Comparison of the GPCP and CMAP Merged Gauge–Satellite Monthly Precipitation Products for the Period 1979–2001

Abstract

Abstract The two monthly precipitation products of the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) are compared on a 23-yr period, January 1979–December 2001. For the long-term mean, major precipitation patterns are clearly demonstrated by both products, but there are differences in the pattern magnitudes. In the tropical ocean the CMAP is higher than the GPCP, but this is reversed in the high-latitude ocean. The GPCP–CMAP spatial correlation is generally higher over land than over the ocean. The correlation between the global mean oceanic GPCP and CMAP is significantly low. It is very likely because the input data of the two products have much less in common over the ocean; in particular, the use of atoll data by the CMAP is disputable. The decreasing trend in the CMAP oceanic precipitation is found to be an artifact of input data change and atoll sampling error. In general, overocean precipitation represented by the GPCP is more reasonable; over land the two products are close, but different merging algorithms between the GPCP and the CMAP can sometimes produce substantial discrepancy in sensitive areas such as equatorial West Africa. EOF analysis shows that the GPCP and the CMAP are similar in 6 out of the first 10 modes, and the first 2 leading modes (ENSO patterns) of the GPCP are nearly identical to their counterparts of the CMAP. Input data changes [e.g., January 1986 for Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI), July 1987 for Special Sensor Microwave Imager (SSM/I), May 1994 for Microwave Sounding Unit (MSU), and January 1996 for atolls] have implications in the behavior of the two datasets. Several abrupt changes identified in the statistics of the two datasets including the changes in overocean precipitation, spatial correlation time series, and some of the EOF principal components, can be related to one or more input data changes.