Data Assimilation using ERA5, ASOS, and the U-STN model for Weather
  Forecasting over the UK
Wenqi Wang Jacob Bieker Rossella Arcucci César Quilodrán-Casas
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.