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Relation between storm characteristics and extreme precipitation statistics over CONUS

Lookup NU author(s): Dr Haider Ali, Professor Hayley Fowler


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© 2023 Elsevier Ltd. In this paper we use a statistical framework to analyze the relation between storm properties and the statistics of extreme precipitation. We identify storm events using a 24-hour dry hiatus separation. We investigate the statistics of the hourly maximum intensity for the heaviest storm events at durations of 1 and 24 h. A two-parameter Weibull distribution is used to represent precipitation frequencies at several stations from a quality-controlled hourly precipitation dataset over the contiguous United States, encompassing seven climate zones. The Spearman correlation between the distribution parameters and a selection of storm properties (duration, intensity, decorrelation time, convective-like ratio) and station properties (elevation and latitude) is used to measure the relation of these properties with the statistics of extreme precipitation. Our results indicate that observed annual maximum hourly precipitation over the entire study area are likely samples from the used distribution, implying that a two-parameter Weibull distribution is suitable for modeling hourly and 24-hour precipitation extremes over the contiguous United States. The spatial variability of shape parameters obtained for hourly events showed lighter tails in the west coast when compared to the rest of CONUS, while the central-north displays heavier tails. We identify statistically significant links (at the 95% confidence level) between storm characteristics connected with the underlying processes (e.g., typical storm duration, typical temporal autocorrelation, proportion of convective-like storms) and the parameters of the distribution. Notably, characteristics typical of convective precipitation, e.g., sharp decorrelation time and high hourly intensity, tend to be related to distributions with heavier tails. These results provide a first step towards linking the characteristics of storms with the local statistics of extremes.

Publication metadata

Author(s): Araujo DSA, Marra F, Ali H, Fowler HJ, Nikolopoulos EI

Publication type: Article

Publication status: Published

Journal: Advances in Water Resources

Year: 2023

Volume: 178

Print publication date: 01/08/2023

Online publication date: 02/07/2023

Acceptance date: 29/06/2023

ISSN (print): 0309-1708

ISSN (electronic): 1872-9657

Publisher: Elsevier Ltd


DOI: 10.1016/j.advwatres.2023.104497


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