Description Usage Arguments Details Value References See Also Examples
Specifies a list of values controling the lognormal 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 lognormal model is fit by ensembleMOSlognormal
:
Y ~ LN(μ, σ)
where LN denotes the lognormal distrbution with meanlog
parameter μ and scalelog
parameter σ, see
Lognormal. The model is parametrized such that the mean value of
the lognormal 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 lognormal distribution, see Baran and Lerch (2015).
See ensembleMOSlognormal for details.
Note that in case of scoringRule = "log"
, forecast cases in the
training period with observation values of 0 are ignored in the model
estimation as 0 is not included in the support of the lognormal
distribution.
A list whose components are the input arguments and their assigned values.
S. Baran and S. Lerch, Lognormal distribution based Ensemble Model Output Statistics models for probabilistic windspeed forecasting. Quarterly Journal of the Royal Meteorological Society 141:2289–2299, 2015.
ensembleMOSlognormal
,
fitMOSlognormal
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("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,
dates = "2008010100",
control = controlMOSlognormal(maxIter = as.integer(100),
scoringRule = "log",
optimRule = "BFGS",
coefRule= "none",
varRule = "square"))

Loading required package: ensembleBMA
Loading required package: chron
Loading required package: evd
Attaching package: ‘ensembleMOS’
The following objects are masked from ‘package:ensembleBMA’:
brierScore, cdf, crps, quantileForecast, trainingData
modeling for date 2008010100 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.63 0.15 0.24 0.25 0.12
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.56 0.22 0.26 0.66
2.29 0.00
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