**6. Application to N**2**O**

20 Will-be-set-by-IN-TECH

(channel at *σ* = 1210.75 cm−1) to methane is shown in 15(c) for the tropical and High Latitude Winter models of atmosphere, whose main atmospheric parameters have been shown in Fig.

> 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Pressure (hPa)

Fig. 15. The polynomial fit at 1210.75 cm−<sup>1</sup> exemplified for two models of atmosphere, (a) and (b); the sensitivity to methane (c); the reference methane mixing ratio profile (d).

The mechanism of the procedure to estimate CH4 columnar amount from IASI observations is the same as that illustrated for CO2 in section 3.3. The procedure for methane has been applied to the 25 IASI spectra and the results are shown in Fig. 16(a). We see that the columnar amount is very stable and varies in between 1.60-1.90 ppmv with an average of (1.70 ± 0.02) ppmv. We remember that this data were acquired in 2007. Today the average global value of methane is credited of a value equal to 1.74 ppmv (Blasing, 2011). During the JAIVEx experiment there were no in situ observations of methane. However, we can perform a consistency check about the observed and computed variability. According to our procedure, the accuracy of the estimates is ≈ 0.094 ppmv. Because the JAIVEx case study consider a limited target area, we have to expect a very low variability as far as the columnar amount of CH4 is considered. This means that the variability we see in Fig. 16(a) has to be largely due to random fluctuations, therefore the standard deviation of the 25 IASI estimates has to be consistent with the computed accuracy of 0.094 ppmv. In fact, this standard deviation gives

qCH4 (ppmv)

−1.5 −1 −0.5 <sup>0</sup> 0.5 <sup>1</sup> 1.5 <sup>2</sup> 2.5 <sup>0</sup>

Data Points Best Fit

D1

(b) High Latitude Winter model

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

(d) CH4 reference profile

CH4 mixing ratio (ppv) x 10−6

Data Points Best Fit

> Tropical HLW

−1.5 −1 −0.5 <sup>0</sup> 0.5 <sup>1</sup> 1.5 <sup>2</sup> 2.5 <sup>0</sup>

D1

(a) tropical model

0 0.2 0.4 0.6 0.8 1

(c) Sensitivity to CH4

Sensitivity, SCH4 , Δ (ppmv)−1

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Pressure (hPa)

**5.1 Application to IASI data**

the value 0.0945 ppmv.

qCH4 (ppmv)

4

As for the methane, N2O is not a linear molecule, therefore partial interferograms, which are capable of enhancing the variations of this gas with respect to those of other dominant atmospheric parameters, have to be be judiciously found by careful inspections of synthetic interferogram signals generated as a function of N2O columnar amount, q*<sup>N</sup>*2*O*. Using this strategy we have found that the partial interferogram in the range 1.06-1.08 cm for a width of 0.02 cm is largely sensitive to N2O. With this reduced bandwidth, according to Eq. 11, we have a noise reduction within the difference spectrum of 10. Actually, because of the effect of IASI noise correlation, the reduction factor is even higher. However, we have to consider that N2O absorption insists within the IASI band 3, which is that with the worse signal-to-noise ratio. With this in mind we have that accuracy with which we can estimate *qN*2*<sup>O</sup>* is of the order of 10% at the level of single channels. In addition, *good* channels tend to be strongly correlated, therefore there is no advantage in trying to combine them to improve the final accuracy.

The regression relation, which fits to the data with an error of less than 3 ppbv is a polynomial of fourth order. The polynomial is independent of the atmospheric sate vector as it is shown in Fig. 17 which exemplifies the polynomial regression for the case of the *d*-channel at 2165.50 cm−1.

The N2O reference profile we use for the radiative transfer calculation is that shown in Fig. 17(d), which gives a columnar amount for N2O of 306.5 ppbv. The sensitivity, *SN*2*O*,Δ(*σ*) of the *d*-spectrum (channel at *σ* = 2204.5 cm−1) to N2O is shown in 17(c) for the tropical and High Latitude Winter models of atmosphere, whose main atmospheric parameters have been shown in Fig. 4

#### **6.1 Application to IASI data**

The philosophy and mechanism of the procedure to estimate N2O columnar amount from IASI observations is the same as that illustrated for CO2 in section 3.3. For N2O, the procedure

Observations. Methodological Aspects and Application to IASI 23

Atmospheric Gases Concentrations from High Spectral Resolution Satellite Observations...

<sup>269</sup> Fourier Transform Spectroscopy with Partially Scanned Interferograms as a Tool to Retrieve

over the Mediterranean area for the month of July 2010 (see Fig. 18(b)), we see that the average concentration comes closer to the monthly values normally observed with in situ observation

We have described and presented the basic aspects of Fourier Spectroscopy with Partially Scanned Interferogram and exemplified its application to the retrieval of minor and trace gases in the atmosphere. Observations from the IASI instrument have been considered and the technique has been applied to estimate the columnar amount of CO2, CO, CH4 and N2O. The retrieval algorithms we have implemented rely on simple polynomial regression relations, whose coefficients, once properly standardized, are independent of the atmospheric state. However, the technique needs the standardization parameters, which may depend on the state vector. Thus, the procedure still relies on the availability of a suitable a-priori best estimate of the atmospheric state vector. We have shown that this best estimate can be confidently obtained by previously inverting IASI radiances for skin temperature, and temperature, water vapour and ozone profiles. The accuracy of this *best estimate* can be by far lower than that expected from IASI radiances themselves. We have shown that uncertainty about the temperature profile of the order of ±2K along the profile are easily tolerated. For water vapour we can easily tolerate uncertainties of more than ±20%, along the profile.

FTS\*PSI is a truly novel methodology as far as its applications to high spectral resolution infrared observations is concerned. The tools we have presented in this work have not been particularly optimized. Nevertheless, their applications to atmospheric gases yielded impressive results for accuracy and quality, which are unprecedented if compared to those normally obtained with the usual machinery of inverting spectral radiances. We think that the capability of the methodology has been only scratched at the surface, and we hope this study can soon attract attention and stimulate new research studies, which can hopefully exploit the

IASI has been developed and built under the responsibility of the Centre National d'Etudes Spatiales (CNES, France). It is flown onboard the Metop satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near real time data distribution service. We thank Dr Stuart Newman (Met Office) for providing the JAIVEx data. The JAIVEx project has been partially funded under EUMETSAT contract Eum/CO/06/1596/PS. The FAAM BAe 146 is jointly funded by the Met Office and the Natural Environment Research Council. The US JAIVEx team was sponsored by the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program

Amato, U.; De Canditiis, D.; Serio, C. (1998). Effect of apodization on the retrieval

of geophysical parameters from Fourier-Transform Spectrometers. *Appl. Opt.*, 37,

Furthermore, the technique is largely insensitive to the surface emission.

at ground level stations.

many facets of the tool.

**8. Acknowledgements**

Office (IPO) and NASA.

6537-6543.

**9. References**

**7. Conclusion**

Fig. 17. The polynomial fit at 2165.50 cm−<sup>1</sup> exemplified for two model of atmosphere, (a) and (b); the sensitivity to N2O (c); the reference N2O mixing ratio profile (d).

has been applied to the 25 IASI spectra and the results are shown in Fig. 18(a). We see that the columnar amount varies in between 250-450 ppbv with a peak-to-peak variability of about 200 ppbv. The average value over the 25 soundings is 303 ppbv. This is a bit lower than the today average global mean value (≈ 323 ppbv (Blasing, 2011)) estimated based on in situ observations at ground level stations. However, N2O may be characterized by large anomaly from its mean global value even on monthly time scales (Blasing, 2011; Lubrano et al., 2004; Ricaud et al., 2009). However, if we consider the monthly map of N2O computed

Fig. 18. (a)- N2O integrated amount estimated from IASI. (b)- IASI N2O for July 2010 over the Mediterranean area.

over the Mediterranean area for the month of July 2010 (see Fig. 18(b)), we see that the average concentration comes closer to the monthly values normally observed with in situ observation at ground level stations.

#### **7. Conclusion**

22 Will-be-set-by-IN-TECH

Pressure (hPa)

Fig. 17. The polynomial fit at 2165.50 cm−<sup>1</sup> exemplified for two model of atmosphere, (a) and

has been applied to the 25 IASI spectra and the results are shown in Fig. 18(a). We see that the columnar amount varies in between 250-450 ppbv with a peak-to-peak variability of about 200 ppbv. The average value over the 25 soundings is 303 ppbv. This is a bit lower than the today average global mean value (≈ 323 ppbv (Blasing, 2011)) estimated based on in situ observations at ground level stations. However, N2O may be characterized by large anomaly from its mean global value even on monthly time scales (Blasing, 2011; Lubrano et al., 2004; Ricaud et al., 2009). However, if we consider the monthly map of N2O computed

Fig. 18. (a)- N2O integrated amount estimated from IASI. (b)- IASI N2O for July 2010 over the

qN<sup>2</sup>

O

−2.5 −2 −1.5 −1 −0.5 <sup>0</sup> 0.5 <sup>1</sup> 1.5 <sup>0</sup>

Data Points Polynomial Best Fit

D1

(b) High Latitude Winter model

0 50 100 150 200 250 300 350

(d) N2O reference profile

N2 O mixing ratio (ppbv)

Average N2O concentration for July 2010 (ppbv)

(b) Mediterranean case study

−2.5 −2 −1.5 −1 −0.5 <sup>0</sup> 0.5 <sup>1</sup> 1.5 <sup>0</sup>

Data points Plynomial Best Fit

D1

(a) tropical model

High Latitude Winter Tropical

−7 −6 −5 −4 −3 −2 −1 0 1

(c) Sensitivity to N2O

(b); the sensitivity to N2O (c); the reference N2O mixing ratio profile (d).

SN2 O, Δ (ppmv)−1

<sup>0</sup> <sup>5</sup> <sup>10</sup> <sup>15</sup> <sup>20</sup> <sup>25</sup> <sup>150</sup>

Number of IASI sounding

(a) JAIVEx case study

Mediterranean area.

qN<sup>2</sup>

O (ppbv)

Pressure (hPa)

qN<sup>2</sup>

O (ppbv)

We have described and presented the basic aspects of Fourier Spectroscopy with Partially Scanned Interferogram and exemplified its application to the retrieval of minor and trace gases in the atmosphere. Observations from the IASI instrument have been considered and the technique has been applied to estimate the columnar amount of CO2, CO, CH4 and N2O.

The retrieval algorithms we have implemented rely on simple polynomial regression relations, whose coefficients, once properly standardized, are independent of the atmospheric state. However, the technique needs the standardization parameters, which may depend on the state vector. Thus, the procedure still relies on the availability of a suitable a-priori best estimate of the atmospheric state vector. We have shown that this best estimate can be confidently obtained by previously inverting IASI radiances for skin temperature, and temperature, water vapour and ozone profiles. The accuracy of this *best estimate* can be by far lower than that expected from IASI radiances themselves. We have shown that uncertainty about the temperature profile of the order of ±2K along the profile are easily tolerated. For water vapour we can easily tolerate uncertainties of more than ±20%, along the profile. Furthermore, the technique is largely insensitive to the surface emission.

FTS\*PSI is a truly novel methodology as far as its applications to high spectral resolution infrared observations is concerned. The tools we have presented in this work have not been particularly optimized. Nevertheless, their applications to atmospheric gases yielded impressive results for accuracy and quality, which are unprecedented if compared to those normally obtained with the usual machinery of inverting spectral radiances. We think that the capability of the methodology has been only scratched at the surface, and we hope this study can soon attract attention and stimulate new research studies, which can hopefully exploit the many facets of the tool.

### **8. Acknowledgements**

IASI has been developed and built under the responsibility of the Centre National d'Etudes Spatiales (CNES, France). It is flown onboard the Metop satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near real time data distribution service. We thank Dr Stuart Newman (Met Office) for providing the JAIVEx data. The JAIVEx project has been partially funded under EUMETSAT contract Eum/CO/06/1596/PS. The FAAM BAe 146 is jointly funded by the Met Office and the Natural Environment Research Council. The US JAIVEx team was sponsored by the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO) and NASA.

#### **9. References**

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Observations. Methodological Aspects and Application to IASI 25

Atmospheric Gases Concentrations from High Spectral Resolution Satellite Observations...

<sup>271</sup> Fourier Transform Spectroscopy with Partially Scanned Interferograms as a Tool to Retrieve

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**12** 

*USA* 

**Identification of Intraseasonal Modes of** 

Michael Richman and Andrew Mercer

*The University of Oklahoma and Mississippi State University* 

**Variability Using Rotated Principal Components** 

Documenting mid-tropospheric global-scale circulation is important to climate modelers. Models are applied to capture the most basic statistics of the flow (e.g., annual and season means and variability). As model physics improve, the goal is to extend the accuracy of the models to shorter, more societally relevant time scales (e.g., monthly average flow). Within a month or season, recurrent modes are present. Comparing such observed flows to modeled counterparts can provide an arbiter of success. There is a long history is isolating such patterns in meteorology using teleconnections (correlation patterns). Some of the initial work by Namias (1980) was used to create atlases of teleconnections at every gridpoint. This approach has the merit of completeness; however, the redundancy in patterns from adjacent gridpoints is inefficient, given present day computational power. An extension of the Namias approach to selected base points provided extensive documentation of Northern Hemisphere winter teleconnection patterns of sea level pressure and 500 mb height (Wallace

A second approach to identifying these variability modes uses eigenvectors to filter the correlation structures into recurrent patterns that are localized. To document the characteristic flow patterns and their time dependent statistics, an objective methodology is applied to portray the localization of the centers of action in each mode. Often, rotated principal component analysis (RPCA) is employed to decompose the aforementioned correlation structure of the flow to obtain information on the morphology of the flow patterns, their associated time behavior and the variability of the total flow associated with each pattern. The eigenvectors are scaled to create principal components that are linearly transformed or rotated to exploit the local structure within the domain, identifying

Barnston & Livezey (1987; hereafter referred to as BL87) present an extensive catalogue of mid-tropospheric patterns using this approach. Their work is somewhat limited by use of a specific rotation (Varimax) that enforces an orthogonal transformation matrix from the principal components to their rotated counterparts. Additionally, they did not test rigorously the number of eigenmodes, selecting ten modes for each analysis. Selecting too few eigenmodes can result in multiple patterns being merged on each eigenvector retained. Moreover, if too many patterns are selected, the circulation modes can be

**1. Introduction** 

& Gutzler, 1981).

fragmented.

physically meaningful circulation patterns.

Smith, W.L.; Howell, H.B.; Woolf, H.M. (1979). The use of interferometric radiance measurements for the sounding the atmosphere. *Appl. Opt.*, 36, 566-575.
