NOAA Atlas 14 Rainfall Depths, NRCS Rainfall Distributions ...
IR : Poor rainfall estimate – great sampling PMW : Good rainfall estimate – poor sampling
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Transcript of IR : Poor rainfall estimate – great sampling PMW : Good rainfall estimate – poor sampling
“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
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)