estimate all the parameters for the G-Wex model of precipitation
fit.GWex.prec(objGwexObs, parMargin, listOption = NULL)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.
object of class GwexObs
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)
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
Guillaume Evin
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.