10 May 2023
 | 10 May 2023
Status: this preprint is currently under review for the journal NPG.

A two-folds deep learning strategy to correct and downscale winds over mountains

Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig

Abstract. Assessing wind fields at a local scale in mountainous terrain has long been a scientific challenge partly because of the complex interaction between large-scale flows and local topography. Traditionally, the operational applications that require high resolution wind forcings rely on downscaled outputs of numerical weather predictions systems. Downscaling models either proceed from a function that links large scale wind fields to local observations (hence including a corrective step), or use operations that account for local scale processes, through statistics or dynamical simulations, and without prior knowledge of large scale modeling errors. This work presents a strategy to first correct and then downscale the wind fields of the numerical weather prediction model AROME operating at 1300 m grid spacing, by using a modular architecture composed of two artificial neural networks and the DEVINE downscaling model. We show that our method is able to first correct the wind direction and speed from the large scale model (1300 m), and then accurately downscale it to a local scale (30 m) by using the DEVINE downscaling model. The innovative aspect of our method lies in its optimization scheme that accounts for the downscaling step in the computations of the corrections of the coarse scale wind fields. This modular architecture yields competitive results without suppressing the versatility of the downscaling model DEVINE, which remains unbounded to any wind observations.

Louis Le Toumelin et al.

Status: open (until 05 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Louis Le Toumelin et al.

Louis Le Toumelin et al.


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Short summary
Forecasting wind fields over mountains is of high importance for several applications and particularly to understand how wind erodes and deposes snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large scale wind forecasts in mountains and increase their spatial resolution.