Description Usage Arguments Details Value References See Also Examples
Fits a EMOS model to ensemble forecasts. Allows specification of a model, training rule, and forecasting dates.
1 2 3 |
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 |
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.
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
.
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.
trainingData
,
ensembleMOSnormal
,
ensembleMOStruncnormal
,
ensembleMOSlognormal
,
ensembleMOScsg0
,
ensembleMOSgev0
,
controlMOSnormal
,
controlMOStruncnormal
,
controlMOSlognormal
,
controlMOScsg0
,
controlMOSgev0
,
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | 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)
|
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 2007122700 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
25.06 0.27 0.00 0.44 0.00 0.21
T2.ngps T2.tcwb T2.ukmo
0.00 0.00 0.00
0.97 0.07
modeling for date 2007122800 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
28.88 0.25 0.11 0.32 0.00 0.21
T2.ngps T2.tcwb T2.ukmo
0.00 0.00 0.01
1.08 0.26
modeling for date 2007122900 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
24.56 0.27 0.01 0.36 0.00 0.27
T2.ngps T2.tcwb T2.ukmo
0.00 0.00 0.00
1.00 0.18
modeling for date 2007123000 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
-1.98 0.35 0.21 0.14 0.00 0.24
T2.ngps T2.tcwb T2.ukmo
0.06 0.00 0.00
1.2 0.0
modeling for date 2007123100 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
-2.41 0.34 0.22 0.15 0.00 0.23
T2.ngps T2.tcwb T2.ukmo
0.05 0.00 0.00
1.14 0.00
modeling for date 2008010100 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
-17.42 0.26 0.19 0.22 0.00 0.24
T2.ngps T2.tcwb T2.ukmo
0.15 0.00 0.00
1.14 0.00
modeling for date 2008010200 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
-18.42 0.25 0.20 0.22 0.01 0.22
T2.ngps T2.tcwb T2.ukmo
0.14 0.01 0.02
1.12 0.00
modeling for date 2008010300 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
-7.85 0.28 0.21 0.17 0.01 0.21
T2.ngps T2.tcwb T2.ukmo
0.15 0.00 0.00
1.42 0.00
modeling for date 2008010400 ...
(Intercept) T2.gfs T2.cmcg T2.eta T2.gasp T2.jma
4.55 0.32 0.15 0.12 0.04 0.25
T2.ngps T2.tcwb T2.ukmo
0.08 0.00 0.03
1.49 0.00
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