1 / 14

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

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

pilar
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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.

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

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

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

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

  6. 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?

  7. 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.

  8. 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

  9. 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

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

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

  12. 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

  13. 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.

  14. 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

More Related