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John E. Janowiak, Robert J. Joyce, and Yelena Yarosh

A system has been developed and implemented that merges pixel resolution (~4 km) infrared (IR) satellite data from all available geostationary meteorological satellites into a global (60°N–60°S) product. The resulting research-quality, nearly seamless global array of information is made possible by recent work by Joyce et al., who developed a technique to correct IR temperatures at targets far from satellite nadir. At such locations, IR temperatures are colder than if identical features were measured at a target near satellite nadir. This correction procedure yields a dataset that is considerably more amenable to quantitative manipulation than if the data from the individual satellites were merely spliced together.

Several unique features of this product exist. First, the data from individual geostationary satellites have been merged to form nearly seamless maps after correcting the IR brightness temperatures for viewing angle effects. Second, with the availability of IR data from the Meteosat-5 satellite (currently positioned at a subsatellite longitude of 63°E), globally complete (60°N–60°S) fields can be produced. Third, the data have been transformed from the native satellite projection of each individual geostationary satellite and have been remapped to a uniform latitude/longitude grid. Fourth, globally merged datasets of full resolution IR brightness temperature have been produced routinely every half hour since November 1998. Fifth, seven days of globally merged, half-hourly data are available on a rotating archive that is maintained by the Climate Prediction Center Web page (http://www.cpc.ncep.noaa.gov/products/global_precip/html/web.html). Unfortunately, international agreement prevents us from distributing Meteosat data within three days of real time, so the data availability is delayed appropriately. Finally, these data are permanently saved at the National Climatic Data Center in Asheville, North Carolina, beginning with data in mid-September of 1999.

In this paper, the authors briefly describe the merging methodology and describe key aspects of the merged product. Present and potential applications of this dataset are also discussed. Applications include near-real time global disaster monitoring and mitigation and assimilation of these data into numerical weather prediction models and research, among others.

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Pingping Xie, Robert Joyce, Shaorong Wu, Soo-Hyun Yoo, Yelena Yarosh, Fengying Sun, and Roger Lin

Abstract

The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

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