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Frank Richards and Phil Arkin

Abstract

The effect of averaging over various spatial scales (0.5–2.5° latitude) and times (1–24 h) on the relationship between the mean fraction of the averaging area covered by clouds colder than various IR equivalent blackbody temperature thresholds and the precipitation over that area is examined. While a linear relationship between fractional coverage and rainfall amount shows considerable scatter at the smallest scale, there is much better correspondence at the larger scales, with linear correlation coefficients often exceeding 0.8. Large-scale rainfall estimates based on linear regression coefficients detect the timing and magnitudes of major rainfall events during GATE. For scales on the order of 2–3° of latitude, estimates based on a linear model are comparable to those found by Stout et al. (1979) and Griffith et al. (1980) for the GATE area. This simple model appears to be limited to scales considerably larger than the convective scale. Averaging over these scales minimizes the effects of the spatial and temporal details of the convective fields. The linear model can be interpreted as the application of an effective mean rainfall rate to the entire precipitating cloudy area. While such an approach does not provide detailed resolution of the field of precipitation, estimation procedures based on linear models may be useful for large-scale budget studies and certain hydrologic and agricultural applications.

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Xungang Yin, Arnold Gruber, and Phil Arkin

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.

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Lennart Bengtsson, Phil Arkin, Paul Berrisford, Philippe Bougeault, Chris K. Folland, Chris Gordon, Keith Haines, Kevin I. Hodges, Phil Jones, Per Kallberg, Nick Rayner, Adrian J. Simmons, Detlef Stammer, Peter W. Thorne, Sakari Uppala, and Russell S. Vose
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