Copula based multisite model for daily precipitation simulation

By András Bárdossy, G. Pegram. 2009

211 citations recorded by the source corpus.

Abstract. From the point of view of multisite stochastic daily rainfall modelling, there are two new ideas introduced in this paper. The first is the use of asymmetrical copulas to model the spatial interdependence structure of the rainfall amounts together with the rainfall occurrences in one relationship. The second is in the evaluation of the (necessary but often ignored) congregating behaviour of the higher values of simulated rainfall; this evaluation is performed by calculating the entropy of the observations at all the near equilateral triangles that can be formed from the sequences at the gauge sites, as a function of their mutual separation distance. It turns out that the model captures the qualities desired and offers a fresh approach to a relatively mature problem in hydrometeorology.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0e4ed63a97d5d1836f60c571625bcedefd4b5410

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 Global and Planetary Change

Citations in the corpus

A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall (2011, methodology): “Some useful contributions in this direction are provided by Salvadori and De Michele [2006], Grimaldi and Serinaldi [2006b], Kao and Govindaraju [2008], Lennartsson et al. [2008], Sun [2008], Bardossy and Pegram [2009] , and Serinaldi [2009] and will be the subject of future research.”
Is southwestern China experiencing more frequent precipitation extremes? (2014, methodology): “These distributions were applied frequently in many other studies (Bardossy and Pegram 2009, Richardson 1981, Wilks 1999, Zhang et al 2012b).”
Fitting bivariate copulas to the dependence structure between storm characteristics: A detailed analysis based on 105 year 10 min rainfall (2010, methodology): “[2008] consider the incorporation of a copula in a hybrid rainfall model, as well as Bárdossy and Pegram [2009]. Also, the work of Kao and Govindaraju [2007a, 2007b] and Serinaldi [2008] contributes to rainfallrelated research. [3] The flexibility offered by copulas is due to the fact that through t”
Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields (2012, background): “Cole and Moore , 2008; Singh, 1997) as they will directly propagate into the predicted variables. The uncertainty propagation in hydrological equations that directly propagate the probability density functions of uncertain model input parameters into the corresponding probability density functions o”
Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data (2014, methodology): “Alternatively, Copulas are able to describe the complex nonlinear dependence structure between variables (Bárdossy and Pegram, 2009).”
Upper tail dependence in rainfall extremes: would we know it if we saw it? (2015, methodology): “…we used Heron’s formula for the calculation of the triangle area A, the acceptance of a suitable triple of points relies on the following criterion (Bárdossy and Pegram 2009): for each of the three pairs of sides in an adopted triangle, we chose that the maximum difference in a pair must be less…”
Stochastic generation of multi-site daily precipitation focusing on extreme events (2018, methodology): “Furthermore, following Bárdossy and Pegram (2009), GWEX also employs the copula theory to introduce a tail dependence between the precipitation amounts simulated at the different stations.”