Transformer for sub-seasonal extreme high temperature probabilistic forecasting over eastern China.

Sub-seasonal high temperature forecasting is significant for early warning of extreme heat weather. Currently, deep learning methods, especially Transformer, have been successfully applied to the meteorological field. Relying on the excellent global feature extraction capability in natural language processing, Transformer may be useful to improve the ability in extended periods. To explore this, we introduce the Transformer and propose a Transformer-based model, called Transformer to High Temperature (T2T). In the details of the model, we successively discuss the use of the Transformer and the position encoding in T2T to continuously optimize the model structure experimentally. In the dataset, the multi-version data fusion method is proposed to further improve the prediction of the model with the reasonable expansion of the dataset. The performance of the well- designed model (T2T) is verified against the European Centre for Medium-Range Weather Forecasts (ECMWF) and multi-layer perceptron (MLP) at each grid of the 100.5°E to 138°E, 21°N to 54°N domain for the April to October of 2016–2019. For the case study initiated from 2 June 2018, the results indicated that T2T is significantly better than ECMWF and MLP, with smaller absolute error and more reliable probabilistic forecast for the extreme high event that happened during the third week. Overall, the deterministic forecast of T2T is superior to MLP and ECMWF due to the ability of utilizing spatial information of grids. T2T also provided a better-calibrated probability of high temperature and a sharper prediction probability density function than MLP and ECMWF. All in all, T2T can meet the operational requirements for extended period forecasting of extreme high temperature. Furthermore, our research can provide experience in the development of deep learning in this field and achieve the continuous progress of seamless forecasting systems. [ABSTRACT FROM AUTHOR]