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In recent years, the convergence of data-driven machine learning models withData Assimilation (DA) offers a promising avenue for enhancing weatherforecasting. This study delves into this emerging trend, presenting ourmethodologies and outcomes. We harnessed the UK's local ERA5 850 hPatemperature 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. Weemployed the advanced kriging method with a polynomial drift term forconsistent spatial resolution. Furthermore, Gaussian noise was superimposed onthe ERA5 T850 data, setting the stage for ensuing multi-time step syntheticobservations. Probing into the assimilation impacts, the ASOS T2m data wasintegrated with the ERA5 T850 dataset. Our insights reveal that while globalforecast models can adapt to specific regions, incorporating atmospheric datain DA significantly bolsters model accuracy. Conversely, the directassimilation of surface temperature data tends to mitigate this enhancement,tempering the model's predictive prowess.

Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK