ensembleMOStruncnormal: Truncated normal EMOS modeling
In ensembleMOS: Ensemble Model Output Statistics
Description
Usage
Arguments
Details
Value
References
See Also
Examples
Description
Fits a truncated normal EMOS model to ensemble forecasts for
specified dates.
Usage
1
2
3
ensembleMOStruncnormal(ensembleData, trainingDays, consecutive = FALSE,
dates = NULL, control = controlMOStruncnormal(),
warmStart = FALSE, exchangeable = NULL)
Arguments
ensembleData
An ensembleData object including ensemble forecasts with
the corresponding verifying observations and their dates.
Missing values (indicated by NA) are allowed.
trainingDays
An integer giving the number of time steps (e.g. days)
in the training period. There is no default.
consecutive
If TRUE then the sequence of dates in the training set are
treated as consecutive, i.e. date gaps are ignored.
dates
The dates for which EMOS forecasting models are desired.
By default, this will be all dates in ensembleData
for which modeling is allowed given the training rule.
control
A list of control values for the fitting functions specified via the
function controlMOStruncnormal. For details
and default values, see controlMOStruncnormal.
warmStart
If TRUE, then starting values for parameters in optimization
are set to the estimates of the preceding date's fit.
exchangeable
A numeric or character vector or factor indicating groups of
ensemble members that are exchangeable (indistinguishable).
The modeling will have equal parameters within each group.
The default determines exchangeability from ensembleData.
Details
Given an ensemble of size m: X_1, … , X_m, the
following truncated normal model is fit by ensembleMOStruncnormal:
Y ~ N_0(a + b_1 X_1 + ... + b_m X_m, c + dS^2),
where N_0 denotes the normal distribution truncated at zero,
with location a + b_1 X_1 + ... + b_m X_m and squared scale
c + dS^2.
B is a vector of fitted regression coefficients: b_1,
…, b_m. Specifically, a, b_1,…, b_m, c, d are
fitted to optimize
control$scoringRule over the specified training period using
optim with method = control$optimRule.
Value
A list with the following output components:
training
A list containing information on the training length and lag and
the number of instances used for training for each modeling date.
a
A vector of fitted EMOS intercept parameters for each date.
B
A matrix of fitted EMOS coefficients for each date.
c,d
The fitted parameters for the squared scale, see details.
References
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.
See Also
controlMOStruncnormal,
fitMOStruncnormal
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
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")
windTestFitTN <- ensembleMOStruncnormal(windTestData, trainingDays = 25)
Example output
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) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.50 0.00 0.00 0.28 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.38 0.00 0.25 0.00
2.08 0.46
modeling for date 2007122800 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.67 0.00 0.00 0.26 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.32 0.00 0.31 0.00
2.08 0.46
modeling for date 2007122900 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.70 0.00 0.18 0.30 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.20 0.00 0.19 0.00
2.41 0.15
modeling for date 2007123000 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.73 0.09 0.19 0.22 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.29 0.00 0.07 0.00
2.32 0.00
modeling for date 2007123100 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.76 0.11 0.23 0.24 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.23 0.01 0.05 0.00
2.42 0.00
modeling for date 2008010100 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.76 0.11 0.26 0.27 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.18 0.04 0.00 0.00
2.28 0.00
modeling for date 2008010200 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.86 0.10 0.24 0.33 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.18 0.02 0.00 0.00
2.15 0.00
modeling for date 2008010300 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.65 0.00 0.17 0.41 0.01
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.24 0.00 0.06 0.00
2.18 0.00
modeling for date 2008010400 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.61 0.00 0.32 0.46 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.09 0.03 0.02 0.00
2.7 0.0
ensembleMOS documentation built on May 2, 2019, 11:03 a.m.
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Description Usage Arguments Details Value References See Also Examples
Description
Fits a truncated normal EMOS model to ensemble forecasts for specified dates.
Usage
1 2 3 | ensembleMOStruncnormal(ensembleData, trainingDays, consecutive = FALSE,
dates = NULL, control = controlMOStruncnormal(),
warmStart = FALSE, 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 specified via the function controlMOStruncnormal. For details and default values, see controlMOStruncnormal. |
warmStart |
If |
exchangeable |
A numeric or character vector or factor indicating groups of
ensemble members that are exchangeable (indistinguishable).
The modeling will have equal parameters within each group.
The default determines exchangeability from |
Details
Given an ensemble of size m: X_1, … , X_m, the
following truncated normal model is fit by ensembleMOStruncnormal:
Y ~ N_0(a + b_1 X_1 + ... + b_m X_m, c + dS^2),
where N_0 denotes the normal distribution truncated at zero,
with location a + b_1 X_1 + ... + b_m X_m and squared scale
c + dS^2.
B is a vector of fitted regression coefficients: b_1,
…, b_m. Specifically, a, b_1,…, b_m, c, d are
fitted to optimize
control$scoringRule over the specified training period using
optim with method = control$optimRule.
Value
A list with the following output components:
training |
A list containing information on the training length and lag and the number of instances used for training for each modeling date. |
a |
A vector of fitted EMOS intercept parameters for each date. |
B |
A matrix of fitted EMOS coefficients for each date. |
c,d |
The fitted parameters for the squared scale, see details. |
References
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.
See Also
controlMOStruncnormal,
fitMOStruncnormal
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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")
windTestFitTN <- ensembleMOStruncnormal(windTestData, trainingDays = 25)
|
Example output
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) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.50 0.00 0.00 0.28 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.38 0.00 0.25 0.00
2.08 0.46
modeling for date 2007122800 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.67 0.00 0.00 0.26 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.32 0.00 0.31 0.00
2.08 0.46
modeling for date 2007122900 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.70 0.00 0.18 0.30 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.20 0.00 0.19 0.00
2.41 0.15
modeling for date 2007123000 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.73 0.09 0.19 0.22 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.29 0.00 0.07 0.00
2.32 0.00
modeling for date 2007123100 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.76 0.11 0.23 0.24 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.23 0.01 0.05 0.00
2.42 0.00
modeling for date 2008010100 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.76 0.11 0.26 0.27 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.18 0.04 0.00 0.00
2.28 0.00
modeling for date 2008010200 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.86 0.10 0.24 0.33 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.18 0.02 0.00 0.00
2.15 0.00
modeling for date 2008010300 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.65 0.00 0.17 0.41 0.01
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.24 0.00 0.06 0.00
2.18 0.00
modeling for date 2008010400 ...
(Intercept) MAXWSP10.gfs MAXWSP10.cmcg MAXWSP10.eta MAXWSP10.gasp
1.61 0.00 0.32 0.46 0.00
MAXWSP10.jma MAXWSP10.ngps MAXWSP10.tcwb MAXWSP10.ukmo
0.09 0.03 0.02 0.00
2.7 0.0
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