Analysis of extreme wind gusts using a high-resolution Australian Regional Reanalysis

Rafei, Moutassem El; Sherwood, S. C.; Evans, Jason P.; Ji, Fei

doi:10.1016/j.wace.2022.100537

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…Previous studies, as highlighted by Brown and Dowdy (2019), have demonstrated that coarse resolution reanalysis products can capture moderate gusts (speeds <20 m/s), but struggle to represent strong and damaging gusts (speeds ≥ 25 m/s) or resolve regional variations that might affect superstation results. See (Jakob et al., 2017; Su et al., 2019, 2021) for details on the performance of high‐resolution products in representing wind fields, and (El Rafei, Sherwood, Evans, & Ji, 2023) for a detailed analysis of extreme wind gusts in the BARRA‐SY product.…”

Section: Methodsmentioning

confidence: 99%

Biases in Estimating Long‐Term Recurrence Intervals of Extreme Events Due To Regionalized Sampling

Rafei

Sherwood

Evans

et al. 2023

Geophysical Research Letters

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Preparing for environmental risks requires estimating the frequencies of extreme events, often from data records that are too short to confirm them directly. This requires fitting a statistical distribution to the data. To improve precision, investigators often pool data from neighboring sites into single samples, referred to as “superstations,” before fitting. We demonstrate that this technique can introduce unexpected biases in typical situations, using wind and rainfall extremes as case studies. When the combined locations have even small differences in the underlying statistics, the regionalization approach gives a fit that may tend toward the highest levels suggested by any of the individual sites. This bias may be large or small compared to the sampling error, for realistic record lengths, depending on the distribution of the quantity analyzed. The results of this analysis indicate that previous analyses could potentially have overestimated the likelihood of extreme events arising from natural weather variability.

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Section: Methodsmentioning

confidence: 99%

Biases in Estimating Long‐Term Recurrence Intervals of Extreme Events Due To Regionalized Sampling

Rafei

Sherwood

Evans

et al. 2023

Geophysical Research Letters

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“…This comparison shows that the computed results agree well with the Australian standard and implies that the standard may have overestimated the wind gust hazard for South Australia. Incidentally, a recent study (El Rafei et al 2023) on the wind gust hazard in New South Wales, Australia, using high-resolution Wang, On reducing bias at high return levels for extreme value analysis Australian regional reanalysis found that using k = 0.10 overestimates the 500-year ARI gust speeds, when compared to that using variable k values, by approximately 4% for non-synoptic and 2.5% for synoptic events.…”

Section: Shape Parameters For Combined Wind Hazardmentioning

confidence: 99%

On reducing bias at high return levels for extreme value analysis

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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Estimates of extremal return levels at high average recurrence intervals (ARIs) are strongly dependent on the shape parameter of the statistical model. Because of scarcity in occurrences, however, many existing extremal data span only a few decades, often resulting in large bias and uncertainty in the estimated shape parameter of the extreme hazard model. This in turn leads to unreliable predicted extreme values at high ARIs. A common approach to ameliorate this shortcoming is the 'super-station' (or station-year) approach which extends the length of record and should reduce the uncertainty in high ARIs. However, the problem of predicted bias remains for return levels beyond the record length of the super-station.This paper illustrates a statistical method that provides a mechanism to obtain a hazard model that produces return levels at high ARIs with reduced bias. For an ensemble of independently collected records from a number of observational sites, the method makes use of the maximum recorded value of each of the extremal data of the ensemble, as shown in Figure 1 as the starred points (green points represent synoptic and red points nonsynoptic wind gusts). The logarithmically transformed probability of the maximum recorded value at a site is shown to follow the Gumbel (Type I extreme-value) distribution, therefore multiple, say m, sites provide a sample of size m transformed probabilities of extreme values, each from a distinct site. The sample can be treated as being drawn from a Gumbel distribution, irrespective of the underlying hazard-generating mechanisms or the statistical hazard models. The method is demonstrated by an analysis of the extreme wind gust data in South Australia. The results are compared to the specifications in the Australian standard AS/NZS 1170.2:2021 and indicates that the standard may have overestimated the wind gust hazard, hence the specified design wind speeds may fall on the conservative side for South Australia.

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The application of the analysis framework for compound extreme event dependencies in China

Wei,

Gao,

Feng

et al. 2024

River

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The escalation of compound extreme events has resulted in noteworthy economic and property losses. Recognizing the intricate interconnections among these events has become imperative. To tackle this challenge, we have formulated a comprehensive framework for the systematic analysis of their dependencies. This framework consists of three steps. (1) Define extreme events using Mahalanobis distance thresholds. (2) Represent dependencies among multiple extreme events through a point process‐based method. (3) Verify dependencies with residual tail coefficients, determining the final dependency structure. Applying this framework to assess the extreme dependence of precipitation on wind speed and temperature in China, revealed four distinct dependency structures. In northern, Jianghuai, and southern China, precipitation heavily relies on wind speed, while temperatures maintain relative independence. In northeastern and northwestern China, precipitation exhibits relative independence, yet a notable dependence exists between temperatures and wind speed. In southwestern China, precipitation strongly depends on temperature, while wind speed remains relatively independent. The Qinghai–Tibet Plateau region displays a significant dependence relationship among precipitation, wind speed, and temperature, with weaker dependence between extreme wind speed and temperature. This framework is instrumental for analyzing dependencies among extreme values in compound events.

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Analysis of extreme wind gusts using a high-resolution Australian Regional Reanalysis

Cited by 9 publications

References 39 publications

Biases in Estimating Long‐Term Recurrence Intervals of Extreme Events Due To Regionalized Sampling

Biases in Estimating Long‐Term Recurrence Intervals of Extreme Events Due To Regionalized Sampling

On reducing bias at high return levels for extreme value analysis

The application of the analysis framework for compound extreme event dependencies in China

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