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Last data update: 2014.03.03

R: Control parameters for Gaussian (normal) EMOS models

controlMOSnormal

R Documentation

Control parameters for Gaussian (normal) EMOS models

Description

Specifies a list of values controling the Gaussian (normal) EMOS fit of ensemble forecasts.

Usage

controlMOSnormal(scoringRule = c("crps", "log"),
                 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.
`coefRule`	Method to control non-negativity of regression estimates. Options are: ``square'' - The 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 variance parameters supplied to `optim`. See details.
`maxIter`	An integer specifying the upper limit of the number of iterations used to fit the model.

Details

Given an ensemble of size m: X_1, … , X_m, the following Gaussian model is fit by
ensembleMOSnormal

Y_t sim mathcal{N} ≤ft( a + b_1X_1 + cdots + b_mX_m , c + dS^2 ight)

B is the array of fitted regression coefficients b_1, … ,b_m for each date. See ensembleMOSnormal.

Value

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

References

T. Gneiting, A. E. Raftery, A. H. Westveld and T. Goldman, calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 133:1098–1118, 2005.

Examples

  data(ensMOStest)

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

  tempTestData <- ensembleData( forecasts = ensMOStest[,ensMemNames],
                                dates = ensMOStest[,"vdate"],
                                observations = ensMOStest[,"obs"],
                                station = ensMOStest[,"station"],
                                forecastHour = 48,
                                initializationTime = "00")

  tempTestFit1 <- ensembleMOSnormal(tempTestData, trainingDays = 30,
                           control = controlMOSnormal(maxIter = as.integer(100),
                           coefRule= "positive", varRule = "square"))