Research on Surface Temperature Prediction Based on High-Resolution Numerical Prediction Products and Deep Learning

Abstract

This study utilized the 2020 -2021 China Meteorological Administration （CMA） Land Data Assimilation System （CLDAS） hourly surface air temperature （T2m） product in combination with the T2m forecast data from the CMA Shanghai Rapid Update Cycle Numerical Forecast （CMA-SH3）.A deep learning semantic segmentation model called MT-Cunet was developed to achieve a 24-hour T2m grid forecast that is updated on an hourly basis.The forecast results for 2022 were then tested and evaluated.Results showed that： MT- Cunet has demonstrated the most effective revision during the 3~9 h time horizon in the study range.It shows a significant reduction of 42.4% and 40.9% in the mean MAE and mean RMSE， respectively.The revision effect during the 10~24 h time horizon is also noteworthy， with a reduction of 26.7% and 26.3% in the mean MAE and mean RMSE， respectively.When evaluating low-temperature （≤0 ℃） and high-temperature （≥35 ℃） events， MT-Cunet exhibits a positive bias in high-temperature forecasts while showing a negative bias in low-temperature forecasts， and the magnitude of error is much smaller compared to CMA-SH3.On the spatial scale， MT-Cunet can substantially reduce the T2m forecast error in complex terrain and decrease the MAE dispersion of CMA-SH3， resulting in a more stable distribution of forecast errors.By examining and assessing the regional warming and cold wave processes in February and March 2022， it has been found that MT-Cunet demonstrates superior capability in predicting the timing and magnitude of temperature increases and decreases.In both warming and cold wave processes， the MAE of MT-Cunet is 28.9% and 33.8% lower than that of CMA-SH3， respectively.This suggests that the MT-Cunet model exhibits improved forecasting skills in transitional weather processes.Therefore， by employing a fast-updating cycle numerical model， it is possible to rapidly increase the number of forecast samples.Additionally， by refining the objective method of the semantic segmentation deep learning model， this approach effectively addresses the issue of poor performance in deep learning training caused by the limited amount of data in conventional numerical models.Furthermore， it opens up new possibilities for maximizing the utilization of domestic model resources and promoting the wider application of domestic model post-processing.

Keywords

• cma-sh3
• cldas
• 2 m temperature above ground
• bias correction
• deep learning