A holistic stochastic model for precipitation events

In the western United States, much of the annual precipitation falls during relatively few storm events. When precipitation is measured as daily (or hourly, etc.) accumulations, these events appear as sequences of various durations. We describe a trivariate probability distribution and assert that it provides a natural description for the durations, magnitudes, and maximum intensities of such events. We show that this distribution, in its most straightforward application, indeed describes precipitation events composed of daily observations at most long-term weather stations across the western United States, allowing for a flexible assessment of specific event probabilities and return periods with respect to their three defining components. This characterization opens the door to further understanding of risk associated with out-of-sample meteorological events.

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Publication Details

Journal
Scientific Reports
Published
2025-02-07
DOI
https://doi.org/10.1038/s41598-024-77031-3
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

A holistic stochastic model for precipitation events

Alexander Gershunov, Tomasz J. Kozubowski, Alexander Weyant, Julie Kalansky et al.
Scientific Reports
Hydrology and Drought Analysis
article

A holistic stochastic model for precipitation events

Alexander Gershunov, Tomasz J. Kozubowski, Alexander Weyant, Julie Kalansky, Anna K. Panorska
article en

Abstract

In the western United States, much of the annual precipitation falls during relatively few storm events. When precipitation is measured as daily (or hourly, etc.) accumulations, these events appear as sequences of various durations. We describe a trivariate probability distribution and assert that it provides a natural description for the durations, magnitudes, and maximum intensities of such events. We show that this distribution, in its most straightforward application, indeed describes precipitation events composed of daily observations at most long-term weather stations across the western United States, allowing for a flexible assessment of specific event probabilities and return periods with respect to their three defining components. This characterization opens the door to further understanding of risk associated with out-of-sample meteorological events.

Scientific ReportsVol. 15(1)
University of Nevada, Reno (US), Scripps Institution of Oceanography (US)
Department of Water Resources
Climate action
Openalex Percentile: Top 98%
Hydrology and Drought Analysis
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