  A transition to renewable energy is needed to mitigate climate change. In
Europe, this transition has been led by wind energy, which is one of the
fastest growing energy sources. However, energy demand and production are
sensitive to meteorological conditions and atmospheric variability at multiple
time scales. To accomplish the required balance between these two variables,
critical conditions of high demand and low wind energy supply must be
considered in the design of energy systems. We describe a methodology for
modeling joint distributions of meteorological variables without making any
assumptions about their marginal distributions. In this context, Gaussian
copulas are used to model the correlated nature of cold and weak-wind events.
The marginal distributions are modeled with logistic regressions defining two
sets of binary variables as predictors: four large-scale weather regimes and
the months of the extended winter season. By applying this framework to ERA5
data, we can compute the joint probabilities of co-occurrence of cold and
weak-wind events on a high-resolution grid (0.25 deg). Our results show that a)
weather regimes must be considered when modeling cold and weak-wind events, b)
it is essential to account for the correlations between these events when
modeling their joint distribution, c) we need to analyze each month separately,
and d) the highest estimated number of days with compound events are associated
with the negative phase of the North Atlantic Oscillation (3 days on average
over Finland, Ireland, and Lithuania in January, and France and Luxembourg in
February) and the Scandinavian Blocking pattern (3 days on average over Ireland
in January and Denmark in February). This information could be relevant for
application in sub-seasonal to seasonal forecasts of such events.
