Complementarity of Large-Eddy Simulation and Synthetic Aperture Radar to understanding surface winds in an extra-tropical cyclone

Extra-tropical cyclones are intense meteorological phenomena, highly destructive and sometimes causing fatalities. Although the mechanisms of formation and propagation are well known, a current challenge is to correctly represent in numerical weather and climate models the mechanisms involving the presence of strong surface winds. Most of these winds are generated by sub-kilometer-scale phenomena with very short lifetimes (few minutes). Motivated by these questions, the ANR WINDGUST project is dedicated to modeling the storm-induced circulations as finely as possible focusing. In this work, we focus on storm Alex, which crossed North-West France between October 1 and 2, 2020, causing extensive damage. In fact, the Sentinel-1A satellite equipped with Synthetic Aperture Radar (SAR) scanned the cyclone area (SAR swath ∼ 250 km) at high horizontal resolution (∼ 100 m) allowing us to characterize fine-scale structures and surface winds.
These data, and in-situ and remote sensing observations, are used to evaluate Méso-NH model simulations performed at 800 m of resolution, in particular the ability to reproduce mesoscale structure. Despite an underestimation of surface winds compared with observations, frontal areas are well located.
The representation of high surface wind values (> 20 m/s) is improved by downscaling (Large-Eddy Simulation (LES); 200 m of horizontal resolution). Storm Alex is decomposed into several frontal zones corresponding to high surface wind values and spectra analysis are perfomed in SAR and LES. The results show the presence of energetic finescale structures responsible for high wind values, corroborated by auto-correlation spectra. Furthermore, LES  highlights the presence of boundary layer rolls where the strongest surface winds are generated by subsident vertical transport of momentum.
This approach enables very high-resolution SAR observations to be used as validation tools for LES in areas with lacking observations while at the same time allowing LES to explain the presence of specific sea-surface signatures in SAR images, answering scientific questions such as the formation of strong surface winds.