DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders

arXiv:2404.18314v1 Announce Type: new
Abstract: In meteorology, finding similar weather patterns or analogs in historical datasets can be useful for data assimilation, forecasting, and postprocessing. In climate science, analogs in historical and climate projection data are used for attribution and impact studies. However, most of the time, those large weather and climate datasets are nearline. They must be downloaded, which takes a lot of bandwidth and disk space, before the computationally expensive search can be executed. We propose a dimension …

abstract arxiv attribution autoencoders climate climate science cs.lg data datasets forecasting however impact meteorology nlin.cd patterns physics.ao-ph projection science studies type weather