Humanitarian and development agencies face difficult decisions about whereand how to prioritise climate risk reduction measures. These tasks areespecially challenging in regions with few meteorological stations, complextopography and extreme weather events. In this study, we blend surfacemeteorological observations, remotely sensed (TRMM and NDVI) data,physiographic indices, and regression techniques to produce gridded maps ofannual mean precipitation and temperature, as well as parameters forsite-specific, daily weather generation in Yemen. Maps of annual means werecross-validated and tested against independent observations. Thesereplicated known features such as peak rainfall totals in the highlands andwestern escarpment, as well as maximum temperatures along the coastal plainsand interior. The weather generator reproduced daily and annual diagnosticswhen run with parameters from observed meteorological series for a test siteat Taiz. However, when run with interpolated parameters, the frequency ofwet days, mean wet-day amount, annual totals and variability wereunderestimated. Stratification of sites for model calibration improvedrepresentation of the growing season's rainfall totals. Future work should focuson a wider range of model inputs to better discriminate controls exerted bydifferent landscape units.
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