fit.GWex.prec {GWEX}

fit.GWex.prec

Description

estimate all the parameters for the G-Wex model of precipitation

Usage

fit.GWex.prec(objGwexObs, parMargin, listOption = NULL)

Arguments

objGwexObs

object of class GwexObs

parMargin

if not NULL, list where each element parMargin[[iM]] corresponds to a month iM=1...12 and contains a matrix nStation x 3 of estimated parameters of the marginal distributions (EGPD or mixture of exponentials)

listOption

list with the following fields:

• th: threshold value in mm above which precipitation observations are considered to be non-zero (=0.2 by default)

• nLag: order of he Markov chain for the transitions between dry and wet states (=2 by default)

• typeMargin: 'EGPD' (Extended GPD) or 'mixExp' (Mixture of Exponentials). 'EGPD' by default

• copulaInt: 'Gaussian' or 'Student': type of dependence for amounts (='Student' by default)

• isMAR: logical value, do we apply a Autoregressive Multivariate Autoregressive model (order 1) =TRUE by default

• is3Damount: logical value, do we apply the model on 3D-amount. =FALSE by default

• nChainFit: integer, length of the runs used during the fitting procedure. =100000 by default

• nCluster: integer, number of clusters which can be used for the parallel computation

Value

a list containing the list of options listOption and the list of estimated parameters listPar. The parameters of the occurrence process are contained in parOcc and the parameters related to the precipitation amounts are contained in parInt. Each type of parameter is a list containing the estimates for each month. In parOcc, we find:

• p01: For each station, the probability of transition from a dry state to a wet state.

• p11: For each station, the probability of staying in a wet state.

• list.pr.state: For each station, the probabilities of transitions for a Markov chain with lag p.

• list.mat.omega: The spatial correlation matrix of occurrences \Omega (see Evin et al., 2018).

In parInt, we have:

• parMargin: list of matrices nStation x nPar of parameters for the marginal distributions (one element per Class).

• cor.int: Matrices nStation x nStation M_0, A, \Omega_Z representing the spatial and temporal correlations between all the stations (see Evin et al., 2018). For the Student copula, dfStudent indicates the \nu parameter.

Author(s)

Guillaume Evin

References

Evin, G., A.-C. Favre, and B. Hingray. 2018. 'Stochastic Generation of Multi-Site Daily Precipitation Focusing on Extreme Events.' Hydrol. Earth Syst. Sci. 22 (1): 655-672. doi.org/10.5194/hess-22-655-2018.