CrIMSS EDR Performance Assessment and Improvements

1. CrIMSS EDR Performance Assessment and Improvements Degui Gu, Xia Ma, Denise Hagan and Alex Foo with collaboration from the CrIMSS Cal/Val team April 4, 2013

2. Outline • CrIMSS EDR MX7.0 Performance Assessment • Identify performance degradation areas and potential improvements • Feed into the overall plan for CrIMSS algorithm code and LUT updates leading to stage-1 validated maturity

3. MX7.0 Operational Code Updates and Verification Test on G-ADA • CrIMSS EDR operational software on G-ADA was updated to match the MX7.0 code to be released by IDPS • Updates based on Mike Wilson’s note “MX7_by_upgrade.docx” • Test data based on Golden Day Sept. 20, 2012 • Both ATMS and CrIS SDRs were re-generated on G-ADA • Good CrIS and ATMS SDRs: 2682 granules • Performance assessment was based on a total of 2648 granules that have the collocated ECMWF match-up truth data • Also processed the same test data using NGAS off-line science code to verify G_ADA implementation

4. MX7.0 Performance: Converged FORs and Yields from MW+IR Combined Retrievals

5. MX7.0 Performance: Temperature and Moisture Bias and RMS for Clear Ocean Scenes

6. MX7.0 Performance: Temperature and Moisture Bias and RMS for Clear Land Scenes

7. MX7.0 Performance: Temperature and Moisture Bias and RMS for Partly Cloudy Ocean Scenes

8. MX7.0 Performance: Temperature and Moisture Bias and RMS for Partly Cloudy Land Scenes

9. MX7.0 Performance Summary • Overall excellent EDR quality performance with a few exceptions that see degraded performance • Reasonable yields except for over ice surfaces ( and cloud free ocean) • Performance improvement areas: • Clear Ocean: • Degradation near surface • Yield could be higher • Cloudy Ocean: • Large negative biases and degradation near surface • Larger than expected biases at tropopause • Clear/Cloudy Land: • Degraded quality near surface • Larger than expected biases at tropopause • Ice Mass: • Low convergence • Degraded quality

10. Clear Ocean Performance Degradation Near Surface Caused by Cloud Leakage AVTP Performance Statistics Over Ocean

11. Overcast Misclassified as Cloud Free Cases (1) Tskin Retrieval Error (K) Retrieved Temperature Profiles Tskin_ecmwf Tskin_ret

12. Overcast Misclassified as Cloud Free Cases (2) Brightness Temperature at 900cm-1(K) Tskin Retrieval Error (K)

13. Clear Ocean Performance Improvement by Better Cloud Detection and MW Tuning • Improve scene classification module to identify clear/overcast scene better (LaRC) • Use CrIS surface channel for clear ID (George Augmann) … • Use MW retrieved cloud amount • Tuning number of cloud formation determination parameters • Use VIIRS Cloud Mask to flag clear/overcast scenes? • Use SST to flag clear/overcast scenes? • Yield expects to be improved after MW LUT tuning to make IR and MW data more consistent

14. Cloudy Ocean Performance Degradation by Residual Cloud Contamination AVTP Performance Statistics Over Ocean

15. Cloud Clearing Performance Improvements • Current cloud clearing performance depends on quality of MW only retrieval products (profiles and surface temperature) • MW only retrieval performance can be improved by tuning of a number of LUTs • Scan-dependent SDR bias correction • NEdN, noise reduction by B-G remapping, RTM error • Current cloud clearing re-calculates η after each iteration – potentially move away from the initial cloud cleared radiance based on MW only profiles – good or bad? • Start with better first guess (e.g., regression) ? (LaRC) • Should we consider the option to use VIIRS radiances to assist cloud clearing? • Concept demonstrated using simulated data

16. MW Retrieval Performance Improvements (1) MW Only Tskin Retrieval Error (K) Scan dependent biases in the retrieved surface temperature will affect cloud clearing accuracy

17. MW Retrieval Performance Improvements (2) Tskin Error from MW only Retrievals (K) Scan dependent biases in the retrieved surface temperature will affect cloud clearing accuracy

18. MW Retrieval Performance Improvements (3) Tskin Error from IR+MW Combined Retrievals (K) Biases removed after IR retrieval for cloud free scenes where no cloud clearing is performed. But biases persist for cloudy scenes where cloud clearing is performed using the MW retrieval results

19. MW Retrieval Performance Improvements (4) TB difference between the observed (after scan dependent bias correction) and the calculated ATMS radiances from ECMWF (blue) and the retrieved profiles (green) over clear ocean scenes Significant biases in the moisture sounding channels (18-21) Small biases in the temperature sounding channels (9-15) Surface channels (1-4, 16-17) are difficult to evaluate due to uncertainty in surface emissivity ATMS SDR bias correction LUT needs to be refined

20. MW Retrieval Performance Improvements (5) Biases in the retrieved AVMP from ATMS

21. MW Retrieval Performance Improvements (6) Inconsistent IR and MW data causing difficulty for the algorithm to converge Second stage IR chi-Square Second stage MW chi-Square Distribution (%) First stage MW chi-Square Chi-Square values

22. Enhancement to Cloud Clearing Methodology (1) • Characterizethe accuracy of cloud cleared radiances • Comparison with VIIRs? • Evaluate needof using VIIRS radiancesto improvecloud-clearing accuracy • Expected improvements – NGAS preliminary study results • Based on prelaunch Simulated test data • Assumptions: VIIRS cloud-free footprints identified and collocated with CrIMSS • Strategy: • Integrate CrIS channels to form virtual CrIS bands that correspond to VIIRS. Use these virtual channels in addition to the CrIS cloud-clearing channels to estimate  parameter • The weights of these virtual channels are tied to the radiance variability of the clear VIIRS FOVs within the CrIMSS retrieval cell (0.5%, 0.5% and 2% assumed for the LWIR, MWIR and SWIR bands respectively) • Results:

23. Enhancement to Cloud Clearing Methodology (2) Cloud cleared radiances in channels with weighting function peaking near the surface can have RMS errors several times larger than sensor errors, but could be reduced by using VIIRS clear FOV radiances in CrIMSS cloud clearing Weighting Functions

24. Land Performance Degradation Caused by Less Than Optimal Handling of Surface Emissivity AVTP Performance Statistics Over Land

25. Improve CrIMSS Performance Over Land • LaRC’s proposal should work well to address the surface emissivity issue and improve the EDR quality performance over land to the desired level • Increase frequency hinge points and adjust their locations • Update climatology • Use surface emissivity database as initial guess • UW CIMSS already developed code for similar implementation. Presentation in next meeting • Update MW emissivity climatology including EOFs • Update profile climatology including EOFs

26. Performance Degradation Over Ice Needs More Investigation AVTP Performance Statistics Over Land Significantly lower convergence rates (<~35%) than other surface types

27. Improve CrIMSS Performance over Ice • Analyze representative cases to determine the root cause of the performance degradation • Update MW emissivity climatology including EOFs • Update profile climatology including EOFs • Update IR emissivity climatology including EOFs and/or first guess

28. Summary of Potential Algorithm LUT Updates to Improve CrIMSS Performance (LaRC) • ATMS bias LUT (CrIMSS-MW-BT-BIAS-CORR-LUT) • CrIS bias LUT (CrIMSS-IR-RTM-BIAS-LUT) • Climatology LUT (CrIMSS-CLIM-LUT) • Atmospheric profile LUT • MW surface emissivity LUT • Land and ice/snow • IR surface emissivity LUT • Land and ice/snow • CrIMSS-IR-ATM-NOISE-LUT • CrIMSS-IR-NOISE-LUT • CrIMSS-MW-ATM-NOISE-LUT • CrIMSS-MW-NOISE-AMPL-LUT • CrIMSS-MW-NOISE-LUT

29. Summary of Potential Algorithm Code Updates to Improve CrIMSS Performance • Fix known coding errors (minor) • Implement improved surface IR emissivity handling • Improve cloud clearing algorithm performance

30. Summary of Potential Investigation Activities • Use VIIRS data to evaluate accuracy of cloud detection (partially done), quality of cloud cleared radiances, and feasibility of assisting cloud clearing • Derive a MW emissivity database from CrIMSS to evaluate/update current MW LUTs