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Distributed Deep Learning on HPC for Infilling Holes in Spatial Precipitation Data
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DescriptionMissing climatological data is a general problem in climate research that leads to uncertainty of prediction models that rely on these data resources. So far, existing approaches for infilling missing precipitation data are mostly numerical or statistical techniques that require time consuming computations and are not suitable for large regions with missing data. Most recent machine learning techniques have proven to perform well on infilling missing temperature or satellite data. However, these techniques consider only spatial variability in the data whereas precipitation data is much more variable in both space and time. We propose a convolutional inpainting network that additionally considers temporal variability and atmospheric parameters in the data. The model was trained and evaluated on the RADOLAN data set over Germany. Since the training of this high-resolved data set requires a large amount of computational resources, we apply distributed training on an HPC system to maximize the performance.