hydrometeors retrieval(s) and other scientific issues for mirs
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Hydrometeors Retrieval(s) and Other Scientific Issues for MIRS. S.-A. Boukabara & Kevin Garrett. 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. - PowerPoint PPT PresentationTRANSCRIPT
Hydrometeors Retrieval(s) and Other Scientific Issues for MIRS
S.-A. Boukabara & Kevin Garrett
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
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
How Does MIRS work in Precipitating Conditions ?
TB
Attempt Retrieval
Convergence?Yes
Output
1st or 2nd Attempt?
1st
Turn ON Rain and Ice& Update Tuning
No
Convergence Failed
Example of Retrieval in Rainy Condition –Katrina Case (Aug 29th 2005)
TB @ 157 GHz TB @ 31 GHz
ScatteringAbsorption
Note the lesser contrast over landConclusion: Both Rain and Ice present
Results of MIRS (Convergence)Before After Implementing 2nd Attempt
Significant improvement in convergence
Number of Iterations Number of Iterations
ChiSq ChiSq
• Graupel-size Ice and Rain are turned ON in second attempt.
• Other parameters are also tuned (#EOFs, RTM uncertainty scaling factors, etc).
Results of MIRS (Hydrometeors retrieval -GWP)
GWP GWP
No convergence was reached before
Before After
RWPRWP
A physically Consistent field
Demonstration of MIRS High-Resolution Capabilities & Assessment
MSPPS RR
MIRS RWP @ MHS Resolution
MIRS GWP @ MHS Resolution
High spatial correlation MSPPS / MIRS
Retrieval in MSPPS flags (undetermined)
Coastal transition smooth without any particular treatment
Results of MIRS (Non-Precip Parameters)
Before
After
A lot more convergence in precipitating conditions with plausible TPW values
A sharp sea/land contrast:Needs more investigation (and fix)
Daily Process
• Effect of 2nd Attempt on spatial coverage
• Retrieval of RWP globally
• Retrieval of GWP (IWP for graupel) globally
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
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
Atmospheric Covariance
NOAA-88
ECMWF
MM5
T Q CLW Rain Ice
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.
Surface Covariance (over ocean)
NOAA-88
ECMWF
MM5
Emissivity/Tskin
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.
Bias Fine Tuning (2/2)Histogram-Adjustment Bias Computation
Statistical Bias Computation