The collaborative historical African rainfall model: Description and evaluation

被引:38
作者
Funk, C
Michaelsen, J
Verdin, J
Artan, G
Husak, G
Senay, G
Gadain, H
Magadazire, T
机构
[1] Univ Calif Santa Barbara, Int Program, US Geol Survey, Santa Barbara, CA 93105 USA
[2] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93105 USA
关键词
Africa; precipitation; rainfall; time series; orographic; climatology; interpolation; food security;
D O I
10.1002/joc.866
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
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/0.5degrees), National Centers for Environmental Prediction reanalysis precipitation fields (daily/1.875degrees) and orographic enhancement estimates (daily/0.1degrees). 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/0.5degrees 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 0.1degrees 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.
引用
收藏
页码:47 / 66
页数:20
相关论文
共 83 条
[1]  
[Anonymous], 1999, STAT FOOD INS WORLD
[2]  
ARAKAWA A, 1974, J ATMOS SCI, V31, P674, DOI 10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO
[3]  
2
[4]  
ARTAN G, 2001, 5 INT WORKSH APPL RE
[5]  
BAILEY T. C., 1995, INTERACTIVE SPATIAL
[6]  
Barnston AG, 1996, WEATHER FORECAST, V11, P506, DOI 10.1175/1520-0434(1996)011<0506:LLFOSP>2.0.CO
[7]  
2
[8]  
BERGERON T, 1960, PHYSICS PRECIPITATIO
[9]   FORECASTING ZIMBABWEAN MAIZE YIELD USING EASTERN EQUATORIAL PACIFIC SEA-SURFACE TEMPERATURE [J].
CANE, MA ;
ESHEL, G ;
BUCKLAND, RW .
NATURE, 1994, 370 (6486) :204-205
[10]   Isolation of amelogenin-positive ameloblasts from rat mandibular incisor enamel organs by flow cytometry and fluorescence activated cell sorting [J].
Chen, WY ;
Lu, LW ;
McDonald, K ;
Osmond, DG ;
Smith, CE .
CONNECTIVE TISSUE RESEARCH, 1998, 38 (1-4) :9-+