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Analyses Through the Metastatistical Extreme Value Distribution Identify Contributions of Tropical Cyclones to Rainfall Extremes in the Eastern United States
Journal article   Open access   Peer reviewed

Analyses Through the Metastatistical Extreme Value Distribution Identify Contributions of Tropical Cyclones to Rainfall Extremes in the Eastern United States

Arianna Miniussi, Gabriele Villarini and Marco Marani
Geophysical research letters, Vol.47(7), e2020GL087238
04/16/2020
DOI: 10.1029/2020GL087238
url
https://doi.org/10.1029/2020GL087238View
Published (Version of record) Open Access

Abstract

Tropical cyclones (TCs) generate extreme precipitation with severe impacts across large coastal and inland areas, calling for accurate frequency estimation methods. Statistical approaches that take into account the physical mechanisms responsible for these extremes can help reduce the estimation uncertainty. Here we formulate a mixed‐population Metastatistical Extreme Value Distribution explicitly incorporating non‐TC and TC‐induced rainfall and evaluate its implications on long series of daily rainfall for six major U.S. urban areas impacted by these storms. We find statistically significant differences between the distributions of TC‐ and non‐TC‐related precipitation; moreover, including mixtures of distributions improves the estimation of the probability of extreme precipitation where TCs occur more frequently. These improvements are greater when rainfall aggregated over durations longer than one day are considered. Plain Language Summary The accurate estimation of the frequency of extreme rainfall has broad implications in designing mitigation measures, policy making, risk management, geology/geomorphology, insurance and reinsurance, and water‐borne disease prevention and management. In many parts of the world tropical cyclones play a significant role in generating heavy rainfall, but the evaluation of their contribution to the frequency of extremes is problematic, due to the low number of tropical cyclones in the measured historical record. Here, we introduce a new statistical tool that explicitly includes tropical cyclone rainfall together with rainfall generated by other physical mechanisms and provides a way to maximize the information that can be extracted from available observations. When applied to locations on the U.S. eastern seaboard, we find that this method significantly improves estimates of extreme rainfall. Key Points We study rainfall extremes from tropical cyclone (TC) and non‐TC rainfall mixtures at six U.S. locations The Metastatistical Extreme Value Distribution based on mixed populations outperforms single population formulations The advantage of considering different rainfall sources is greater for 3‐day cumulative rainfall
Metastatistical Extreme Value Distribution Mixed distributions Rainfall extremes Tropical Cyclones Tropical Cyclones rainfall

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