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Robert Joyce RS Information Systems John Janowiak Climate Prediction Center/NCEP/NWS

* C PC Morph ing Technique. The Evaluation of a Passive Microwave-Based Satellite Rainfall Estimation Algorithm with an IR-Based Algorithm at Short time Scales. Robert Joyce RS Information Systems John Janowiak Climate Prediction Center/NCEP/NWS

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Robert Joyce RS Information Systems John Janowiak Climate Prediction Center/NCEP/NWS

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  1. *CPC Morphing Technique The Evaluation of a Passive Microwave-Based Satellite Rainfall Estimation Algorithm with an IR-Based Algorithm at Short time Scales Robert Joyce RS Information Systems John Janowiak Climate Prediction Center/NCEP/NWS Phillip Arkin ESSIC – University of Maryland Pingping Xie Climate Prediction Center/NCEP/NWS 2nd International Precipitation Working Group October 25-28, 2004

  2. Outline • 1. CMORPH concept • 2. CMORPH methodology • 3. Validation • 4. Improvement potential and future work • 5. Conclusions

  3. At present, precipitation estimates are used from various passive microwave sensor types on 8 platforms: • AMSU-B (NOAA 15, 16, 17) • SSM/I (DMSP 13, 14, 15) • TMI (TRMM – NASA/Japan) • AMSR-E (Aqua) 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.

  4. TMI rainfall estimates from NASA’s 2A12 algorithm (Kummerow et al., 1996) Goddard Profiling (GPROF) version 5, soon version 6 AMSR-E precipitation estimates from GPROF-6 rainfall algorithm run at NOAA/NESDIS/ORA. SSMI precipitation estimates from NOAA/NESDIS/ORA GPROF-6 SSMI rainfall algorithm. AMSU-B rainfall estimates from new NESDIS/ORA AMSU-B rainfall algorithm (Weng et al., 2003) Half hourly, 0.0727 lat/lon (8 km at equator) resolution arrays (separate for each sensor type) are assigned the nearest rainfall estimate within swath regions Averaging of retrieval estimates within same grid points (AMSR-E and TMI only) Anomalous microwave estimated rainfall screened with NESDIS Satellite Services Division (SSD) daily Interactive Multi-sensor Snow and Ice Mapping System (IMS) product

  5. 3-hr mosaic: good coverage but time of obs. varies by 3 hrs • PMW rainfall gridded to 8km resolution MANY thanks to NESDIS/OSDPD & R. Ferraro (NESDIS/ORA)

  6. 3-hr mosaic: good coverage but time of obs. varies by 3 hrs • PMW rainfall gridded to 8km resolution

  7. IR Data • All 5 geostationary meteorological satellites • Obtained via McIDAS • Merged into ½ hr global (60N-60S), ~ 4 km maps • Corrections for limb darkening & parallax applied Refs: Janowiak et al., Bull. Amer. Meteor. Soc., Feb 2001) Joyce et al., J. Appl. Meteor., Apr 2001 Most recent 8 days (each ½ hr period) available at: ftpprd.noaa.gov: pub/precip/global_full_res_IR

  8. Advection Vector Components Zonal Meridional 20Z March 7, 2004

  9. CMORPH RADAR Hourly Rainfall during 06UTC to 23UTC on Oct 5, 2003

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

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

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

  13. DJF 2003-2004 statistics using Australian Bureau of Meteorology 0.25 degree lat/lon daily rain gauge analyses for validation • Red line = CMORPH • Blue line = Merged PMW • Black = gauge analyses

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

  15. Hourly, 0.25 degree lat/lon CMORPH timestamp = 1 (30 minutes from nearest PMW scan) correlation with Stage II radar rainfall (top panel) • Hourly, 0.25 degree lat/lon IR-based PMW/IR combined frequency matching rainfall estimation (IRFREQ) correlation with Stage II radar rainfall (2nd from top) • CMORPH – radar rainfall correlation minus IRFREQ • Correlation pair counts

  16. Hourly, 0.25 degree lat/lon CMORPH timestamp = 2 (60 minutes from nearest PMW scan) correlation with Stage II radar rainfall (top panel) • Hourly, 0.25 degree lat/lon IRFREQ correlation with Stage II radar rainfall (2nd from top) • CMORPH – radar rainfall correlation minus IRFREQ • Correlation pair counts

  17. Hourly, 0.25 degree lat/lon CMORPH timestamp = 3 (90 minutes from nearest PMW scan) correlation with Stage II radar rainfall (top panel) • Hourly, 0.25 degree lat/lon IRFREQ correlation with Stage II radar rainfall (2nd from top) • CMORPH – radar rainfall correlation minus IRFREQ • Correlation pair counts

  18. Hourly, 0.25 degree lat/lon CMORPH timestamp = 4 (120 minutes from nearest PMW scan) correlation with Stage II radar rainfall (top panel) • Hourly, 0.25 degree lat/lon IRFREQ correlation with Stage II radar rainfall (2nd from top) • CMORPH – radar rainfall correlation minus IRFREQ • Correlation pair counts

  19. The cumulative percentage of half hourly periods sampled for an eight day period, in 30 minute increments to nearest past/future scan, instantaneous (timestamp = 0, top) • cumulative % sampled within 30 minutes of half hourly frame (timestamp <= 1, middle) • cumulative % sampled within 60 minutes of half hourly frame (timestamp <= 2)

  20. Half hourly, 0.25 degree lat/lon CMORPH correlation against withheld MWCOMB rainfall 23 June – 6 August 2004. Temporal distance of CMORPH to nearest PMW scan = 30 minutes (timestamp = 1, top) • Half hourly, 0.25 degree lat/lon IRFREQ correlation against withheld MWCOMB rainfall (timestamp = 1, 2nd from top) • CMORPH correlation minus IRFREQ (3rd from top) • # of correlation pairs (bottom)

  21. Half hourly, 0.25 degree lat/lon CMORPH correlation against withheld MWCOMB rainfall, temporal distance of CMORPH to nearest PMW scan = 60 minutes (timestamp = 2, top) • Half hourly, 0.25 degree lat/lon IRFREQ correlation against withheld MWCOMB rainfall (timestamp = 2, 2nd from top) • CMORPH correlation minus IRFREQ (3rd from top) • # of correlation pairs (bottom)

  22. Half hourly, 0.25 degree lat/lon CMORPH correlation against withheld MWCOMB rainfall, temporal distance of CMORPH to nearest PMW scan = 90 minutes (timestamp = 3, top) • Half hourly, 0.25 degree lat/lon IRFREQ correlation against withheld MWCOMB rainfall (timestamp = 3, 2nd from top) • CMORPH correlation minus IRFREQ (3rd from top) • # of correlation pairs (bottom)

  23. Half hourly, 0.25 degree lat/lon CMORPH correlation against withheld MWCOMB rainfall, temporal distance of CMORPH to nearest PMW scan = 120 minutes (timestamp = 4, top) • Half hourly, 0.25 degree lat/lon IRFREQ correlation against withheld MWCOMB rainfall (timestamp = 4, 2nd from top) • CMORPH correlation minus IRFREQ (3rd from top) • # of correlation pairs (bottom)

  24. Daily times series of correlation comparing IRFREQ, CMORPH, and CMORPH-IR 0.25 degree lat/lon rainfall estimates with same high-quality rain gauge and radar analyses over the U.S. for the 7 May – 27 July 2004 period. IRFREQ = blue lines CMORPH = green lines CMORPH-IR = red lines

  25. FUTURE WORK • Improve accuracy of CMORPH PMW rainfall vectors • Continue to investigate model winds – GMORPH • Continue investigation into the development of CMORPH – IR

  26. Satellite Estimated Rainfall Validation over United States: http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.shtml Australia: http://www.bom.gov.au/bmrc/wefor/staff/eee/SatRainVal/dailyval.html CMORPH Web:http://www.cpc.ncep.noaa.gov/products/janowiak/cmorph.html (includes data access info.) E-mail:john.janowiak@noaa.gov or robert.joyce@noaa.gov Paper: Joyce, R. J., J. E. Janowiak, P. A. Arkin and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydromet. Vol. 5, No. 3, pages 487-503. Further Information The End – Thank You

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