  The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution
regional reanalysis dataset for the European domain. In recent years it has
shown significant utility across various climate-related tasks, ranging from
forecasting and climate change research to renewable energy prediction,
resource management, air quality risk assessment, and the forecasting of rare
events, among others. Unfortunately, the availability of CERRA is lagging two
years behind the current date, due to constraints in acquiring the requisite
external data and the intensive computational demands inherent in its
generation. As a solution, this paper introduces a novel method using diffusion
models to approximate CERRA downscaling in a data-driven manner, without
additional informations. By leveraging the lower resolution ERA5 dataset, which
provides boundary conditions for CERRA, we approach this as a super-resolution
task. Focusing on wind speed around Italy, our model, trained on existing CERRA
data, shows promising results, closely mirroring original CERRA data.
Validation with in-situ observations further confirms the model's accuracy in
approximating ground measurements.
