Distributed Quantitative Precipitation Forecasting Using Information from Radar and Numerical Weather Prediction Models

Abstract

The benefits of short-term (1–6 h), distributed quantitative precipitation forecasts (DQPFs) are well known. However, this area is acknowledged to be one of the most challenging in hydrometeorology. Previous studies suggest that the “state of the art” methods can be enhanced by exploiting relevant information from radar and numerical weather prediction (NWP) models, using process physics and data-dictated tools where each fits best. Tests indicate that improved results are obtained by decomposing the overall problem into component processes, and that each process may require alternative tools ranging from simple interpolation to statistical time series models and artificial neural networks (ANNs). A new hybrid modeling strategy is proposed for DQPF that utilizes measurements from radar [Weather Surveillance Radar-1998 Doppler (WSR-88D) network: 4 km, 1 h] and outputs from NWP models (48-km Eta Model: 48 km, 6 h). The proposed strategy improves distributed QPF over existing methods like radar extrapolation or NWP-based QPF by themselves, as well as combinations of radar extrapolation and NWP-based QPF.