“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 samplingPMW: Good rainfall estimate – poor sampling

Satellite - CPC gauge analysisSatellite - CPC gauge analysis

Merged PMW – only & RadarMerged PMW – only & Radar

Difference from gauge analysisDifference from gauge analysis

Satellite - CPC gauge analysisSatellite - CPC gauge analysis

CMORPH & RadarCMORPH & Radar

Difference from gauge analysisDifference from gauge analysis

Comparison with U.S. Gauge Analyses

RadarCMORPHRADAR

Merged PMW

CPC gauge analysis ( Aug 2003)CPC gauge analysis ( Aug 2003)

CMORPH analysis ( Aug 2003)CMORPH analysis ( Aug 2003)

CMORPH with evap. adjustmentCMORPH with evap. adjustment

Limitations

• Present estimation algorithms cannot retrieve precip. over snow orice 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 (1/4o 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 radiationfrom 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 transportprecip. features is < error in using IR to estimate precip.

IR: Poor rainfall estimate – great samplingPMW: 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 anestimate when PMW data are unavailable (NRL, NASA,UC-Irvine)