In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting. This study delves into this emerging trend, presenting our methodologies and outcomes. We harnessed the UK's local ERA5 850 hPa temperature data and refined the U-STN12 global weather forecasting model, tailoring its predictions to the UK's climate nuances. From the ASOS network, we sourced T2m data, representing ground observations across the UK. We employed the advanced kriging method with a polynomial drift term for consistent spatial resolution. Furthermore, Gaussian noise was superimposed on the ERA5 T850 data, setting the stage for ensuing multi-time step synthetic observations. Probing into the assimilation impacts, the ASOS T2m data was integrated with the ERA5 T850 dataset. Our insights reveal that while global forecast models can adapt to specific regions, incorporating atmospheric data in DA significantly bolsters model accuracy. Conversely, the direct assimilation of surface temperature data tends to mitigate this enhancement, tempering the model's predictive prowess.

探究了数据驱动的机器学习模型与数据同化相结合的趋势对天气预测的增强作用，通过利用ERA5 850hPa温度数据和改进的U-STN12全球天气预报模型，针对英国的气候特点进行预测，并发现将大气数据整合到DA中显著增强了模型的准确性，然而直接同化地表温度数据倾向于减弱这种增强效果。

利用ERA5、ASOS和U-STN模型进行英国天气预报的数据同化