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

Armon

Borga

et al. 2021

Orographic impact on extreme subdaily precipitation is critical for risk management but remains insufficiently understood due to complicated atmosphere‐orography interactions and large uncertainties. We investigate the problem adopting a framework able to reduce uncertainties and isolate the systematic interaction of Mediterranean cyclones with a regular orographic barrier. The average decrease with elevation reported for hourly extremes is found enhanced at subhourly durations. Tail heaviness of 10‐min intensities is negligibly affected by orography, suggesting self‐similarity of the distributions at the convective scale. Orography decreases the tail heaviness at longer durations, with a maximum impact around hourly scales. These observations are explained by an orographically induced redistribution of precipitation toward stratiform‐like processes, and by the succession of convective cores in multihour extremes. Our results imply a breaking of scale‐invariance at subhourly durations, with important implications for natural hazards management in mountainous areas.

“…is the cumulative distribution function of the i-th of S types of ordinary events, and n i is the corresponding average yearly number (Marra et al, 2019;Miniussi et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Orographic Effect on Extreme Precipitation Statistics Peaks at Hourly Time Scales

Armon

Borga

et al. 2021

“…By relying on ordinary events, for which more data are available, this approach decreases the stochastic uncertainties inherent in the realization of extremes (Marra et al., 2018; Zorzetto et al., 2016). Events generated by different types of processes and thus described by distinct distributions, such as midlatitude versus tropical cyclones (or, in our case, Type‐1 vs. Type‐2 ), can be combined to derive a compound distribution for extreme return levels (Marra, Zoccatelli, et al., 2019; Miniussi et al., 2020). This distribution quantifies the yearly exceedance probability

ζ

associated with the precipitation amount

x

as a function of the intensity distributions of the ordinary events (

F_{i = 1, \dots, S}

, where

i

represents the type of process) and the expected value of their yearly number of occurrences (

n_{i}

) such that

ζ (x) ≃ F_{1}^{n_{1}} \cdot F_{2}^{n_{2}} \cdot \dots \cdot F_{S}^{n_{S}}

(Marra, Zoccatelli, et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Toward Narrowing Uncertainty in Future Projections of Local Extreme Precipitation

Armon

Adam

et al. 2021

Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to provide actionable information on impacts and risks at the regional, watershed and city scales relevant for practical applications. Here, we show that the interaction of precipitating systems with local features can constrain the statistical description of extreme precipitation. These observational constraints can be used to project local extremes of low yearly exceedance probability (e.g., 100‐year events) using synoptic‐scale information from climate models, which is generally represented more accurately than the local scales, and without requiring climate models to explicitly resolve extremes. The novel approach, demonstrated here over the south‐eastern Mediterranean, offers a path for improving the predictability of local statistics of extremes in a changing climate, independent of pending improvements in climate models at regional and local scales.

“…Once the cumulative distributions of the ordinary events F ( x , θ j ), where θ j are the distribution parameters, are known for every year j = 1… M , the extreme values cumulative distribution can be written as:

ζ ((), x) = \frac{1}{M} \sum_{j = 1}^{M} F {(x, θ_{j})}^{n_{j}}

, where n j is the number of ordinary events observed in the j th year (Zorzetto et al, 2016). The framework can include any class of distributions for F and allows to consider multiple types of ordinary events, for example, nontropical and tropical cyclones, to derive compound extreme value distributions (Marra et al, 2019; Miniussi et al, 2020). Making use of the full available data record, MEV also largely decreases the parameter estimation uncertainty and the stochastic uncertainty related to the sampling of extremes.…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Extreme Subdaily Precipitation Frequency Analyses Based on Ordinary Events

Borga

Morin

2020

The metastatistical extreme value approach proved promising in the frequency analysis of daily precipitation from ordinary events, outperforming traditional methods based on sampled extremes. However, subdaily applications are currently restrained by two knowledge gaps: It is not known if ordinary events can be consistently examined over durations, and it is not clear to what extent their entire distributions represent extremes. We propose here a unified definition of ordinary events across durations and suggest the simplified metastatistical extreme value formulation for dealing with extremes emerging from the tail, rather than the entire distributions, of ordinary events. This unified framework provides robust estimates of extreme quantiles (<10% error on the 100 yr from a 26 yr long record) and allows representations in which ordinary and extreme events share the scaling exponent. Future applications could improve our knowledge of subdaily extreme precipitation and help investigate the impact of local factors and climatic forcing on their frequency. Plain Language Summary We propose here a unified methodology to quantify the intensity of extreme rainfall of short duration, such as events expected to occur on average once every 100 yr. As opposed to alternative methods in literature, we rely on the simultaneous analysis of all everyday rainfall events, which, being much larger in number than extremes, were shown to provide improved estimates for daily rainfall. We show that, under our approach, the hypothesis of everyday and extreme events being similar enough holds also for short-duration rainfall. Application of our method to 26 yr of data from an individual station reproduces analyses based on more than 150 yr of observations from multiple nearby stations, with less than 10% error on the estimation of rain intensities expected to occur on average once every 100 yr, which are not directly quantifiable from the 26 yr of observations. The proposed methodology could help improve our knowledge of short-duration rainfall extremes, with implications for water resources and risk management, and could help investigate the impact of climate change on extreme rainfall events. et al., 2016). The framework can include any class of distributions for F and allows to consider multiple ©2020. American Geophysical Union. All Rights Reserved.