Hyper-Resolution Wind Forecasting in Austral Chile combining WRF forecasting and Deep Learning techniques.

Abstract

Near-surface wind conditions, specifically at 10 meters above ground, play a crucial role in areas with complex topography like the Austral Chilean Territory, characterized by small islands, channels, and fiords. The impact of topography and land cover on wind patterns is particularly significant. In the other hand, wind impacts local transport and, consequently, the economy and social activities. Accurate forecasting of these winds is essential for optimal planning and heightened maritime safety. While dynamic models, such as the WRF model, have proven valuable for stakeholders, their operational use is limited by the high computational cost, restricting spatial resolutions to a few kilometers. For example, the Chilean Navy Weather Service employs the WRF model with a resolution of up to 3 km in specific areas, and the Chilean Weather Office uses a 4 km resolution across the entire continental territory. This study addresses this limitation by developing an emulator for dynamic downscaling of surface wind, aiming for hyper resolutions (~300 m) over Austral Chile. Utilizing cluster analysis of ERA 5 10m-wind fields, eight wind patterns were identified. Multi-day simulations were conducted with telescopic domains reaching 100 m resolution, incorporating NASA's ASTER DEM into WRF and updating the coastline in the default 500 m land-use dataset. The consistency analysis of these results will be presented. To achieve hyper-resolution forecasting, various deep learning models, including Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN), were trained to downscale the 3 km domain to the 100 m one. The presentation will focus on the evaluation and comparison of these models, showcasing the first key results of this research. This study is part of a larger research project that aims to produce a very high-resolution wind forecasting system, based on the downscaling of WRF simulations by using Deep learning techniques (SiVAR-Austral, funded by ANID ID22I10206). Results will be valuable to stakeholders by enhancing both planning capabilities and maritime safety in the Austral Chilean Territory.