Authors: Christopher Kadow, Étienne Plésiat, and Johannes Meuer (German Climate Computing Centre (DKRZ)); David Matthew Hall (NVIDIA Corporation); Uwe Ulbrich (Free University of Berlin); and Hannes Thiemann and Thomas Ludwig (German Climate Computing Centre (DKRZ))
Abstract: Historical temperature measurements are the basis of important global climate datasets like HadCRUT4 and HadCRUT5 to analyze climate change. These datasets contain many missing values and have low resolution grids. Here we demonstrate that artificial intelligence can skillfully fill these observational gaps and upscale these when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate reconstructions via transfer learning. In addition, high resolution in weather and climate was always a common and ongoing goal of the community. We gain a neural network which reconstructs and downscales the important observational data sets (IPCC AR6) at the same time, which is unique and state-of-the-art in climate research.
Best Poster Finalist (BP): no
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