Simulation of non-Gaussian processes using fractile correlation

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A copula-based joint deficit index for droughts. (2010, methodology): “R is evaluated from Spearman’s ri,j using procedures suggested by Phoon et al. (2004) , in which each element qi,j in R is computed by qi,j = 2 sin (pri,j/6).”
Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology (2007, methodology): “Phoon et al. [49] proposed an alternative method, which allows the marginal and dependence parameters to be estimated independently: 1.”
Bivariate simulation using copula and its application to probabilistic pile settlement analysis (2013, background): “The reason is that there is no non-Gaussian simulation technique available to date that can match rank and product-moment correlation simultaneously [25].”
Bivariate distribution models using copulas for reliability analysis (2013, background): “It can be observed from equations (8) and (9) that the coefficient of tail dependence increases as the parameter u increases.”
Trivariate statistical analysis of extreme rainfall events via the Plackett family of copulas (2008, methodology): “For instance, the correlation matrix may not be positive definite if it is constructed from nonparametric procedures using Spearman’s rank correlation coefficient [Phoon et al., 2004].”