Modelling dependence between observed and simulated wind speed data using copulas

André, Lídia Maria; Bermudez, P. de Zea

doi:10.1007/s00477-020-01866-1

Cited by 4 publications

(1 citation statement)

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“…Wong et al [29] fitted a trivariate copula to drought characteristics, i.e., drought magnitude, duration and intensity, for some Australian rainfall districts. André and de Zea Bermudez [30] analysed and characterised the dependence using copulas between daily maximum wind speeds observed in mainland Portugal and simulate daily maximum wind speeds produced by a numerical physical model. Gouveia-Reis et al [31] investigate the dependence of Madeira's rainfall data and spatial variables (altitude, slope orientation, distance between rain gauges, and distance to the sea) within an extreme value copula approach through an analysis of maximum annual data.…”

Section: Introductionmentioning

confidence: 99%

Bivariate Modelling of a Teleconnection Index and Extreme Rainfall in a Small North Atlantic Island

Espinosa¹,

Portela²,

Filho

et al. 2021

Climate

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This paper explores practical applications of bivariate modelling via copulas of two likely dependent random variables, i.e., of the North Atlantic Oscillation (NAO) coupled with extreme rainfall on the small island of Madeira, Portugal. Madeira, due to its small size (∼740 km2), very pronounced mountain landscape, and location in the North Atlantic, experiences a wide range of rainfall regimes, or microclimates, which hamper the analyses of extreme rainfall. Previous studies showed that the influence of the North Atlantic Oscillation (NAO) on extreme rainfall is at its largest in the North Atlantic sector, with the likelihood of increased rainfall events from December through February, particularly during negative NAO phases. Thus, a copula-based approach was adopted for teleconnection, aiming at assigning return periods of daily values of an NAO index (NAOI) coupled with extreme daily rainfalls—for the period from December 1967 to February 2017—at six representative rain gauges of the island. The results show that (i) bivariate copulas describing the dependence characteristics of the underlying joint distributions may provide useful analytical expressions of the return periods of the coupled previous NAOI and extreme rainfall and (ii) that recent years show signs of increasing climate variability with more anomalous daily negative NAOI along with higher extreme rainfall events. These findings highlight the importance of multivariate modelling for teleconnections of prominent patterns of climate variability, such as the NAO, to extreme rainfall in North Atlantic regions, especially in small islands that are highly vulnerable to the effects of abrupt climate variability.

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Section: Introductionmentioning

confidence: 99%