A model for real‐time quantitative rainfall forecasting using remote sensing: 2. Case studies

Abstract

The performance of a real‐time physically based rainfall forecasting model is examined using radar, satellite, and ground station data for a region of Oklahoma. Model formulation is described in an accompanying paper (French and Krajewski, this issue). Spatially distributed radar reflectivity observations are coupled with model physics and uncertainty analysis through (1) linearization of model dynamics and (2) a Kalman filter formulation. Operationally available remote sensing observations from radar and satellite, and surface meteorologic stations define boundary conditions of the two‐dimensional rainfall model. The spatially distributed rainfall is represented by a two‐dimensional field of cloud columns, and model physics define the evolution of vertically integrated liquid water content (the model state) in space and time. Rainfall forecasts are evaluated using least squares criteria such as mean error of forecasted rainfall intensity, root mean square error of forecasted rainfall intensity, and correlation coefficient between spatially distributed forecasted and observed rainfall rates. The model performs well compared with two alternative real‐time forecasting strategies: persistence and advection forecasting.