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。