Compounded Wind Gusts and Maximum Temperature via Semiparametric copula joint density modelling in the risk assessments of power blackouts and air-conditioning demands for major cities in Canada

Abstract

Abstract A semiparametric copula joint framework was proposed to model wind gust speed (WGS) and maximum temperature (MT) in Canada, using Gaussian kernel density estimation (GKDE) with parametric copulas to assess power blackouts (PBs) and heightened air conditioning demands (HACDs). The framework used two extreme sample groups to define extreme pairs at different time lags, i.e., 0 to ± 3 days, annual maximum WGS (AMWGS) and corresponding MT and annual highest MT (AHMT) and corresponding WGS. GKDE outperformed the parametric model in defining marginal distribution. The extreme pairs, except in Calgary and Halifax, showed significant positive correlations inconsistently varying with a time lag for selected cities. AMWGS or AHMT events considered independently would be stressful for all stations due to high estimated quantiles with low univariate RPs. The bivariate events exhibited lower AND-joint RPs with moderate to high design quantiles indicated higher risk of PBs and HACDs, which varied inconsistently with time lags across the station. The bivariate AMWGS-MT events would be stressful in Regina, Quebec City, Ottawa, and Edmonton, while AMWGS-MT events in Toronto, Regina, and Montreal. Conversely, Vancouver poses a lower risk of joint action of pairs AHMT-WGS. These statistics can help plan for community well-being during extreme weather.