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 \verb+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 \verb+ASOS+ network, we sourced \verb+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 \verb+ERA5+ \verb+T850+ data, setting the stage for ensuing multi-time step virtual observations. Probing into the assimilation impacts, the \verb+ASOS+ \verb+t2m+ data was integrated with the \verb+ERA5+ \verb+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.