Spatial modeling of precipitation based on data-driven warping of Gaussian processesSpatial modeling of precipitation based on data-driven warping of Gaussian processes Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2023-08-222022enThis research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014–2020» in the context of the project “Gaussian Anamorphosis with Kernel Estimators for Spatially Distributed Data and Time Series and Applications in the Analysis of Precipitation” (MIS 5052133).Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. We investigate the proposed warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated with non-Gaussian noise and (ii) a reanalysis dataset of monthly precipitation from the Mediterranean island of Crete. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. We conclude that wGPR equipped with the proposed data-driven warping provides enhanced flexibility and—at least for the cases studied– improved predictive accuracy for non-Gaussian data.http://creativecommons.org/licenses/by/4.0/Entropy243Agou_et_al_Entropy_24(3)_2022.pdfChania [Greece]Library of TUC2023-08-22application/pdf1.8 MBfree Agou Vasiliki Αγου Βασιλικη Pavlidis Andreas Παυλιδης Ανδρεας Christopoulos Dionysios Χριστοπουλος Διονυσιος MDPI Non-Gaussian data Skewed distributions Gaussian anamorphosis Reanalysis data Kriging Warped Gaussian processes