Dynamic ANN for precipitation estimation and forecasting from radar observations

Instantaneous quantitative precipitation estimation (QPE) and quantitative precipitation forecasting (QPF) by utilizing the meteorological radar data are potentially of great benefit for operational hydrology in river basins. The most commonly used technique of radar-based rainfall estimation is a best fitted power-law function between reflectivity (Z) and rain intensity (R). An emerging tool in QPE/QPF using radar data is the artificial neural network (ANN) that is capable of learning complex nonlinear relationships. In this study, we introduce a dynamic ANN approach to construct instantaneous QPE and one-hour-ahead QPF by using a three-dimensional radar data structure, which takes into account the terminal velocity and the horizontal advection. Radar measurements and three rain gauges for six typhoon events in the Keelung River, Taiwan, were used for calibrating and evaluating the Z–R relation and dynamic ANN models. The results of current rainfall estimation and one-hour-ahead rainfall forecasting all indicate that the dynamic ANN can produce much more accurate and stable performance than the Z–R power-law models. This study demonstrates that the dynamic ANN can be applied successfully in instantaneous QPE and QPF by using remote sensing data.