Statistical Tests for Extreme Precipitation Volumes

Author:

Korolev VictorORCID,Gorshenin AndreyORCID,Belyaev Konstatin

Abstract

The analysis of the real observations of precipitation based on the novel statistical approach using the negative binomial distribution as a model for describing the random duration of a wet period is considered and discussed. The study shows that this distribution fits very well to the real observations and generalized standard methods used in meteorology to detect an extreme volume of precipitation. It also provides a theoretical base for the determination of asymptotic approximations to the distributions of the maximum daily precipitation volume within a wet period, as well as the total precipitation volume over a wet period. The paper demonstrates that the relation of the unique precipitation volume, having the gamma distribution, divided by the total precipitation volume taken over the wet period is given by the Snedecor–Fisher or beta distributions. It allows us to construct statistical tests to determine the extreme precipitations. Within this approach, it is possible to introduce the notions of relatively and absolutely extreme precipitation volumes. An alternative method to determine an extreme daily precipitation volume based on a certain quantile of the tempered Snedecor–Fisher distribution is also suggested. The results of the application of these methods to real data are presented.

Funder

Russian Foundation for Basic Research

RF Presidential scholarship program

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Russian Studies on Clouds and Precipitation in 2019–2022;Izvestiya, Atmospheric and Oceanic Physics;2023-12

2. Russian Studies on Clouds and Precipitation in 2019–2022;Известия Российской академии наук. Физика атмосферы и океана;2023-12-01

3. Bounds for convergence rate in laws of large numbers for mixed Poisson random sums;Statistics & Probability Letters;2021-01

4. On the Efficiency of Machine Learning Algorithms for Imputation in Spatiotemporal Meteorological Data;Advances in Intelligent Systems and Computing;2021

5. Probability Models and Statistical Tests for Extreme Precipitation Based on Generalized Negative Binomial Distributions;Mathematics;2020-04-16

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