EDR Algorithms for the Cross-track Infrared Sounder

1. EDR Algorithms for the Cross-track Infrared Sounder Xu Liu Atmospheric Environmental research, Inc.131 HartwellAve, Lexington, Ma 02421 Contact: Xliu@aer.com and Ronald J. Glumb, Christopher E. Lietzke and Joseph P. Predina ITT Industries, ITT Aerospace/Communications,1919 West Cook Road, P.O. Box 3700, Fort Wayne, IN 46801, USA Contact: joe.predina@itt.com

2. CrIMSS EDR Products CrIS EDR Algorithm Climateology(first guess) ps (NWP) Surface Map

3. AMSU/MHS Channels Used(ATMS to replace AMSU/MHS) CrIS EDR Algorithm

4. CrIS Channels Used CrIS EDR Algorithm • Channels Excluded During MW/IR Retrieval • Ozone Band from 950 cm-1 to 1095 cm-1 • Trace gas channels • Low information content channels (channel selection) • Good results using only 400, 300 and 150 channels • Special High Information Content Channels Used • 709.5 cm-1 to 746 cm-1 for cloud parameter h estimation and Principle Component Analysis (PCA) for estimating number of cloud formations • 2190 cm-1 to 2250 cm-1 for cloud parameter h estimation

5. Fast Forward Model CrIS EDR Algorithm • Optimal Spectral Sampling (OSS) • Developed at Atmospheric Environmental Research (AER) • Fast and accurate (validated against Line-by-line models) • Can model non-localized Instrument Line Shape (ILS) • Computes Jacobian efficiently • Accurate treatment of reflective radiation from surface • Both MW and IR versions developed Benchmarked against AIRS fast forward model in 1999……… OSS > 20 times faster OSS > 40 times faster when including Jacobians Only 20% Speed Penalty when Modeling sinc vs. Blackman-Harris Instrument ILS

6. Retrieval Methodology (Rogers, 1976) CrIS EDR Algorithm Error Covariance Matrix of Background Observed Radiance Error Covariance Matrix of Measurement Calculated Radiance GeneralForm Used Matrix of Partial Derivatives of yi With Respect to x Background Temp/moisture/Emissivity/Reflectivity/etc. Retrieved Variable EOF Form Used EOF Form Has Much Smaller Dimension & Is Modified to Handle Nonlinear Case

7. Retrieved Parameters CrIS EDR Algorithm

8. CrIMSS Retrieval Process (1 of 6) CrIS EDR Algorithm • Initialization • Load instrument specifications (AMSU, MHS, CrIS frequencies, Noise, etc.) • Load climatoligical atmospheric data base • Load surface background data base (Mean profiles, error covariance matrices) • Load OSS parameters (Optical depth tables) • Load solar spectrum • Load topography & land/ocean mask Used for First Guess Fast Forward Model Parameters Indexed Every 10 Degree of Temperature GTOPO30 Digital Elevation Map, land/ocean mask

9. CrIMSS Retrieval Process (2 of 6) CrIS EDR Algorithm • Preprocessing • Test for precipitation • Compute surface pressure from NWP data • Test for surface type • Land • Ocean • Ice • Snow • Coast: ocean/land • Coast: ice/snow • Choose Background by Surface type & mix with emissivity information Interpolated in Time and Space Land/ocean maps & MW Brightness Temperature tests @ 31, 50, 89 & 23 GHz MW Ocean Emissivity per Wilheit model (Wilheit, 1979) MW Land Emissivity per Grody model (Grody, 1988)

10. CrIMSS Retrieval Process (3 of 6) CrIS EDR Algorithm • Microwave Only Retrieval • Average radiances from 9 MHS FOVs • Execute OSS fast forward model based upon climatology first guess • Perform inversion & update first guess profile • Calculate new radiances using OSS • Test for convergence • Continue iteration if convergence criteria not met

11. 3 2 1 6 5 4 9 8 7 CrIMSS Retrieval Process (4 of 6) CrIS EDR Algorithm • Scene Classification • Purpose • Maximize # of good retrievals within a field of regard (FOR) • Optimize an FOV clustering strategy for treatment of clouds and use of cloud clearing algorithm • Method • Form matrix of 9 FOVs by 62 IR channels in the spectral region from 709 to 748 cm-1 • Analyze for up to 9 principle components • Determine number of cloud formations present from PCA analysis & 2 statistical tests • Cluster minimum number of FOVs needed to perform cloud clearing Example of PCA Analysis 3 principle components indicating 2 cloud formations

12. CrIMSS Retrieval Process (5 of 6) CrIS EDR Algorithm • Combined MW/IR Retrieval • Uses MW retrieval as first guess (profiles, surface, cloud parameters) • Modified Maximum Likelihood Method for nonlinear retrieval • Dynamically adjust channel weights to improve convergence and stability • Cloud clearing parameter h estimated each iteration • FOVs averaged above 80 mbar • Test for convergence • Continue iterations if convergence criteria not met

13. CrIMSS Retrieval Process (6 of 6) CrIS EDR Algorithm • Quality Control • If normalized c2 > 1.0 for the retrieval, then retrieval is not reported • If MW radiances after MW/IR retrieval differ by more than 3 K from observed radiances, then entire retrieval rejected • If MW retrieval and MW/IR retrieval differ by more than 3 K, then the MW/IR retrieval is rejected • If difference between cloud cleared CrIMSS radiances and VIIRS cloud free radiance is greater than 2 k, then retrieval is suspect

14. Performance for Various Levels of Channel Selection CrIS EDR Algorithm

15. CrIMSS Projected Performance CrIS EDR Algorithm Based on Global Average

16. CrIS CrIS HIRS HIRS Expected Improvement Over HIRS(RMS Uncertainty, Global Average Basis) CrIS EDR Algorithm