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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