An Attempt to Improve the Forecasting of Persistent Severe Rainfall Using the Spectral Nudging and Update Cycle Methods

Abstract

sing the interior spectral nudging and update cycle (SN+UIC) methods in the regional Weather Research and Forecasting (WRF) Model, the numerical predictions of four persistent severe rainfall (PSR) events during the preflood season in south China were investigated, based on the fact that the global model has an advantage in predicting the large-scale atmospheric variation and the regional model is better in terms of simulating small-scale changes. The simulation results clearly indicated that the SN+UIC improved the prediction of the PSR events? daily precipitation for moderate, heavy, and torrential rains (10?100 mm day?1). It also improved the simulative forecasts of the two categories of rain with accumulated precipitation above 50 and 100 mm at lead times of 5?11 days. Moreover, the longer the forecast lead time is, the larger the decrease in the Brier score. Additionally, the SN+UIC method decreased the root-mean-square error for accumulated rainfall (6.2%) and relative humidity (5.67%).