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

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

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

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

Discrepancy Principle Residual = Observation error

2. HES simulation

(Example 1 is used in the simulation) Waveband (cm-1) Wavelength (um) Unapodized spectral bin size (cm-1) 650 – 1200 15.38 – 8.33 0.625 1650 – 2250 6.06 – 4.44 0.625

GOES HES

GOES HES

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

Clear sky HES sounding retrieval RMSE (Regression versus Physical)

Spectral resolution (0.3, 0.6, 1.2 cm**-1) impact on T/q retrieval LW SMW HES

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

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

AIRS Std. Operational Product CIMSS

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

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

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

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

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

6. IR/MW Synergy Study HES assumptions: AMSU assumption: Spectral coverage: 650 – 1200 + 1650 – 2250 cm-1 Spectral resolution 0.625 cm-1 Noise from PORD AMSU assumption: AMSU+HSB on Aqua Noise from AMSU instrument specification

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

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.