ensembleMOSlognormal: Log-normal EMOS modeling
In ensembleMOS: Ensemble Model Output Statistics

Description Usage Arguments Details Value References See Also Examples

Fits a log-normal EMOS model to ensemble forecasts for specified dates.

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ensembleMOSlognormal(ensembleData, trainingDays, consecutive = FALSE,
                     dates = NULL, control = controlMOSlognormal(),
                     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 controlMOStruncnormal. For details and default values, see controlMOStruncnormal.
`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 log-normal model is fit by ensembleMOSlognormal:

Y ~ LN(μ, σ)

where LN denotes the log-normal distrbution with meanlog parameter μ and scalelog parameter σ, 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 + … + b_m X_m of the ensemble forecats, and the variance is a linear function c + d S^2. For transformations between μ, σ and mean and variance of the log-normal distribution, see Baran and Lerch (2015). See ensembleMOSlognormal for details. 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`	The fitted parameters for the variance, see details.

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.

controlMOSlognormal, fitMOSlognormal

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)