Extreme Atlantic Hurricane Probability of Occurrence Through the Metastatistical Extreme Value Distribution

Abstract

The estimation of extreme hurricane probability is hampered by small samples and by limitations in our models of extreme tropical storms. Current best estimates of extreme hurricane probability based on the traditional extreme value theory assume hurricane arrivals to be a homogeneous Poisson process. We reformulate here, for application to Atlantic hurricanes, the Metastatistical Extreme Value Distribution (MEVD) that relaxes this key assumption in the traditional extreme value theory and uses all available observations. The MEVD application to the 1886–2017 observational record improves the accuracy of extreme Atlantic hurricane estimation by about 50% with respect to current best estimates. The MEVD allows trends to emerge above estimation uncertainty: A sliding‐window analysis shows a significant increase in extreme hurricane likelihood in the past century. This trend, previously uncertain and controversial, remains significant after correcting for hurricane underdetection in the presatellite era and when focusing only on the modern observational period (1970–2017).

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

“…The distribution of extremes arising from mixing different populations of ordinary events is described here using a modified Metastatistical Extreme Value Distribution (MEVD) (Marani & Ignaccolo, 2015), which provides flexibility in incorporating the joint effect of different statistical populations and leverages the added value of incorporating physical mechanisms into statistical analysis (Klemeš, 1974). The MEVD relaxes some of the restrictive assumptions of the traditional Extreme Value Theory (EVT) and has been shown to outperform it in a wide range of applications, from daily and hourly rainfall, to remotely sensed precipitation, to hurricane intensities in the Atlantic Ocean, to peak flood flows (Zorzetto et al, 2016; Marra et al, 2018; Zorzetto & Marani, 2019; Zorzetto & Marani, 2020; Schellander et al, 2019; Hosseini, Scaioni, & Marani, 2020; Miniussi et al, 2020). Here we apply the mixed and original formulations of the MEVD to long series of daily rainfall in several American metropolitan areas, which have a high likelihood of being struck by a TC.…”

Bayesian non-asymptotic extreme value models for environmental data

“…In these circumstances the assumption of a time-independent form of the parent distribution can be questionable. Examples of this type of issues can be found in many Earth-system processes and variables, such as rainfall intensity (Marani and Ignaccolo 2015;Marra et al 2018), flood magnitudes (Miniussi et al 2020a), wind speeds, and tropical storm intensities (Hosseini et al 2020). Overall, though mitigated by advanced estimation approaches, the above limitations can have wide implications in the many applications requiring the accurate estimation of large quantiles, i.e.…”

PHEV! The PHysically-based Extreme Value distribution of river flows

“…The GEV distribution is a standard tool [33,34] traditionally applied to samples of block maxima under the assumption that the number of events within each block tends towards infinity [35]. The MEV distribution is instead a recently proposed method to estimate extremes from the features of ordinary events, which is currently gaining momentum [36][37][38][39][40][41][42]. It relaxes the mentioned assumptions which lie at the heart of the GEV distribution and regards as random variables both the number of independent ordinary events occurring in the considered time interval and the parameters of the distribution used to describe their .…”