Some Aspects of AIRS Sounding Retrieval and their Impact on IMAPP Products

1. Some Aspects of AIRS Sounding Retrieval and their Impact on IMAPP Products Pradeep Thapliyal Visiting Scientist, CIMSS/SSEC, UW Madison Scientist, Space Applications Centre, ISRO, Ahmedabad, India pkthapliyal@sac.isro.org

2. Outline • Regional Regression Analysis • Sunglint effect on AIRS retrieval • Sensitivity to Reflectivity Parameterization • Sensitivity to Spectral IR Surface Emissivity • Sensitivity to Principal Components • Linearization of humidity vs radiance • Detection of Low level inversion • Conclusion

3. Regional Regression Analysis • Significant for Geostationary sounding retrieval and IMAPP broadcast users interested in limited spatial domain. • Radiosonde data around Indian region for two years 2003-2004 (Courtesy: H. Woolf, CIMSS). • Data for 2003 (N=21325) used as training dataset for regression coefficient • Data for 2004 (N=9820) used to independently validate the regression algorithm • Day and night radiances for testing dataset simulated from SARTA using random solzen (0-80) and fixed (90), respectively. • Data over mountains not used (PS > 985 hPa) for both training and testing dataset. • Spectral emissivity to each profile is assigned based on IGBP ecosystem classification and MOD11 emissivity dataset (Courtesy: Seemann & Borbas, CIMSS). • Regional regression coefficients (R1688) computed similar to IMAPP Global regression (G1688, PCR technique). • Since shortwave channels are affected by reflected solar radiation, separate regression coefficients are computed after excluding SW channels (R1422).

4. Histogram of temperature (K) at different levels Location of training profiles for global and regional datasets (Regional dataset: Courtesy – Haal Woolf) Histogram of water vapor (g/kg) at different levels

5. RMS error for temperature and humidity retrieval from regional testing dataset using regression coefficients G1688, R1688 and R1422

6. RMS error for temperature and humidity retrievals from global AIRS matchup dataset using regression coefficients G1688, R1688 and R1422 Sunglint MODIS 31May2005-0845Z MODIS 31May2005-2045Z MODIS Images showing cloud conditions over selected day/night scene

7. Retrieved TS using global and regional coefficients and their differences Top panel -daytime granule and bottom panel - nighttime granule

8. Retrieved T 500 mb using global and regional coefficients and their differences Top panel - daytime granule and bottom panel -nighttime granule

9. Retrieved T 200 mb using global and regional coefficients and their differences Top panel - daytime granule and bottom panel - nighttime granule

10. Retrieved TPW using global and regional coefficients and their differences Top panel - daytime granule and bottom panel - nighttime granule

11. Retrieved WV ~932 mb (g/kg) using global and regional coefficients and their differences Top panel - daytime granule and bottom panel - nighttime granule

12. IGBP classification based MODIS emissivity (Courtesy: Seeman & Borbas, CIMSS)

13. IMAPP Retrieved spectral surface IR emissivity using global coefficients for daytime granule

14. IMAPP Retrieved spectral surface IR emissivity using global coefficients for nighttime granule

15. Sunglint effect on AIRS retrieval Tb at 950.886 cm-1 Nighttime Tb at 2665.244 cm-1 Daytime Tb at 950.886 cm-1 Daytime Tb at 2665.244 cm-1 Nighttime Brightness temperature for day/night granules in longwave and shortwave window channels AIRS Tb spectra for sunglint maxima (Blue curve) and sunglint free region (Red curve) for daytime granule (~02:00 PM Local Time)

16. Seasonal movement of sunglint affected region shown by daytime shortwave window channels Tb

17. Differences in retrieved TPW, TS, and T at 931 and 853 mb using regional regression coefficients R1422 andR1688

18. IMAPP Retrieved emissivity using regional coefficients R1688 for daytime granule

19. IMAPP Retrieved emissivity using regional coefficients R1422 for daytime granule

20. Sensitivity of Reflectivity Parameterization IMAPP : LRHOT = .FALSE. Exp: LRHOT : LRHOT = .TRUE. Differences in retrieved TPW, TS, and T at 931 and 853 mb using R1688 and LRHOT daytime (top panels) and nighttime (bottom panels).

21. IMAPP Retrieved emissivity using LRHOT for daytime granule

22. Sensitivity to Spectral IR Surface Emissivity TPW Diff (cm) (LowResEmis – HighResEmis) TS Diff (K) (LowResEmis – HighResEmis) Different emissivity spectra (UCSB) assigned to the training dataset over sea and desert regions. Difference in Retrieved TPW (cm) and TS (K) using high and low resolution emissivity spectra in training dataset over sea and desert regions.

23. Sensitivity of IMAPP retrieval to emissivity uncertainty Regional regression coefficients were generated with fixed spectral emissivity (=0.98) in the training dataset (R1688FixEmis). RMS errors derived for retrieval using simulated radiances (test dataset) with fixed emissivity (0.98). Compared with the RMS errors for regional regression coefficients with ecosystem based spectral emissivity classification (R1688).

24. Sensitivity of AIRS brightness temperature spectra to spectral surface emissivity during daytime and nighttime for standard tropical atmosphere (Emis1: constant IR spectral emissivity = 0.99, Emis2: constant IR spectral emissivity = 0.90)

25. Sensitivity to Principal Components Retrieved TPW (cm) and TS using number of principal components (from noisefree covariance matrix) N=30 (top panels) and N=60 (bottom panels)

26. RMS errors in T and WV profiles using different numbers of PCs N=25, 30, 35, 40, 50, 60 for noisy PCR (top panels) and noisefree PCR (bottom panels)

27. Linearization of humidity w.r.t. radiance Demonstration of linearity/non-linearity between WV at 850 mb and WV predictor at 850 mb, i.e. difference in Rad/ Tb for channels with weighting function peak at 850 mb and surface RMS errors in humidity retrievals using ‘q’ (G1688qlin) and ‘ln(q)’ (G1688) as predictant.

28. ARM Profiles and AIRS Window Channel Radiances No Inversion Inversion Detection of Low Level Inversion Fig.34: AIRS Tb spectra in longwave IR window for normal profile (no-inversion, top panel) and inversion case (bottom panel). Corresponding temperature and humidity profiles for ARM best estimate and AIRS standard product retrieval are also shown indicating normal and inversion temperature profile near surface.

29. BT with Normal temperature profile BT with Normal temperature profile BT with Temperature inversion at surface BT with Temperature inversion above surface Difference in BT Difference in BT Fig.35: Simulated AIRS Tb spectra using subarctic winter atmosphere for normal profile (no-inversion, black curve) and inversion case (red curve for inversion at surface and blue curve for inversion above surface). Corresponding temperature and humidity profiles are also shown indicating normal and inversion temperature profile at surface and above surface.

30. Fig.36: Simulated AIRS Tb spectra using standard tropical atmosphere for normal profile (no-inversion, black curve) and inversion case (red curve for inversion at surface and blue curve for inversion above surface). Corresponding temperature (2 cases) and humidity profiles (6 cases) are also shown indicating normal and inversion temperature profile at surface and above surface.

31. Conclusions • Localized regression coefficients are better for retrieval over specific regions, particularly for humidity profiles - Significant for development of geostationary sounder retrieval algorithm, e.g. GOES for US region and INSAT-3D for Indian-ocean region. • Global training dataset may be improved to make the distribution more Gaussian and removing dry bias. Since high nonlinearity exists between radiances and WV, it may be worth examining the impact on retrieval if WV in training dataset is made Gaussian. • Due to large uncertainties due to reflected solar radiation during daytime only LW channels should be used. This require separate regression coefficient for day and night. Alternatively, this may also be achieved by using better reflectivity parameterization (e.g. LRHOT). However, while using better reflectivity parameterization in regression analysis, an additional predictor for solar/satellite zenith angle should also be used that will control the magnitude of regression coefficients with predictors in shortwave channels. • To minimize the uncertainty error in the retrieval due to variable spectral surface emissivity, it may be better to use separate regression coefficients for land and ocean. This may improve retrieval over ocean due to the smaller uncertainties and variability in the spectral surface emissivity over seawater. This may also be achieved by including a-priori spectral surface emissivity as additional predictor in the regression analysis. • q instead of ln(q) for regression with AIRS radiances for retrieval may improve humidity retrievals. This needs further investigation. For linearization in regression analysis it may be better to use Tb instead of radiances and dew-point temperature instead of humidity mixing ratio. This would require use of SRF based coefficients to compute Tb from radiances instead of using simple planck’s formula with central wavenumber.

32. Suitable number of PCs may be used separately in noise-free PCR for humidity and temperature retrievals, whereas similar numbers of PCs may be used in noisy PCR. • High-resolution spectral emissivity is needed for surfaces having high spectral gradients, e.g. desert. However, it may be appropriate to include high-resolution surface emissivity only in window channels and week absorption channels, since in strong absorption bands radiances have no sensitivity to surface properties, such as surface emissivity. • In IMAPP retrieval routine - check for retrieved humidity mixing ratio above saturation and to replace the retrieved humidity with saturation mixing ratio if it exceeds the saturation value. • Profiles with P > PS should not be used for regression coefficient computation for parameters at level with pressure P. e.g. in present version of IMAPP regression there are only ~8400 profile (out of ~12000 profiles) having surface below 986 mb (level# 97) level and for remaining profiles the atmospheric parameters are extrapolated from upper levels. Use of these extrapolated profiles should be avoided for regression coefficients generation and also for computation of means for all parameters at that level. • Present 1000 cm-1 channel for BT classification in IMAPP regression may be replaced with suitable window channel because this channel is affected by ozone and water vapor absorption.

33. Thanks…..