Downscaling of Precipitation Forecasts Based on Single Image Super-Resolution

High spatial resolution weather forecasts that capture regional-scale dynamics are important for natural hazards prevention, especially for the regions featured with large topographical variety and local climate. While deep convolutional neural networks have made great progress in single image super-resolution (SR) which learns mapping relationship between low- and high- resolution images, limited efforts have been made to explore the potential of downscaling in this way. In the study, three advanced SR deep learning frameworks including Super-Resolution Convolutional Neural Network (SRCNN), Super-Resolution Generative Adversarial Networks (SRGAN) and Enhanced Deep residual networks for Super-Resolution (EDSR) are proposed for downscaling forecasts of daily precipitation in southeast China (100°E -130°E, 15°N -35°N). The SR frameworks are designed to improve the horizontal resolution of daily precipitation forecasts from raw 1/2 degrees (~50km) to 1/4 degrees (~25km) and 1/8 degrees (~12.5km), respectively. For comparison, Bias Correction Spatial Disaggregation (BCSD) as a traditional SD method is also performed under the same framework. The precipitation forecasts used in our work are obtained from different Ensemble Prediction Systems (EPSs) including ECMWF, NCEP and JMA which are provided by TIGGE datasets. A group of metrics have been applied to assess the performance of the three SR models, including Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC) and Equitable Threat Score (ETS). Results show that three SR models can effectively capture the detailed spatial information of local precipitation that is ignored in global NWPs. Among the three SR models, EDSR obtains the optimum results with lower RMSE and higher ACC which shows better downscaling skills. Furthermore, the SR downscaling methods can be extended to the statistical downscaling for other predictors as well.