ensembleMOS {ensembleMOS} | R Documentation |
EMOS modeling
Description
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 |
trainingDays |
An integer giving the number of time steps (e.g. days) in the training period. There is no default. |
consecutive |
If |
dates |
The dates for which EMOS forecasting models are desired.
By default, this will be all dates in |
control |
A list of control values for the fitting functions. The corresponding
control function has to be chosen in accordance with the selected
|
warmStart |
If |
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 |
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 containing information on the training (length, lag and the
number of instances used for training for each modeling date), the
exchangeability, and vectors and/or matrics containing the estimated
regression and variance coefficient values depending on the specified
model
.
References
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.
Log-normal EMOS model:
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.
Censored and shifted gamma EMOS model:
M. Scheuerer and T. M. Hamill, Statistical post-processing 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.
See Also
trainingData
,
ensembleMOSnormal
,
ensembleMOStruncnormal
,
ensembleMOSlognormal
,
ensembleMOScsg0
,
ensembleMOSgev0
,
controlMOSnormal
,
controlMOStruncnormal
,
controlMOSlognormal
,
controlMOScsg0
,
controlMOSgev0
,
Examples
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")
tempTestFit <- ensembleMOS(tempTestData, trainingDays = 25,
model = "normal")
## Same as
## tempTestFit <- ensembleMOSnormal(tempTestData, trainingDays = 25)