Deep learning‐based postprocessing for hourly temperature forecasting

Abstract In this article, a prediction model based on spatiotemporal stacked ResNet (Res‐STS) for hourly temperature prediction is designed. On the timescale, the Res‐STS removes the gate structure of the long short‐term memory (LSTM) model, and the data of multiple consecutive time nodes are stacked together to preserve all temporal characteristics of the data. A point‐to‐point data mapping relationship is developed on the spatial scale to maximize the impact of large‐scale environmental background field characteristics on a single grid point. Based on the historical gridded data from the China Meteorological Administration land data assimilation system (CLDAS) and the optimal factor dataset of the European Centre for Medium‐Range Weather Forecasts Integrated Forecasting System (ECMWF‐IFS) from 2017 to 2020, hourly temperature prediction models based on convolutional long short‐term memory (ConvLSTM) and Res‐STS model are developed, respectively. Furthermore, the prediction results of the two models in 2021 are compared with the ECMWF‐IFS. The results show that the root mean square error (RMSE) of the prediction results by ConvLSTM and Res‐STS models are both smaller than that of ECMWF‐IFS. Specially, the Res‐STS model performs best: it reduces the RMSE by 20.8% (24.5%) compared with the ConvLSTM (ECMWF‐IFS). Specifically, the RMSE peaks in the afternoon when the daily maximum temperature occurs, while it is relatively smaller at night. Res‐STS demonstrates a significant improvement in forecast performance compared with ECMWF‐IFS, while ConvLSTM's correction during the period of maximum temperature occurrence has been enhanced. Moreover, the forecast performance of the Res‐STS model is least affected by terrain compared with those of the ConvLSTM and ECMWF‐IFS. For the regions with terrain height greater than 1 km, the model Res‐STS evidently improves the RMSE.