Emulating the adaptation of wind fields to complex terrain with deep-learning


Estimating the impact of wind-driven snow transport requires modeling wind fields with a lower grid spacing than the spacing on the order of one or a few kilometers used in the current numerical weather prediction (NWP) systems. In this context, we introduce a new strategy to downscale wind fields from NWP systems to decametric scales, using high resolution (30m) topographic information. Our method (named DEVINE) leverage on a convolutional neural network (CNN), trained to replicate the behaviour of the complex atmospheric model ARPS, previously run on a large number (7279) of synthetic Gaussian topographies under controlled weather conditions. A 10-fold cross validation reveals that our CNN is able to accurately emulate the behavior of ARPS (mean absolute error for wind speed = 0.16 m/s). We then apply DEVINE to real cases in the Alps, i.e. downscaling wind fields forecasted by AROME NWP system using information from real alpine topographies. DEVINE proved able to reproduce main features of wind fields in complex terrain (acceleration on ridges, leeward deceleration, deviations around obstacles). Furthermore, an evaluation on quality checked observations acquired at 61 sites in the French Alps reveals an improved behaviour of the downscaled winds (AROME wind speed mean bias is reduced by 27% with DEVINE), especially at the most elevated and exposed stations. Wind direction is however only slightly modified. Hence, despite some current limitations inherited from the ARPS simulations setup, DEVINE appears as an efficient downscaling tool whose minimalist architecture, low input data requirements (NWP wind fields and high-resolution topography) and competitive computing times may be attractive for operational applications.