Description Usage Arguments Details Value References See Also Examples
Specifies a list of values controling the Gaussian (normal) EMOS fit of ensemble forecasts.
1 2 3 4 5 6 7 
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 
coefRule 
Method to control nonnegativity of regression estimates. Options are:

varRule 
Method to control nonnegativity of the variance parameters.
Options 
start 
A list of starting parameters, 
maxIter 
An integer specifying the upper limit of the number of iterations used to fit the model. 
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 Gaussian model is fit by ensembleMOSnormal
:
Y ~ N(a + b_1 X_1 + ... + b_m X_m , c + dS^2).
B
is the array of fitted regression coefficients b_1,
…, b_m for each date. See ensembleMOSnormal for details.
A list whose components are the input arguments and their assigned values.
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.
ensembleMOSnormal
,
fitMOSnormal
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  data("ensBMAtest", package = "ensembleBMA")
ensMemNames < c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")
obs < paste("T2", "obs", sep = ".")
ens < paste("T2", ensMemNames, sep = ".")
tempTestData < ensembleData(forecasts = ensBMAtest[,ens],
dates = ensBMAtest[,"vdate"],
observations = ensBMAtest[,obs],
station = ensBMAtest[,"station"],
forecastHour = 48,
initializationTime = "00")
tempTestFit < ensembleMOSnormal(tempTestData, trainingDays = 25,
dates = "2008010100",
control = controlMOSnormal(maxIter = as.integer(100),
scoringRule = "log",
optimRule = "BFGS",
coefRule= "none",
varRule = "square"))

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