Data-driven analysis of annual rain distributions

Yosef Ashkenazy; Naftali R. Smith

doi:10.1103/PhysRevResearch.6.023187

Data-driven analysis of annual rain distributions

Yosef Ashkenazy
, Naftali R. Smith

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Rainfall is an important component of the climate system, and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well.

Original language	English
Article number	023187
Journal	Physical Review Research
Volume	6
Issue number	2
DOIs	https://doi.org/10.1103/PhysRevResearch.6.023187
State	Published - Apr 2024
Externally published	Yes

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Publisher Copyright:
© 2024 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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10.1103/PhysRevResearch.6.023187

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title = "Data-driven analysis of annual rain distributions",

abstract = "Rainfall is an important component of the climate system, and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well.",

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AB - Rainfall is an important component of the climate system, and its statistical properties are vital for prediction purposes. In this study, we have developed a statistical method for constructing the distribution of annual precipitation. The method is based on the convolution of the measured monthly rainfall distributions and does not depend on any presumed annual rainfall distribution. Using a simple statistical model, we demonstrate that our approach allows for a better prediction of extremely dry or wet years with a recurrence time several times longer than the original time series. The method that has been proposed can be utilized for other climate variables as well.

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Data-driven analysis of annual rain distributions

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