, the lesser

of spaceborne GNSS-R, including the geometry, polarization, and scattering model were discussed. The geometry of GNSS-R mainly is determined by the incident angle of GNSS signal, and the height of LEO and GNSS satellite. When GNSS signal occurs reflection on the sea surface, the polarization of GNSS signal is changed. At nadir and small incident angle, the LCHP component dominates the reflected GNSS signal. For the diffuse scattering of the GNSS signal, the correlation power level called as DDM spreads to large delay and Doppler frequency and presents the horseshoe shape in delay-Doppler domain. Then, the spaceborne receivers and processing methods were discussed. The results showed that parallel correlation in Doppler domain has lowest computational complexity and optimal performance so that it is optimal to choose to process the reflected GNSS signal in real-time for spaceborne receiver. Finally, the approach based on DDM metric was used to retrieve wind speed. Proposed DDM metrics have inversely proportional relationship with wind speed. By latitude mask and the threshold of SNR to remove low-quality data, proposed approaches could obtain the RMSE of 2.11 m/s and 2.15 m/s. For further improvement of retrieving accuracy, it is necessary to set higher SNR threshold which results in lower spatial coverage. In one word, GNSS-R will become an effective way to observe global sea wind speed, especially low attenuation of GNSS

GNSS Application in Retrieving Sea Wind Speed http://dx.doi.org/10.5772/intechopen.74149 113

signal for the rainfall will be an optimal choice to monitor tropical cyclone.

School of Electronic and Information Engineering, Beihang University, Beijing, China

[1] Adler RF. Estimating the benefit of TRMM tropical cyclone data in saving lives. In: American Meteorological Society, 15th Conference on Applied Climatology; 2005; Savannah, USA

[2] Draper DW, Mccabe PJ. A review of satellite altimeter measurement of sea surface wind speed: With a proposed new algorithm. Journal of Geophysical Research. 1985;90(C3):

[3] Stoffelen A, Anderson DLT. Wind retrieval and ERS-1 scatterometer radar backscater measurements. Advances in Space Research. 1993;13(5):53-60. DOI: 10.1016/0273-1177(93)90527-I

[4] Chang PS, Li L. Ocean surface wind speed and direction retrievals from the SSM/I. IEEE Transactions on Geoscience and Remote Sensing. 1998;36(6):1866-1871. DOI: 10.1109/36.729357

[5] Komkathy A, Armatys M, Masters D, Axelrad P, Zavorotny VU, Katzberg SJ. Retrieval of ocean wind speed and wind direction using reflected GPS signals. Journal of Atmo-

Author details

References

Dongkai Yang\* and Feng Wang

\*Address all correspondence to: wangf.19@163.com

4707-4720. DOI: 10.1029/JC090iC03p04707

spheric and Oceanic Technology. 2004;21(3):515-526

Figure 17. Relationship of RMSE of retrieved wind speed vs. wind speed.

Figure 18. Trend plot of RMSE as the threshold of SNR changing.

the DDMs used to the retrieved wind speed are, i.e., the coverage of the UK-TDS-1 is lower for the higher threshold of the SNR. It has to be noted that no precise calibration and correction for the DDM measurement in SGR-ReSI also is one of the reasons causing the low performance of retrieving wind speed.

#### 5. Conclusion

GNSS-R has been a new tool of remote sensing to monitor Earth's physical parameters, such as sea wind speed, sea surface height, sea ice, soil moisture and others. Following the success of UK-DMC satellite which was carried on the first GNSS-R receiver, the spaceborne GNSS-R which could provide the global and all-days observation has been an hot focus of the research groups. Some spaceborne missions have been proposed and planned, such as CYGNSS of NASA, GEROS-ISS of ESA, <sup>3</sup> CAT-2 of the Remote Sensing Lab and the NanoSat Lab at the universitat Politècnica de Catalunya-Barcelona Tech. This chapter was focused on the discussion of spaceborne GNSS-R to monitor sea wind speed. First, the basic theories of spaceborne GNSS-R, including the geometry, polarization, and scattering model were discussed. The geometry of GNSS-R mainly is determined by the incident angle of GNSS signal, and the height of LEO and GNSS satellite. When GNSS signal occurs reflection on the sea surface, the polarization of GNSS signal is changed. At nadir and small incident angle, the LCHP component dominates the reflected GNSS signal. For the diffuse scattering of the GNSS signal, the correlation power level called as DDM spreads to large delay and Doppler frequency and presents the horseshoe shape in delay-Doppler domain. Then, the spaceborne receivers and processing methods were discussed. The results showed that parallel correlation in Doppler domain has lowest computational complexity and optimal performance so that it is optimal to choose to process the reflected GNSS signal in real-time for spaceborne receiver. Finally, the approach based on DDM metric was used to retrieve wind speed. Proposed DDM metrics have inversely proportional relationship with wind speed. By latitude mask and the threshold of SNR to remove low-quality data, proposed approaches could obtain the RMSE of 2.11 m/s and 2.15 m/s. For further improvement of retrieving accuracy, it is necessary to set higher SNR threshold which results in lower spatial coverage. In one word, GNSS-R will become an effective way to observe global sea wind speed, especially low attenuation of GNSS signal for the rainfall will be an optimal choice to monitor tropical cyclone.
