Semiparametric Copula-Based Stochastic Weather Generator

Stochastic Weather Generators (SWGs) try to replicate the stochastic patterns of climatological variables characterized by high dimensionality, non-normal probability density functions and non-linear dependence relationships. However, conventional SWGs usually typify weather variables with not always justified probability distributions assuming linear dependence between variables. This research proposes an alternative SWG that introduces the advantages of the copula modeling into the replication of stochastic weather patterns. The semiparametric copula-based SWG introduces more flexibility allowing researcher to model non-linear dependence structures independently of the marginals involved. Also, it can better model tail dependence, which would result in a more accurate reproduction of extreme weather events.