“CMORPH” is a method that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave observations. Use IR only as a transport vehicle. The underlying assumption is that errors in using IR to transport precip. features is < error in using IR to estimate precip. IR: Poor rainfall estimate – great sampling PMW: Good rainfall estimate – poor sampling

Satellite - CPC gauge analysis Merged PMW – only & Radar Difference from gauge analysis

Satellite - CPC gauge analysis CMORPH & Radar Difference from gauge analysis

Comparison with U.S. Gauge Analyses Radar CMORPH RADAR Merged PMW

CPC gauge analysis ( Aug 2003) CMORPH analysis ( Aug 2003) CMORPH with evap. adjustment

Limitations Present estimation algorithms cannot retrieve precip. over snow or ice covered surfaces - New algorithms being developed (Liu, Ferraro) Will not presently detect precip. that develops, matures & decays between microwave scans Data Latency: ~ 18 hours past real-time Limits on how far back data can be processed … early 1990’s?

Utility -The spatial & temporal characteristics of CMORPH (1/4 o lat/lon & half-hourly) make it a good candidate for global flood monitoring & mitigation - Presently used for USAID/FEWS for crop monitoring/forecasting in Africa, SE Asia, Central America - Presently used for model precipitation assimilation in “regional reanalysis” and in the NCEP & NASA land data assimilation systems - Because CMORPH merges products and is not an estimation algorithm it is flexible and can incorporate estimates from new algorithms based on any sensor - The accuracy of CMORPH can be enhanced substantially with additional satellite observations like that expected from NASA’s Global Precipitation Mission.

Refine & implement evaporation adjustment Integrate CMORPH with IR-based estimates Investigate use of model winds -- tropics Investigate orographic precipitation enhancement Examine global diurnal cycle of precipitation Annual, Seasonal, Interannual variations? Assess NWP model performance PRESENT & FUTURE WORK

Refine & implement evaporation adjustment Integrate CMORPH with IR-based estimates Investigate use of model winds – extend back to early 1990’s? Investigate orographic precipitation enhancement Examine global diurnal cycle of precipitation Annual, Seasonal, Interannual variations? Assess NWP model performance PRESENT & FUTURE WORK

Surface Infrared - Poor precip. estimate - Great sampling (global, 1/2 hr, 4 km)

Surface Passive Microwave “Emission” Detects thermal emission from hydrometeors - most physically direct - polar platform only - over ocean only (20-50GHz)

Surface Freezing Level Passive Microwave “Scattering” (PMW) Upwelling radiation from Earth’s surface Upwelling radiation is scattered by “large” ice particles in the tops of convective clouds - land & ocean (85 GHz) - polar platform only

“CMORPH” is not a precipitation estimation technique but rather a method that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave observations. uses IR only as a transport vehicle. Underlying assumption is that errors in using IR to transport precip. features is < error in using IR to estimate precip. At present, precipitation estimates are used from 3 passive microwave sensor types on 7 platforms: AMSU-B (NOAA 15, 16, 17) SSM/I (DMSP 13, 14, 15) TMI (TRMM – NASA/Japan) AMSR/E (Aqua – NASA EOS) … soon NOAA/NESDIS (Ferraro et al)

“CMORPH” uses IR only as a transport vehicle. Underlying assumption is that errors in using IR to transport precip. features is < error in using IR to estimate precip. IR: Poor rainfall estimate – great sampling PMW: Good rainfall estimate – poor sampling Use together to meld the strengths each has to offer Several existing methods exist that use IR data to make an estimate when PMW data are unavailable (NRL, NASA, UC-Irvine)