2024 Volume 80 Issue 16 Article ID: 23-16197
Estimating global precipitation fields is important for disaster forecasting and water resource management. This study proposes a new methodology to estimate global precipitation fields from ground rain gauge observations using advanced ensemble data assimilation techniques. Here, we use the algorithm of local ensemble transform Kalman filter (LETKF) in which the first guess and its error covariance are developed using reanalyzed precipitation data from the European Center for Medium-Range Forecasts. Our estimates have better agreements with the independent reference data than an existing product issued by the National Oceanic and Atmospheric Administration, as demonstrated by the verification against an independent rain gauge observation. This improvement would be achieved by improved error variance and covariance owing to the ensemble-based error covariance estimation. In addition, limiting the number of assimilated observations within the LETKF was beneficial to improve the precipitation estimates.
