Short-time forecast on heavy rainfall in Eastern China using the deep generative models

Heavy rainfall, generated by multi-scale processes and characterized by small spatial scale, sudden occurrence, low predictability and strong disaster-causing, is the bottleneck of short-time forecast in China. At present, the grid resolution of quantitative forecast on precipitation in national quantitative precipitation forecast operation is only 5km, which could not meet the requirements of fine forecast for disaster prevention and reduction. Recent study in Europe and the United States shows that the high resolution short-time probabilistic forecast with advantages in heavy rainfall forecast could be generated by using the deep generative models to correct the error and improve the resolution of the low resolution numerical model forecast. In view of the above facts, based on the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) forecast data and China Meteorological Administration Land Surface Data Assimilation System (CLDAS) observed precipitation gridded data in 1km resolution, the short-time probabilistic precipitation forecast was studied in 1km resolution forecast by using the deep generative model in Eastern China. Results show that the short-time probabilistic forecast could be effectively generated by the deep generative model with the forecast feature inputs associated to the formation of heavy rainfall. Meanwhile, the critical success index and fractions skill score of 3 hours accumulation precipitation above 20mm could be greater than the model forecast. Case studies showed that the forecast generated by the deep generative model could effectively improve the forecast of the location and intensity of heavy rainfall, which showed promising application in the future in forecast operation in China.