Description Usage Arguments Value References See Also Examples
Fits an EMOS model to a given training set.
1 2 
ensembleData 
An 
control 
A list of control values for the fitting functions. The corresponding
control function has to be chosen in accordance with the selected

model 
A character string describing the EMOS model to be fit.
Current choices are 
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 
A list with estimated coefficient values. The specific content depends on
the chosen model
.
Gaussian (normal) EMOS model:
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.
Truncated (normal) EMOS model:
T. L. Thorarinsdottir and T. Gneiting,
Probabilistic forecasts of wind speed:
Ensemble model output statistics by using
heteroscedastic censored regression.
Journal of the Royal Statistical Society Series A
173:371–388, 2010.
Lognormal EMOS model:
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.
Censored and shifted gamma EMOS model:
M. Scheuerer and T. M. Hamill, Statistical postprocessing of ensemble precipitation
forecasts by fitting censored, shifted gamma distributions.
Monthly Weather Review 143:4578–4596, 2015.
S. Baran and D. Nemoda, Censored and shifted gamma distribution based EMOS
model for probabilistic quantitative precipitation forecasting.
Environmetrics 27:280–292, 2016.
Censored generalized extreme value distribution EMOS model:
M. Scheuerer, Probabilistic quantitative precipitation forecasting using ensemble
model output statistics. Quarterly Journal of the Royal Meteorological
Society 140:1086–1096, 2014.
fitMOSnormal fitMOStruncnormal fitMOSlognormal fitMOScsg0 fitMOSgev0 controlMOSnormal controlMOStruncnormal controlMOSlognormal controlMOScsg0 controlMOSgev0
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("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")
tempTrain < trainingData(tempTestData, trainingDays = 30,
date = "2008010100")
tempTrainFit < fitMOS(tempTrain, model = "normal")
## equivalent to
## tempTrainFit < fitMOSnormal(tempTrain)

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
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