Non-parametric Kernel-Based Estimation of Probability Distributions for Precipitation Modeling

• URL: http://arxiv.org/abs/2109.09961v1
• Date: Tue, 21 Sep 2021 04:52:00 GMT
• Title: Non-parametric Kernel-Based Estimation of Probability Distributions for Precipitation Modeling
• Authors: Andrew Pavlides, Vasiliki Agou, Dionissios T. Hristopulos
• Abstract summary: We derive non-parametric estimates of the cumulative distribution function (CDF) of precipitation amount for wet time intervals. We show that KCDE provides better estimates of the probability distribution than the standard empirical (staircase) estimate.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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