Assimilation of the deep learning-corrected global forecast system fields into the regional model for improving medium-range persistent precipitation forecasts

Abstract

The error of regional models gradually increases during long-term integration, and it can be reduced with the constraint of large-scale circulation by assimilating global forecast system (GFS) fields to improve the forecast performance. However, with increasing forecast time, the GFS forecast error also significantly increases and varies widely. Directly assimilating GFS fields into the regional model without processing may generate negative impacts, whereas employing postprocessing correction methods can effectively mitigate GFS forecast errors. In this study, the GFS data of European Centre for Medium-Range Weather Forecasts (ECMWF) are first corrected using a convolutional long- and short-term memory network method. The correction model can correct multiple forecasts simultaneously by training only one model, and the correction results are less erroneous than those obtained by training multiple correction models for the different forecast times and pressure layers. The corrected ECMWF forecast fields are then assimilated into Weather Research and Forecasting Model (WRF) using the nudging method. This assimilation method generally improves the forecasts for precipitation above the moderate rainfall level at forecast times of 4–7 days, and reduces the errors of surface wind speed forecasts. This study improves the accuracy of correction models and the forecasts of persistent precipitation weather event with regional model.