Entropy (Feb 2022)

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes

  • Vasiliki D. Agou,
  • Andrew Pavlides,
  • Dionissios T. Hristopulos

DOI
https://doi.org/10.3390/e24030321
Journal volume & issue
Vol. 24, no. 3
p. 321

Abstract

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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.

Keywords