controlMOSgev0: Control parameters for censored generalized extreme value...
In ensembleMOS: Ensemble Model Output Statistics

Description Usage Arguments Details Value References See Also Examples

Specifies a list of values controling the censored generalized extreme value distribution EMOS fit of ensemble forecasts.

controlMOSgev0(optimRule = c("Nelder-Mead", "L-BFGS-B", "BFGS"),
               coefRule = c("square", "none", "positive"),
               varRule = c("square", "none"),
               start = list(a = NULL, B = NULL, 
                            s = NULL, c = NULL, 
                            d = NULL, q = NULL),
               maxIter = Inf)

`optimRule`	Numerical optimization method to be supplied to `optim`. Options are "BFGS" for the Broyden-Fletcher-Goldfarb-Shanno algorithm, "L-BFGS-B" for a constrained version thereof, and "Nelder-Mead" for the Nelder-Mead method, see optim for details.
`coefRule`	Method to control non-negativity of regression estimates. Options are: `"square"` EMOS coefficients are parameterized as squares and thus gauranteed to be non-negative. `"positive"` finds non-negative coefficents iteratively by setting negative estimates at the current iteration to zero. `"none"` no restriction on the coefficient estimates.
`varRule`	Method to control non-negativity of the scale parameters. Options `"square"` and `"none"` are the same as in `coefRule`.
`start`	A list of starting parameters, `a`, `B`, `s`, `c`, `d` and `q` specifying initial values for the location, scale and shape coefficients supplied to `optim`. See details.
`maxIter`	An integer specifying the upper limit of the number of iterations used to fit the model.

Note that only minimum CRPS estimation is available and chosen by default.

If no value is assigned to an argument, the first entry of the list of possibly choices will be used by default.

Given an ensemble of size m: X_1, … , X_m, the following generalized extreme value distribution EMOS model left-censored at 0 is fit by ensembleMOSgev0:

Y ~ GEV_0(μ,σ,q)

where GEV_0 denotes the generalized extreme value distribution left-censored at zero, with location μ, scale σ and shape q. The model is parametrized such that the mean m is a linear function a + b_1 X_1 + … + b_m X_m + s p_0 of the ensemble forecats, where p_0 denotes the ratio of ensemble forecasts that are exactly 0, and the shape parameter σ is a linear function of the ensemble variance c + d MD(X_1,…,X_m), where MD(X_1,…,X_m) is Gini's mean difference. See ensembleMOSgev0 for details.

A list whose components are the input arguments and their assigned values.

M. Scheuerer, Probabilistic quantitative precipitation forecasting using ensemble model output statistics. Quarterly Journal of the Royal Meteorological Society 140:1086–1096, 2014.

ensembleMOScsg0, fitMOScsg0

data("ensBMAtest", package = "ensembleBMA")

ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")

obs <- paste("PCP24","obs", sep = ".")
ens <- paste("PCP24", ensMemNames, sep = ".")
prcpTestData <- ensembleData(forecasts = ensBMAtest[,ens],
                             dates = ensBMAtest[,"vdate"],
                             observations = ensBMAtest[,obs],
                             station = ensBMAtest[,"station"],
                             forecastHour = 48,
                             initializationTime = "00")
                             
prcpTestFitGEV0 <- ensembleMOSgev0(prcpTestData, trainingDays = 25,
                        dates = "2008010100",
                        control = controlMOSgev0(maxIter = as.integer(100),
                           optimRule = "Nelder-Mead",
                           coefRule= "none", 
                           varRule = "square"))

Loading required package: ensembleBMA
Loading required package: chron
Loading required package: evd

Attaching package: 'ensembleMOS'

The following objects are masked from 'package:ensembleBMA':

    brierScore, cdf, crps, quantileForecast, trainingData


modeling for date 2008010100 ...
(Intercept)   PCP24.gfs  PCP24.cmcg   PCP24.eta  PCP24.gasp   PCP24.jma 
      -0.76        0.22       -0.24        0.27        0.16        0.44 
 PCP24.ngps  PCP24.tcwb  PCP24.ukmo             
       0.17       -0.03        0.32        0.85 
                  
 0.24  1.02 -0.02