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