Estimating global precipitation fields from rain gauge observations using local ensemble data assimilation

Abstract. It is crucial to improve global precipitation estimates for a better understanding on water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using the algorithm of the local ensemble transform Kalman filter (LETKF) in which the first guess and its error covariance are developed based on the reanalysis data of precipitation from the European Center for Medium-Range Forecasts (ERA5). For the estimation of each date, the climatological ensembles are constructed using the ERA5 data 10 years before and after that date, and thereafter are utilized to obtain the first guess and its error covariance. Additionally, the global rain gauge observations provided by the National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) are used for observation inputs in the LETKF algorithm.

Our estimates have better agreements against independent rain gauge observations compared to the existing precipitation estimates of the NOAA CPC in general. Because we utilized the same rain gauge observations for the inputs of our estimation as those used in the NOAA CPC product, it is indicated that the proposed estimation method is superior to that of the NOAA CPC (i.e., the Optimal Interpolation). Our proposed method took the advantage of constructing a physically guaranteed first guess and its error variance using reanalysis data for interpolating precipitation fields. Furthermore, the method of this study is shown to be particularly beneficial for mountainous or rain-gauge-sparse regions.