Hydrometeors Retrieval(s) and Other Scientific Issues for MIRS

1. Hydrometeors Retrieval(s) and Other Scientific Issues for MIRS S.-A. Boukabara & Kevin Garrett

2. Progress • No degradation of clear/cloudy cases performances is top priority • Retrieval of hydrometeors and other parameters when rainy/icy, is done after first attempt is performed. • In second attempt, turn ON the retrieval of ice and rain profiles • A 2nd attempt mechanism for the retrieval (in case non-convergence occurs) was implemented. In this, the following parameters can be changed: • Covariance matrix • RTM/Instrument Error matrix • EOF decomposition set up • Which parameters to retrieve • Retrieval tuning and covariance tuning • First guess usage • Levenberg-Marquardt non-linearity term • Channels to use • Bias application approach (by channel, by surface type) • Scaling of RTM uncertainty • Number of iterations • Maximum relative humidity allowed

3. When there is rain/ice • Before: • Non convergence • Flagged as invalid (by ChiSquare) • Now: • Converge • Retrieve hydrometeors parameters (RWP and IWP) through retrieval of profiles (using EOF decomposition) • Retrieve T, Q, SkinT, Emiss as well. • In progress: • Qualitative validation & Assessment • Needs to be done: • Validation in these cases by direct comparison

4. How Does MIRS work in Precipitating Conditions ? TB Attempt Retrieval Turn ON Rain and Ice & Update Tuning 1st 1st or 2nd Attempt? Convergence? Yes No Output Convergence Failed

5. Note the lesser contrast over land Scattering Absorption Example of Retrieval in Rainy Condition –Katrina Case (Aug 29th 2005) Conclusion: Both Rain and Ice present TB @ 157 GHz TB @ 31 GHz

6. Results of MIRS (Convergence) After Implementing 2nd Attempt Before Number of Iterations Number of Iterations Significant improvement in convergence ChiSq ChiSq

7. A physically Consistent field Before After GWP GWP RWP RWP No convergence was reached before Results of MIRS (Hydrometeors retrieval -GWP) • Graupel-size Ice and Rain are turned ON in second attempt. • Other parameters are also tuned (#EOFs, RTM uncertainty scaling factors, etc).

8. High spatial correlation MSPPS / MIRS Coastal transition smooth without any particular treatment Retrieval in MSPPS flags (undetermined) Demonstration of MIRS High-Resolution Capabilities & Assessment MSPPS RR MIRS RWP @ MHS Resolution MIRS GWP @ MHS Resolution

9. Results of MIRS (Non-Precip Parameters) Before A lot more convergence in precipitating conditions with plausible TPW values After A sharp sea/land contrast: Needs more investigation (and fix)

10. Daily Process • Effect of 2nd Attempt on spatial coverage • Retrieval of RWP globally • Retrieval of GWP (IWP for graupel) globally

11. Conclusion (s) • Mechanism has been implemented to: • Retrieve hydrometeors • Retrieve non-precip parameters in rainy/icy conditions • Adapt parameters for the second retrieval • Keep performances in clear/cloudy skies the same • Qualitatively, the system is doing the right thing • Improvements are needed: • Improve covariance matrix (correlation) • Make sure there is no land/ocean sharp contrast

12. Progress in the Covariance Matrix Fine Tuning • A new covariance is being fine-tuned, tested • Based on ECMWF & MM5 • Correlations between T,Q,Clw,Ice are being implemented and tested

13. T Q CLW Rain Ice Atmospheric Covariance NOAA-88 ECMWF MM5

14. Combined Covariance (clear/cloudy) Obtained by combining ECMWF-based covariance with MM5-based correlations for rain (correlations with Ice, Temperature, Humidity, etc) This assures that T, Q, CLW, Rain and Ice Retrievals are physically consistent, on average.

15. Surface Covariance (over ocean) Emissivity/Tskin NOAA-88 ECMWF MM5

16. Bias Fine Tuning To be less sensitive to cloud and coastal contaminations, bias is computed by adjusting the peak of the TB difference distribution histogram.

17. Bias Fine Tuning (2/2) Histogram-Adjustment Bias Computation Statistical Bias Computation