The collaborative historical African rainfall model: description and evaluation

Authors

  • Chris Funk,

    Corresponding author
    1. International Program, United States Geologic Survey, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
    2. Geography Department, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
    • International Program, United States Geologic Survey and Geography Department, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
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  • Joel Michaelsen,

    1. Geography Department, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
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  • Jim Verdin,

    1. International Program, United States Geologic Survey, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
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  • Guleid Artan,

    1. International Program, United States Geologic Survey, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
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  • Greg Husak,

    1. Geography Department, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
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  • Gabriel Senay,

    1. International Program, United States Geologic Survey, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
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  • Hussein Gadain,

    1. International Program, United States Geologic Survey, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
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  • Tamuka Magadazire

    1. International Program, United States Geologic Survey, University of California, Santa Barbara, 5721 Ellison Hall, UCSB, Santa Barbara, CA 93105, USA
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  • This article is a US Government work and is in the public domain in the USA.

Abstract

In Africa the variability of rainfall in space and time is high, and the general availability of historical gauge data is low. This makes many food security and hydrologic preparedness activities difficult. In order to help overcome this limitation, we have created the Collaborative Historical African Rainfall Model (CHARM). CHARM combines three sources of information: climatologically aided interpolated (CAI) rainfall grids (monthly/0.5° ), National Centers for Environmental Prediction reanalysis precipitation fields (daily/1.875° ) and orographic enhancement estimates (daily/0.1° ). The first set of weights scales the daily reanalysis precipitation fields to match the gridded CAI monthly rainfall time series. This produces data with a daily/0.5° resolution. A diagnostic model of orographic precipitation, VDELB—based on the dot-product of the surface wind V and terrain gradient (DEL) and atmospheric buoyancy B—is then used to estimate the precipitation enhancement produced by complex terrain. Although the data are produced on 0.1° grids to facilitate integration with satellite-based rainfall estimates, the ‘true’ resolution of the data will be less than this value, and varies with station density, topography, and precipitation dynamics. The CHARM is best suited, therefore, to applications that integrate rainfall or rainfall-driven model results over large regions.

The CHARM time series is compared with three independent datasets: dekadal satellite-based rainfall estimates across the continent, dekadal interpolated gauge data in Mali, and daily interpolated gauge data in western Kenya. These comparisons suggest reasonable accuracies (standard errors of about half a standard deviation) when data are aggregated to regional scales, even at daily time steps. Thus constrained, numerical weather prediction precipitation fields do a reasonable job of representing large-scale diurnal variations. Published in 2003 by John Wiley & Sons, Ltd.

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