Description Usage Arguments Details Value References See Also Examples

Fits a log-normal EMOS model to ensemble forecasts for specified dates.

1 2 3 | ```
ensembleMOSlognormal(ensembleData, trainingDays, consecutive = FALSE,
dates = NULL, control = controlMOSlognormal(),
warmStart = FALSE, exchangeable = NULL)
``` |

`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 |

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.
`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`

.

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 variance, see details. |

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.

`controlMOSlognormal`

,
`fitMOSlognormal`

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")
windTestFitLN <- ensembleMOSlognormal(windTestData, trainingDays = 25)
``` |

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