STATION SPECIFIC DOWNSCALING OF CLIMATE INFORMATION FOR AGRICULTURAL APPLICATIONS IN KENYA

Abstract

It has long been recognized that if society could have advance information on weather, the adverse effects associated with it could be minimized. Prevalence of traditional forecast practices in various parts of the world reflects the demand for long- range forecasts to manage uncertainties associated with climate variability. Recent advancements in climate prediction promise huge benefits for society. An analysis and understanding of the relationships between the weather and agricultural production systems and especially the complexities associated with the predictability, variability and distribution of rainfall is essential element in improving crop production and agricultural planning decision making. The severe impacts associated with extreme climate events can be reduced through good understanding of the climate patterns of the previous events, enhanced monitoring and timely dissemination of early warning as well as improved awareness on the usefulness of climate information and prediction products in decision making. In this project, a model developed for converting probability climate forecasts into atmospheric values is validated. Chapter one gives an overview of seasonal downscaling of climate. The theoretical development of the model is covered in chapter two while in chapter three the forecast has been translated into potential rainfall amounts using the climatological forecast interpretation. Climate outlook statements as produced by the Greater horn of Africa climate outlook forums for the years 1999-2002 are considered.