Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches

Reanalysis datasets constitute the main source of spatially homogeneous information for climate analysis since they provide long records (spanning several decades) of physically consistent hourly/daily gridded data for many variables produced globally with a particular atmospheric general circulation model (AGCM) assimilating the available observations (see https://reanalyses.org for an overview of the current reanalyses). Besides the historical records, in some cases reanalyses provide near real-time information that allows monitoring the state of the climate. For instance, ERA5 [Hersbach et al., 2020] is the latest ECMWF climate reanalysis, providing hourly data on many atmospheric and land-surface parameters at 0.25º resolution, from 1940 to near real-time. However, much of this data is generated at coarse spatial resolutions, typically on the order of tens of kilometres, hampering their application for local and regional climate analysis, including extreme weather events, which often occur on smaller spatial scales. Enhancing the spatial resolution of reanalyses datasets is therefore critical for improving its utility for local-scale climate analysis and decision-making. A number of downscaling methods have been developed over the last decades for improving the spatial resolution of AGCM outputs based on two main approaches [Maraun and Widmann, 2017]: dynamical and statistical downscaling. Dynamical downscaling employs regional atmospheric models (Limited Area Models, LAMs) over limited areas of interest, driven at the boundaries by the AGCM outputs, to increase their coarse-resolution. This approach allows to solve regional/local processes and provides physically consistent results, but is limited by its high computational demands. It has been recently applied to generate regional reanalysis over continental-wide areas, such as the CERRA renalysis over Europe using the HARMONIE-ALADIN regional model (driven by ERA5) at a 5.5km resolution.