ensembleMOSnormal: Gaussian (normal) EMOS modeling
In ensembleMOS: Ensemble Model Output Statistics

Description Usage Arguments Details Value References See Also Examples

Fits a Gaussian (normal) EMOS model to ensemble forecasts for specified dates.

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ensembleMOSnormal(ensembleData, trainingDays, consecutive = FALSE,
                  dates = NULL, control = controlMOSnormal(),
                  warmStart = FALSE, exchangeable = NULL)

`ensembleData`	An `ensembleData` object including ensemble forecasts with the corresponding verifying observations and their dates. Missing values (indicated by `NA`) are allowed.
`trainingDays`	An integer giving the number of time steps (e.g. days) in the training period. There is no default.
`consecutive`	If `TRUE` then the sequence of dates in the training set are treated as consecutive, i.e. date gaps are ignored.
`dates`	The dates for which EMOS forecasting models are desired. By default, this will be all dates in `ensembleData` for which modeling is allowed given the training rule.
`control`	A list of control values for the fitting functions specified via the function controlMOSnormal. For details and default values, see controlMOSnormal.
`warmStart`	If `TRUE`, then starting values for parameters in optimization are set to the estimates of the preceding date's fit.
`exchangeable`	A numeric or character vector or factor indicating groups of ensemble members that are exchangeable (indistinguishable). The modeling will have equal parameters within each group. The default determines exchangeability from `ensembleData`.

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 a vector of fitted regression coefficients: b_1, … ,b_m. Specifically, a,b_1,… , b_m, c, d are fitted to optimize control$scoringRule over the specified training period using optim with method = control$optimRule.

A list with the following output components:

`training`	A list containing information on the training length and lag and the number of instances used for training for each modeling date.
`a`	A vector of fitted EMOS intercept parameters for each date.
`B`	A matrix of fitted EMOS coefficients for each date.
`c,d`	Vectors of the fitted variance parameters for each date, see details.

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.

controlMOSnormal, fitMOSnormal

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)