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IR : Poor rainfall estimate – great sampling PMW : Good rainfall estimate – poor sampling

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Presentation Transcript
slide1
IR: Poor rainfall estimate – great sampling

PMW: Good rainfall estimate – poor sampling

“CMORPH” is a method that creates spatially & temporally complete information using existingprecipitationproducts 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.

slide2
Satellite - CPC gauge analysis

Merged PMW – only & Radar

Difference from gauge analysis

slide3
Satellite - CPC gauge analysis

CMORPH & Radar

Difference from gauge analysis

slide4
Radar

CMORPH

RADAR

Merged PMW

Comparison with U.S. Gauge Analyses

slide5
CPC gauge analysis ( Aug 2003)

CMORPH analysis ( Aug 2003)

CMORPH with evap. adjustment

slide6
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?
slide7
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.
slide8
PRESENT & FUTURE WORK
  • 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
slide9
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
slide10
- Poor precip. estimate

- Great sampling

(global, 1/2 hr, 4 km)

Surface

Infrared

slide11
most physically direct
  • polar platform only
  • - over ocean only (20-50GHz)

Passive Microwave “Emission”

Detects thermal emission

from hydrometeors

Surface

slide12
land & ocean (85 GHz)
  • - polar platform only

Upwelling radiation is scattered by “large” ice

particles in the tops of convective clouds

Surface

Upwelling radiation

from Earth’s surface

Passive Microwave “Scattering”

(PMW)

Freezing Level

slide13
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” is not a precipitation estimation technique but rather a method that creates spatially & temporally complete information using existingprecipitationproducts 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.

slide14
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)

“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

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