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Deep Convolutional Networks for Feature Selection in Statistical Downscaling

Conference: 8th International Workshop on Climate Informatics
Year: 2018
Contribution type: Poster
PDF file: 2018_bano_CI.pdf
Poster: CI2018_poster_paper8.pdf

The potential of (deep) convolutional neural networks for automatic predictor selection in statistical downscaling over large continental domains is analyzed focusing on a simple illustrative example (precipitation occurrence). It is shown that these models automatically handle redundancy and perform geographical and variable selection/transformation of predictors in a robust and spatially consistent form, obtaining similar features for different predictor sets. Results are compared with best performing standard methods from the largest-to-date intercomparison of statistical downscaling methods (VALUE) using “perfect” reanalysis predictors.