By linking higher resolution single column precipitation models with non-parametric probability models, we can express, utilize and explore these relationships. The global climate model data is used in two ways. First, the precipitation rate is extracted for each day, for each grid cell situated over Africa. Second, wind velocity, humidity and temperature fields in conjunction with local topography obtained from a digital elevation model are used to drive a diagnostic model of orographic precipitation. The distribution of daily rainfall amounts varies widely with location in Africa.
Different regions have different climate dynamics and different distributions of daily rainfall.
To account for this spatial heterogeneity we develop unique sets of statistical models for each weather station. These logistic regression models link the global climate and orographic rainfall amounts to the observed frequency of rainfall events at a range of intensities. Given climate and orographic rainfall amounts we are then capable of building a conditional distribution function, from which we can infer probabilities of given events or the maximum likelihood estimate of precipitation. The coefficients of these models are interpolated to a continental grid, and used to derive a historic time-series of daily rainfall. The orographic model has been coded in C and run, generating precipitation fields for 33 years of daily events on a 0.1 degree grid of Africa.