Description Usage Arguments Details Value References See Also Examples
Specifies a list of values controling the log-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 non-negativity of regression estimates. Options are:
|
varRule |
Method to control non-negativity 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 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.
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 log-normal
distribution.
A list whose components are the input arguments and their assigned values.
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.
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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