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contributor authorOphélia Miralles
contributor authorDaniel Steinfeld
contributor authorOlivia Martius
contributor authorAnthony C. Davison
date accessioned2023-04-12T18:52:34Z
date available2023-04-12T18:52:34Z
date copyright2022/11/28
date issued2022
identifier otherAIES-D-22-0018.1.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290399
description abstractNear-surface wind is difficult to estimate using global numerical weather and climate models, because airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, we use a statistical approach based on deep learning and a high-resolution digital elevation model to spatially downscale hourly near-surface wind fields at coarse resolution from ERA5 reanalysis from their original 25-km grid to a 1.1-km grid. A 1.1-km-resolution wind dataset for 2016–20 from the operational numerical weather prediction model COSMO-1 of the national weather service MeteoSwiss is used to train and validate our model, a generative adversarial network (GAN) with gradient penalized Wasserstein loss aided by transfer learning. The results are realistic-looking high-resolution historical maps of gridded hourly wind fields over Switzerland and very good and robust predictions of the aggregated wind speed distribution. Regionally averaged image-specific metrics show a clear improvement in prediction relative to ERA5, with skill measures generally better for locations over the flatter Swiss Plateau than for Alpine regions. The downscaled wind fields demonstrate higher-resolution, physically plausible orographic effects, such as ridge acceleration and sheltering, that are not resolved in the original ERA5 fields.
publisherAmerican Meteorological Society
titleDownscaling of Historical Wind Fields over Switzerland Using Generative Adversarial Networks
typeJournal Paper
journal volume1
journal issue4
journal titleArtificial Intelligence for the Earth Systems
identifier doi10.1175/AIES-D-22-0018.1
treeArtificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 004
contenttypeFulltext


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