1. Hyperspectral IR Clear/Cloudy
Sounding Retrievals
Xuebao Wu, Jun Li, Chian-Yi Liu
In collaboration with CIMSS colleagues
Cooperative Institute for Meteorological Satellite Studies
University of Wisconsin-Madison
Madison, WI 53706, U.S.A.
This research is supported by MURI and NOAA GOES-R risk reduction project
5th Workshop on Hyperspectral Science of UW-Madison MURI and GOES-R
7 – 9 June 2005 The Pyle Center, Madison, WI

2. Outline (Clear Study) 1. Algorithm---regression and physical
2. HES simulation
3. AIRS demonstration
(Cloudy Study)
4. Cube Simulation (IHOP cloudy study) on HES
5. Profile retrieval on cloud-cleared radiance
6. IR/MW synergy study

3. 1. Clear IR Sounding Retrieval
Regression
Physical retrieval (regularization) which needs
--Radiance measurements and observation errors
--First guess from regression or other sources
--RTM, linearized RTM and adjoint

4. Regularization Inversion
Equations
Unknowns
Solutions
Newton Iteration
Discrepancy Principle (Li and Huang 1999)

5. Discrepancy Principle
Residual = Observation error

6. 2. HES simulation

7. (Example 1 is used in the simulation)
Waveband (cm-1) Wavelength (um) Unapodized spectral bin size (cm-1)
650 – –
1650 – –

8. GOES
HES

9. GOES
HES

10. NOAA88 6055 Global RAOB profiles between 65S and 65N
Training or dependent data set (9/10 profiles)
Testing or independent data set (1/10 profiles)
Simulated radiances
Simulated radiances
Radiance Eigenvector calculation
Radiances added with instrument noise
Radiances added with instrument noise
Obtained EV regression retrievals
Regression coefficient
Physical retrievals
Retrieval RMS statistics

12. Clear sky HES sounding retrieval RMSE (Regression versus Physical)

13. Spectral resolution (0.3, 0.6, 1.2 cm**-1) impact on T/q retrievalLW
SMW
HES

14. 3. AIRS Demonstration
ARM AIRS/GOES Sounder/RAOB matchup (July 2002 – April 2003)
Use AIRS retrieval algorithm developed at CIMSS (regression + physical)
GOES Sounder retrievals are from operational products

15. Dewpoint temperature soundings
AIRS versus GOES Sounder (Mar.6,2003)

16. AIRS Std. Operational Product
CIMSS

17. 4. IHOP Cube Simulation on Cloudy Sounding
Cloudy radiative transfer model - Wei et al. (2004, IEEE TGARS)
MM5 data is used, spatial resolution is 4 ~ 8 km
Temporal resolution: 30 minutes
One cloud-layer is assumed

18. True
HES Retrieval
True – Retrieval
No soundings due to thick clouds
Q: 250 hPa

19. True
HES Retrieval
True – Retrieval
No soundings due to thick clouds
Q: 500 hPa

20. True
HES Retrieval
True – Retrieval
No soundings due to thick clouds
Q: 850 hPa

23. 5. Retrieval on Cloud-Cleared Radiances
Optimal cloud-clearing for AIRS radiances using MODIS
Presentation: Allen Huang’s talk
Publication: Jun Li et al.,2005, IEEE Trans. on Geoscience and Remote Sensing

26. 6. IR/MW Synergy Study HES assumptions: AMSU assumption:
Spectral coverage: 650 – – 2250 cm-1
Spectral resolution cm-1
Noise from PORD
AMSU assumption:
AMSU+HSB on Aqua
Noise from AMSU instrument specification

29. The approximate linear form of RTE for IR or microwave sounding unit channel (Li et al. 2000):
=0 when Ts=Ta
=0 when ε=1
=0 when p ->ps

31. Summary
Clear sky sounding retrieval algorithm has been tested using AIRS data. Both regression and physical retrieval work reliably.
Cube data study from IHOP case has demonstrated that HES provides retrievals with better accuracy and coverage (in partial cloud cover) than the current GOES sounder.
Cloud-clearing algorithm has been developed for single-layer cloudy sounding retrieval.
Imager/Sounder/MW combination is also in progress.