Data availability and accessibility often present challenges to resolving regional water management issues. One primary input essential to models and other tools used to inform policy decisions is daily precipitation. Since observed datasets are not always present or accessible, data from the Climate Forecast System Reanalysis (CFSR) have become a potential alternative. A comparison of CFSR precipitation data to available observed data from stations in the East African countries Kenya, Uganda, and Tanzania showed notable differences between the two datasets, particularly with respect to precipitation totals and number of days receiving rainfall. A sliding window bias correction approach evaluated using 3 methods with 8 different window length and timestep variations showed that empirical quantile mapping with a 30-day sliding window length and 1-day timestep achieved the best performance. A comparison of bias corrected CFSR precipitation data against observed data showed marked improvement in the similarity of the number of wet days and maximum daily rainfall between the two datasets. For precipitation totals, bias correction reduced underprediction errors by 32% and overprediction errors by 81%. Results indicate that bias-corrected CFSR precipitation data provides an improved basis for water resources applications in the study region. Methodologies and approaches are extendable to other data-scarce regions or areas where complete and consistent data are not easily accessible.

数据的可用性和可访问性通常给解决区域水管理问题带来挑战。每天的降水量是模型和其他用于决策的工具所必不可少的主要投入。由于观察到的数据集并不总是存在或可访问，因此来自气候预测系统重新分析（CFSR）的数据已成为潜在的替代方法。CFSR降水数据与东非国家肯尼亚，乌干达和坦桑尼亚的气象站观测数据的比较表明，这两个数据集之间存在显着差异，特别是在降水总量和接受降雨的天数方面。使用3种方法在8种不同的窗口长度和时间步长变化情况下评估的滑动窗口偏差校正方法表明，以30天的滑动窗口长度和1天的时间步长进行经验分位数映射可获得最佳性能。偏差校正的CFSR降水数据与观测数据的比较表明，两个数据集之间的湿日数和最大日降水量的相似性显着提高。对于降水总量，偏差校正可将预测不足的误差减少32％，将预测过度的误差减少81％。结果表明，偏差校正后的CFSR降水数据为研究区域的水资源应用提供了改进的基础。方法和方法可以扩展到其他数据稀缺的区域或难以访问完整且一致的数据的区域。