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CrIMSS EDR Performance Assessment and Tuning. Alex Foo, Xialin Ma and Degui Gu Sept 11, 2012. Overview. Updates to CrIS sensor noise LUT and CrIS RTM noise LUT These LUTs control the inversion of radiances into geophysical state parameters

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crimss edr performance assessment and tuning

CrIMSS EDR Performance Assessment and Tuning

Alex Foo, Xialin Ma and Degui Gu

Sept 11, 2012

  • 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
    • Handling of RTM error and sensor error
  • Tuning QC thresholds
  • Assessed impact on CrIMSS EDR performance
cris sensor error lut evaluation
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.

cris rtm error lut evaluation
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.

tuning quality control
Tuning Quality Control
  • Second-stage (IR and MW combined) retrievals are quality controlled based on chi-square values which are essentially the averaged radiance residuals normalized by the combined sensor and RTM noise errors
  • Current chi-Square thresholds are set to 1 for IR and 2 for MW
  • If a retrieval doesn’t pass the chi-square test, the second-stage retrieval is tossed out and the first stage (MW only) retrieval is reported
  • Examination of EDR quality vs. chi-square values indicated that the current thresholds are set too tight and, as a result, many good IR+MW retrievals are thrown away
  • The trade study was performed based on the accuracy of the retrieved Tskin. As the threshold value increases, accuracy of the MW+IR combined retrievals decreases and eventually becomes lower than the MW only. The crossover point is the threshold when the IR retrievals are no longer good and should be replaced with MW
biases in retrieved surface skin temperature could indicate needs for further mw tuning
Biases in Retrieved Surface Skin Temperature Could Indicate Needs for Further MW Tuning
  • In the study it is also noticed, as shown in the plots on the previous chart, persistent negative biases in the MW-only retrieved surface skin temperatures
  • No biases for IR retrieved surface skin temperatures for cloud free retrievals when MW has little impact
  • Negative biases are observed for cloudy IR retrievals when MW is used in cloud clearing and MW has more weight on the retrievals
  • Further investigation and tuning if necessary to remove residual biases in MW data
cris sensor and rtm error lut update impact on edr performance
CrIS Sensor and RTM Error LUT Update Impact on EDR Performance
  • Second stage MW chi-square threshold set to 5 for the “best” balance between yield and quality
  • Limited to warm ocean retrievals and temperature profile EDRs only
  • Observed significant improvement over the MX6.3 baseline performance in both yield and quality
    • In average, yield improved from 27-30% (MX6.3) to 46-47% (proposed for MX7)
    • Accuracy (standard deviation) improved ~0.2K in lower troposphere
improved edr yield and quality with lut updates and qc tuning
Improved EDR Yield and Quality with LUT Updates and QC Tuning

Night Ocean Scenes

Daytime Ocean Scenes

crimss provided superior sounding capability
CrIMSS Provided Superior Sounding Capability

AVTP Quality for Daytime Cloud Free Ocean Scenes

Lower yield due to cloud leakage and MW tuning?

next step
Next Step
  • Fine-tuning ATMS SDR bias correction (surface and moisture channels)
  • Evaluating ATMS sensor error LUT
  • Evaluating ATMS RTM error LUT
  • Evaluating ATMS remap noise reduction LUT
  • Refine EDR quality control logic
  • Supporting MX7 update delivery