Fits a EMOS model to ensemble forecasts.
Allows specification of a model, training rule, and forecasting dates.
Usage
ensembleMOS(ensembleData, trainingDays, consecutive = FALSE,
dates = NULL, control = NULL, warmStart = FALSE,
model = NULL, exchangeable = NULL)
Arguments
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.
The default is controlMOSnormal() for Gaussian (normal) models.
warmStart
If TRUE, then starting values for parameters in optimization
are set to the estimates of the preceding date's fit.
model
A character string describing the EMOS model to be fit.
Current choices are "normal", typically used for temperature
or pressure data.
For specific details on model fitting see ensembleMOSnormal
exchangeable
A numeric or character vector or factor indicating groups of
ensemble members that are exchangeable (indistinguishable).
The model fit will have equal weights and parameters within each
group.
The default determines exchangeability from ensembleData.
Details
If dates are specified in dates that cannot be forecast
with the training rule, the corresponding EMOS model parameter
outputs will be missing (NA) but not NULL.
The training rule uses the number of days corresponding to its
length regardless of whether or not the dates are consecutive.
Value
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
ensembleMOSnormal
for details.
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.
providing 
.