A multivariate model of sea storms using copulas

By Carlo De Michele, G. Salvadori, G. Passoni, R. Vezzoli. 2007

224 citations recorded by the source corpus.

Subfields: Atmospheric Science, Earth-Surface Processes, Oceanography

View Paper: https://www.semanticscholar.org/paper/f04d7983f39a779264fa83efdc656307dca0b931

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Earth-Surface Processes

In Oceanography

Citations in the corpus

A copula-based joint deficit index for droughts. (2010, background): “…runoff (Kao and Govindaraju, 2007b), multivariate L-moment homogeneity test (Chebana and Ouarda, 2007), remote sensing data (Gebremichael and Krajewski, 2007), tail dependence (Poulin et al., 2007), rainfall IDF curves (Singh and Zhang, 2007), and sea storm analysis (De Michele et al., 2007).”
Data‐based analysis of bivariate copula tail dependence for drought duration and severity (2013, methodology): “Copula functions have been applied extensively in hydrological frequency analysis and simulation (De Michele and Salvadori, 2003; Salvadori and De Michele, 2004; Chebana and Ouarda, 2011; De Michele et al., 2007; Kao and Govindaraju, 2008; Lee and Salas, 2011).”
Fitting bivariate copulas to the dependence structure between storm characteristics: A detailed analysis based on 105 year 10 min rainfall (2010, methodology): “The work of Salvadori [2004], Salvadori and De Michele [2004], and Salvadori et al. [2007] (and references therein) provides a nice theoretical framework for copula-based frequency analysis of storms, floods, droughts, etc., and several of these concepts have been applied in studies of De Michele e”
Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation (2012, methodology): “Recent developments in statistical hydrology have shown the great potential of copulas for the construction of multivariate cumulative distribution functions (CDFs) and for carrying out a multivariate frequency analysis ( Favre et al. , 2004; Salvadori , 2004; Salvadori and De Michele , 2004, 2007; ”
Copulas and Its Application in Hydrology and Water Resources (2018, background): “2009), drought frequency analysis (Shiau 2006; Kao and Govindaraju 2010; Song and Singh 2010), sea storm analysis (Michele et al. 2007), streamflow simulation (Chen et al.”
Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: a multivariate statistical approach based on Copula functions (2012, methodology): “Wong et al. (2010) performed drought analyses and De Michele et al. (2007) analysed sea storms based on a trivariate Copula model by taking the parameters wave height, storm duration and storm direction into account.”
On the return period and design in a multivariate framework (2011, background): “G. Salvadori et al.: Return period and design For a thorough theoretical introduction to copulas see Joe (1997); Nelsen (2006); for a practical approach see Salvadori et al. (2007); Jaworski et al. (2010).”