This 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).
Agou, V. D., Pavlides, A., and Hristopulos, D. T., “Space-Time Analysis of Precipitation Reanalysis Data for the Island of Crete using Gaussian Anamorphosis with Hermite Polynomials”, 2021.
Andrew Pavlides, Vasiliki Agou, Dionissios T. Hristopulos, "Non-parametric Kernel-Based Estimation of Probability Distributions for Precipitation Modeling," September 2021. Manuscript submitted to the journal Advances in Water Resources.
Preprint available on the Arxiv e-print open-access repository.
The probability distribution of precipitation amount strongly depends on geography, climate zone, and time scale considered. Closed-form parametric probability distributions are not sufficiently flexible to provide accurate and universal models for precipitation amount over different time scales. In this paper we derive non-parametric estimates of the cumulative distribution function (CDF) of precipitation amount for wet time intervals. The CDF estimates are obtained by integrating the kernel density estimator leading to semi-explicit CDF expressions for different kernel functions. We investigate kernel-based CDF estimation with an adaptive plug-in bandwidth (KCDE), using both synthetic data sets and reanalysis precipitation data from the island of Crete (Greece). We show that KCDE provides better estimates of the probability distribution than the standard empirical (staircase) estimate and kernel-based estimates that use the normal reference bandwidth. We also demonstrate that KCDE enables the simulation of non-parametric precipitation amount distributions by means of the inverse transform sampling method.
Using cross-validation it was shown that the maps obtained using Gaussian anamorphosis with Hermite polynomials are more accurate than those generated by means of the standard Ordinary Kriging approach.