In real applications, associations between variables are often non-linear and data commonly exhibit strong asymmetries and/or heavy tails. Copula models enable to create the joint distribution of vectors of random variables independently of their marginal distributions. This paper aims to analyse and characterise the dependence between daily maximum wind speeds, X, observed in Portugal and simulated daily maximum wind speeds, Y, produced by a numerical-physical model. One of the major benefits of using simulated data is their availability at high spatial and temporal resolutions contrarily to observed data, which are commonly scarce. The main problem is that the simulated and the observed winds, in some stations, do not match well and tend to differ mostly in the right tail. Consequently, it is very important to understand the dependence between X and Y. The ultimate purpose is to calibrate the simulated data and bring it in line with observed data. That offers practitioners richer data sources. The results showed that, in the overall, Gamma and Lognormal are the most suitable marginal distributions for our data and Gumbel copula is the most adequate to model the dependence structure. Finally, the classical modelling is compared with a Bayesian approach.

在实际应用中，变量之间的关联通常是非线性的，数据通常表现出很强的不对称性和/或繁重的尾部。Copula模型能够创建随机变量向量的联合分布，而与它们的边际分布无关。本文旨在分析和表征葡萄牙观测到的每日最大风速X与模拟每日最大风速Y之间的相关性，由数值物理模型产生。使用模拟数据的主要好处之一是它们在高空间和时间分辨率下的可用性，与通常稀缺的观测数据相反。主要的问题是，在某些站点中，模拟风和实测风的匹配度不高，并且在右尾部往往存在较大差异。因此，了解X和Y之间的依赖关系非常重要。最终目的是校准模拟数据并使之与观察到的数据保持一致。这为从业人员提供了更丰富的数据源。结果表明，总体而言，Gamma和对数正态分布是最适合我们数据的边际分布，而Gumbel copula最适合于建模依存结构。最后，将经典建模与贝叶斯方法进行比较。