ensembleMOS (version 0.8.2)

controlMOSlognormal: Control parameters for log-normal EMOS models

Description

Specifies a list of values controling the log-normal EMOS fit of ensemble forecasts.

Usage

controlMOSlognormal(scoringRule = c("crps", "log"),
                    optimRule = c("BFGS","Nelder-Mead"),
                    coefRule = c("square", "none", "positive"),
                    varRule = c("square", "none"),
                    start = list(a = NULL, B = NULL,
                                 c = NULL, d = NULL),
                    maxIter = Inf)

Arguments

scoringRule

The scoring rule to be used in optimum score estimation. Options are "crps" for the continuous ranked probability score and "log" for the logarithmic score.

optimRule

Numerical optimization method to be supplied to optim. Options are "BFGS" for the Broyden-Fletcher-Goldfarb-Shanno algorithm 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 variance parameters. Options "square" and "none" are the same as in coefRule.

start

A list of starting parameters, a, B, c and d specifying initial values for the intercept coefficient and scale parameters supplied to optim. See details.

maxIter

An integer specifying the upper limit of the number of iterations used to fit the model.

Value

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

Details

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 \(m\): \(X_1, \ldots , X_m\), the following log-normal model is fit by ensembleMOSlognormal:

$$Y ~ LN(\mu, \sigma)$$

where \(LN\) denotes the log-normal distrbution with meanlog parameter \(\mu\) and scalelog parameter \(\sigma\), see Lognormal. The model is parametrized such that the mean value of the log-normal distribution is a linear function \(a + b_1 X_1 + \ldots + b_m X_m\) of the ensemble forecats, and the variance is a linear function \(c + d S^2\). For transformations between \(\mu, \sigma\) and mean and variance of the log-normal distribution, see Baran and Lerch (2015). See ensembleMOSlognormal for details.

Note that in case of scoringRule = "log", forecast cases in the training period with observation values of 0 are ignored in the model estimation as 0 is not included in the support of the log-normal distribution.

References

S. Baran and S. Lerch, Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting. Quarterly Journal of the Royal Meteorological Society 141:2289--2299, 2015.

See Also

ensembleMOSlognormal, fitMOSlognormal

Examples

# NOT RUN {
data("ensBMAtest", package = "ensembleBMA")

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

obs <- paste("MAXWSP10","obs", sep = ".")
ens <- paste("MAXWSP10", ensMemNames, sep = ".")
windTestData <- ensembleData(forecasts = ensBMAtest[,ens],
                             dates = ensBMAtest[,"vdate"],
                             observations = ensBMAtest[,obs],
                             station = ensBMAtest[,"station"],
                             forecastHour = 48,
                             initializationTime = "00")

windTestFitLN <- ensembleMOSlognormal(windTestData, trainingDays = 25,
                                      dates = "2008010100",
                                      control = controlMOSlognormal(maxIter = as.integer(100),
                                                                    scoringRule = "log",
                                                                    optimRule = "BFGS",
                                                                    coefRule= "none", 
                                                                    varRule = "square"))
# }