The Evaluation of a Passive Microwave-Based Satellite Rainfall Estimation Algorithm with an IR- Based Algorithm at Short time ScalesRobert Joyce RS Information Systems John Janowiak Climate Prediction Center/NCEP/NWS Phillip Arkin ESSIC – University of Maryland Pingping Xie Climate Prediction Center/NCEP/NWS 2 nd International Precipitation Working Group October 25-28, 2004 *C PC Morph ing Technique
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The Evaluation of a Passive Microwave-Based Satellite Rainfall Estimation Algorithm with an IR-Based Algorithm at Short time Scales Robert Joyce RS Information.
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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
“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.
At present, precipitation estimates are used from various passive microwave sensor types on 8 platforms:
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
• DJF 2003-2004 statistics using DJF 2003-2004 statistics using Australian Bureau of Australian Bureau of Meteorology 0.25 degree Meteorology 0.25 degree lat/lon daily rain gauge lat/lon daily rain gauge analyses for validationanalyses for validation
• Hourly, 0.25 degree lat/lon Hourly, 0.25 degree lat/lon CMORPH timestamp = 2 (60 CMORPH timestamp = 2 (60 minutes from nearest PMW scan) minutes from nearest PMW scan) correlation with Stage II radar correlation with Stage II radar rainfall (top panel)rainfall (top panel)
• Hourly, 0.25 degree lat/lon Hourly, 0.25 degree lat/lon IRFREQ correlation with Stage II IRFREQ correlation with Stage II radar rainfall (2radar rainfall (2ndnd from top) from top)
• CMORPH – radar rainfall CMORPH – radar rainfall correlation minus IRFREQ correlation minus IRFREQ
• Hourly, 0.25 degree lat/lon Hourly, 0.25 degree lat/lon CMORPH timestamp = 3 (90 CMORPH timestamp = 3 (90 minutes from nearest PMW scan) minutes from nearest PMW scan) correlation with Stage II radar correlation with Stage II radar rainfall (top panel)rainfall (top panel)
• Hourly, 0.25 degree lat/lon Hourly, 0.25 degree lat/lon IRFREQ correlation with Stage II IRFREQ correlation with Stage II radar rainfall (2radar rainfall (2ndnd from top) from top)
• CMORPH – radar rainfall CMORPH – radar rainfall correlation minus IRFREQ correlation minus IRFREQ
• Hourly, 0.25 degree lat/lon Hourly, 0.25 degree lat/lon CMORPH timestamp = 4 (120 CMORPH timestamp = 4 (120 minutes from nearest PMW scan) minutes from nearest PMW scan) correlation with Stage II radar correlation with Stage II radar rainfall (top panel)rainfall (top panel)
• Hourly, 0.25 degree lat/lon Hourly, 0.25 degree lat/lon IRFREQ correlation with Stage II IRFREQ correlation with Stage II radar rainfall (2radar rainfall (2ndnd from top) from top)
• CMORPH – radar rainfall CMORPH – radar rainfall correlation minus IRFREQ correlation minus IRFREQ
• The cumulative percentage of half The cumulative percentage of half hourly periods sampled for an hourly periods sampled for an eight day period, in 30 minute eight day period, in 30 minute increments to nearest past/future increments to nearest past/future scan, instantaneous (timestamp = scan, instantaneous (timestamp = 0, top) 0, top)
• cumulative % sampled within 30 cumulative % sampled within 30 minutes of half hourly frame minutes of half hourly frame (timestamp <= 1, middle)(timestamp <= 1, middle)
• cumulative % sampled within 60 cumulative % sampled within 60 minutes of half hourly frame minutes of half hourly frame (timestamp <= 2)(timestamp <= 2)
Daily times series of correlation Daily times series of correlation comparing IRFREQ, CMORPH, comparing IRFREQ, CMORPH, and CMORPH-IR 0.25 degree and CMORPH-IR 0.25 degree lat/lon rainfall estimates with lat/lon rainfall estimates with same high-quality rain gauge and same high-quality rain gauge and radar analyses over the U.S. for radar analyses over the U.S. for the 7 May – 27 July 2004 period.the 7 May – 27 July 2004 period.
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 CMORPHWeb: http://www.cpc.ncep.noaa.gov/products/janowiak/cmorph.html (includes data access info.)
Paper: Joyce, R. J., J. E. Janowiak, P. A. Arkin and P. Xie, 2004: CMORPH: A method that produces global precipitationestimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydromet. Vol. 5, No. 3,