GPCP Pentad Precipitation Analyses: An Experimental Dataset Based on Gauge Observations and Satellite Estimates

• Published in: Journal of climate 2003-07, Vol.16 (13), p.2197-2214
• Main Authors: Xie, Pingping, Janowiak, John E., Arkin, Phillip A., Adler, Robert, Gruber, Arnold, Ferraro, Ralph, Huffman, George J., Curtis, Scott
• Format: Article
• Language: English
• Subjects: Analytical estimating; Atolls; Climate prediction; Climatology; Datasets; Distribution patterns; Earth, ocean, space; Exact sciences and technology; External geophysics; Global climate models; Global telecommunication system; Land; Meteorological satellites; Meteorology; Meteors; Oceans; Precipitation; Rain; Spatial analysis; Spatial distribution; Water in the atmosphere (humidity, clouds, evaporation, precipitation)
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/2769.1

As part of the Global Precipitation Climatology Project (GPCP), analyses of pentad precipitation have been constructed on a 2.5° latitude–longitude grid over the globe for a 23-yr period from 1979 to 2001 by adjusting the pentad Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) against the monthly GPCP-merged analyses. This adjustment is essential because the precipitation magnitude in the pentad CMAP is not consistent with that in the monthly CMAP or monthly GPCP datasets primarily due to the differences in the input data sources and merging algorithms, causing problems in applications where joint use of the pentad and monthly datasets is necessary. First, pentad CMAP-merged analyses are created by merging several kinds of individual data sources including gauge-based analyses of pentad precipitation, and estimates inferred from satellite observations. The pentad CMAP dataset is then adjusted by the monthly GPCP-merged analyses so that the adjusted pentad analyses match the monthly GPCP in magnitude while the high-frequency components in the pentad CMAP are retained. The adjusted analyses, called the GPCP-merged analyses of pentad precipitation, are compared to several gauge-based datasets. The results show that the pentad GPCP analyses reproduced spatial distribution patterns of total precipitation and temporal variations of submonthly scales with relatively high quality especially over land. Simple applications of the 23-yr dataset demonstrate that it is useful in monitoring and diagnosing intraseasonal variability. The Pentad GPCP has been accepted by the GPCP as one of its official products and is being updated on a quasi-real-time basis.