An empirical ensemble rainfall nowcasting model using multi-scaled analogues

Abstract

Short-term rainfall prediction with high spatial and temporal resolution is of great importance to rainfall-triggered natural hazards such as landslide, flood, and debris flow. An analogue-based forecasting method may be able to provide short-term radar rainfall predictions, or “nowcasts,” in which the probability distribution of the atmospheric state at a given location in the future is estimated based on a set of past observations. Expensive computations and diverse atmospheric interactions across multiple spatial scales, however, limit the application of this method. This study employs a physically based, empirical ensemble rainfall nowcasting model to obtain ensembles from historical rainfall fields. To do this, we consider meteorological factors, rainfall spatial distributions, and the temporal evolution of rainfall patterns. Cross-correlation is used as a measure of the similarity between the spatial distributions of two rainfall patterns, and the movement rate of rain, with respect to both direction and distance, is used to compare the patterns temporally. A moving window scheme is used to reduce the computational load, with rainfall data providing most reference values. The relationship between different scales and rainfall forecasts is further explored by cascade divisions. While smaller analogue scales indicate better forecasts in this study, the optimal analogue scale will vary for different rainfall events. This study is only a start in incorporating multi-scaled analogue into rainfall nowcasting analysis, and a great deal of more effort is still needed to build a realistic and comprehensive analogue-based rainfall prediction model.