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Spatial modeling of precipitation based on data-driven warping of Gaussian processes

Agou Vasiliki, Pavlidis Andreas, Christopoulos Dionysios

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URI: http://purl.tuc.gr/dl/dias/8D690926-FF07-44DB-A6B3-4F2A674563DF
Year 2022
Type of Item Peer-Reviewed Journal Publication
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Bibliographic Citation V. D. Agou, A. Pavlides, and D. T. Hristopulos, “Spatial modeling of precipitation based on data-driven warping of Gaussian processes,” Entropy, vol. 24, no. 3, Feb. 2022, doi: 10.3390/e24030321. https://doi.org/10.3390/e24030321
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Summary

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.

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