Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/749E2767-D43A-426D-86AB-8F46F12DF9B3 | ||
| Year | 2022 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
|
||
| Bibliographic Citation | A. Pavlides, V. D. Agou, and D. T. Hristopulos, “Non-parametric kernel-based estimation and simulation of precipitation amount,” J. Hydrol., vol. 612, Sep. 2022, 10.1016/j.jhydrol.2022.127988. https://doi.org/10.1016/j.jhydrol.2022.127988 | ||
| Appears in Collections |
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. This paper derives non-parametric estimates of the cumulative distribution function (CDF) of precipitation amount for wet periods. The CDF estimates are obtained by integrating the kernel density estimator leading to semi-explicit CDF expressions for different kernel functions. An adaptive plug-in bandwidth estimator (KCDE) is investigated, using both synthetic data sets and reanalysis precipitation data from the Mediterranean island of Crete (Greece). It is shown 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. It is also demonstrated that KCDE enables the simulation of non-parametric precipitation amount distributions by means of the inverse transform sampling method.
Copyright © DIAS 2013
