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CrIMSS EDR Performance Assessment and Tuning (Preliminary Results)

CrIMSS EDR Performance Assessment and Tuning (Preliminary Results). Alex Foo, Xialin Ma and Degui Gu August 28, 2012. Overview. NGAS independent assessment of MX6.3 LUT updates CrIS RTM bias correction coefficient LUT ATMS SDR bias correction coefficient LUT

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CrIMSS EDR Performance Assessment and Tuning (Preliminary Results)

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  1. CrIMSS EDR Performance Assessment and Tuning (Preliminary Results) Alex Foo, Xialin Ma and Degui Gu August 28, 2012

  2. Overview • NGAS independent assessment of MX6.3 LUT updates • CrIS RTM bias correction coefficient LUT • ATMS SDR bias correction coefficient LUT • Updates to CrIS sensor noise LUT and CrIS RTM noise LUT • These LUTs control the inversion of radiances into geophysical state parameters • Sensor noises can be estimated from operational data • RTM noises can be estimated from analysis of CrIMSS retrieval radiance residuals • Coding errors fixes and improvements

  3. CrIS RTM Bias Correction LUT Assessment • Analysis based on 5/15/2012 Golden Day dataset • CrIS SDRs are based on IDPS product (verified with the re-processed SDRs on G-ADA) • Simulated radiances computed using the latest OSS forward model (same as the CrIMSS code) based on STAR’s ECMWF matchup dataset • MX6.3 update was based on LaRC estimates • Data filtering • Night time only • Warm ocean only (Tskin>=274K) • Clear only • Uniform FORs identified by CrIMSS EDR algorithm • Retrieved surface skin temperature error <0.5K • Surface skin temperature and “observed” surface skin temperature difference < 0.5K • The relationship between radiance and surface skin temperature is derived from the co-located simulated data • IR retrieval converged

  4. Clear FORs Identification Cloud Leakage? ECMWF Surface Skin Temperature (K)

  5. Comparison of NGAS and LaRC CrIS RTM Bias Correction Coefficients Comparison of CrIS RTM Bias correction coefficients Excellent agreement overall Slightly larger differences in SW band Correction Coefficients Differences

  6. Assessment of ATMS Bias Correction LUT • ATMS SDRs are bias corrected using LaRC LUTs • The corrected SDRs are compared to the simulated and retrieved SDRs for the selected clear scenes (same subset as in CrIS LUT assessment) • Residuals could indicate incomplete bias correction to the ATMS SDRs

  7. Residual ATMS SDR Biases TB difference between the observed (after scan dependent bias correction) and the calculated ATMS radiances from ECMWF (blue) and retrieved profiles (green) 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

  8. MW and IR Retrieved Moisture Profiles Biases in the retrieved AVMP from ATMS

  9. CrIS Sensor and RTM Noise LUT Assessment • At-launch values of the CrIS sensor noise LUT are conservative and based on sensor specs • Causing leakage in cloud detection and many cloudy scenes mis-classified as clear • Estimated CrIS sensor noise by analyzing observed radiances from uniform scenes • Nadir FORs only to minimize scan angle effects • Correlation analysis to remove residual scene variability • Estimated CrIS RTM noise by analyzing radiance retrieval residuals • Include so-called environmental noise due to sub-FOR scene variability effects • Update CrIS sensor noise LUT and RMT noise LUT • Sensor noise LUT used in cloud detection and determination • RTM noise LUT combined with sensor noise used in the inversion of state parameters and evaluation of retrieval convergence

  10. Radiance Variation of Uniform Scenes Radiance variation for uniform scenes (with most scene dependent components removed) SW ringing effects

  11. CrIS Sensor Error LUT Evaluation CrIS sensor error estimated from uniform scenes Channels used in cloud detection Current Ops LUT Post-launch Estimate (preliminary) Updating the CrIS sensor error LUT will improve cloud detection performance, but need to be careful not to force the algorithm to fit the noise.

  12. Retrieved Radiance Residuals of Selected Scenes Difference between the observed radiances and the retrieved radiances

  13. CrIS RTM Error LUT Evaluation CrIS RTM error estimated from radiance residuals Current Ops LUT Post-launch Estimate (preliminary) “RTM error” is designated to account for the non-sensor cause of radiance mismatch between the observed and the calculated. This LUT can be tuned to improve both EDR quality and yield.

  14. Coding Errors in the CrIMSS EDR Algorithm • Indexing error of N-LTE affected channels • Index didn’t account for ATMS channels • Handling of atmospheric noise (RTM random error) • Atmospheric noise and sensor noise need to be combined in evaluating the radiance residuals (Obs –Cal)

  15. Comparison of MW and MW+IR Retrieval Convergence Rates

  16. Next Steps • Testing the new LUTs on G-ADA to evaluate performance impact • Evaluating convergence criteria • Current thresholds: IR chi-square <= 1 and MW chi-square <= 2 • Implementing code changes to improve EDR performance • Fixing coding errors • Enhancement proposed by the team • Evaluating ATMS sensor noise, RTM noise, noise amplification factor LUTs

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