A Global Kalman Filtered CMORPH using TRMM to Blend Satellite Rainfall Robert Joyce NOAA/NCEP/CPC Wyle Information Systems Pingping Xie NOAA/NCEP/CPC John Janowiak University of Maryland NASA GSFC 16 Apr, 2009, Greenbelt MD

1. Current CMORPH processing algorithm 2. Regional Kalman filter CMORPH using radar rainfall to evaluate/weight satellite rainfall components 3. Rain rate damping in Kalman filtered CMORPH 4. Global Kalman filter CMORPH using withheld TRMM TMI to evaluate/weight satellite rainfall components 5. Conclusions and Future plans Outline

1. Current CMORPH processing algorithm 2. Regional Kalman filter CMORPH using radar rainfall to evaluate/weight satellite rainfall components 3. Rain rate damping in Kalman filtered CMORPH 4. Global Kalman filter CMORPH using withheld TRMM TMI to evaluate/weight satellite rainfall components 5. Conclusions and Future plans Outline

Correlation of 0.25 degree lat/lon propagated PMW rainfall w/ Stage II radar rainfall

σ 2 z2 σ 2 z1 Y = ___________ z1 + _________ z2 σ 2 z1 + σ 2 z2 σ 2 z1 + σ 2 z2 KALMAN FILTER example with inverse error weighting Where: Y = new estimate z1 = estimate from previous scan z2= estimate from future scan * σ 2 = error variance for z1 & z2 * Alternatively, could use correlation coef. or explained variance

1. Current CMORPH processing algorithm 2. Regional Kalman filter CMORPH using radar rainfall to evaluate/weight satellite rainfall components 3. Rain rate damping in Kalman filtered CMORPH 4. Global Kalman filter CMORPH using withheld TRMM TMI to evaluate/weight satellite rainfall components 5. Conclusions and Future plans Outline

IPWG U.S. daily 0.25 degree rainfall validation Kalman filter CMORPH no PDF adjustment Kalman filter CMORPH PDF adjusted NEXRAD Stage II radar rainfall

Cumulative rain rate distribution of Kalman filtered rainfall, PDF adjusted Kalman filtered, and radar rainfall.

1. Current CMORPH processing algorithm 2. Regional Kalman filter CMORPH using radar rainfall to evaluate/weight satellite rainfall components 3. Rain rate damping in Kalman filtered CMORPH 4. Global Kalman filter CMORPH using withheld TRMM TMI to evaluate/weight satellite rainfall components 5. Conclusions and Future plans Outline

Correlation of PMW Precip from most recent scan with TMI precip. Simultaneous (within 30 min.) 30 min. 60 min. 90 min. 120 min. backward forward

Simultaneous (within 30 min.) 30 min. 60 min. 90 min. 120 min. backward forward Similar to previous, but Correlation Difference: TMI & (SSMI/AMSRE)– TMI & IRFREQ

Correlation of forward in time propagated AMSU rainfall with TMI ( left panels). Differences between forward propagated HQ PMW and AMSU rainfall with TRMM TMI (right panels)

1. For convective-dominated rainfall regimes, the inclusion of the IR derived estimates in the regional Kalman filtering process in substantially improves the combined satellite rainfall product, most substantial contributions are in largest gaps between PMW scans. 2. Use of instantaneous rain rate PDFs eliminate the rain rate damping and increase of spatial coverage created by the filtering process. 3.Surface type and latitude region provide substantial variability in the statistical nature of propagated PMW rainfall and IR-based rainfall estimates relative to TRMM TMI. 4. Continue the Kalman filter study for use with TRMM TMI (GPM in the future) for regional/seasonal depiction of skill/error variance of each sensor/algorithm. Need to look at the sensor type weighting variability as a function of season. Conclusions and Future Work