Description Usage Arguments Details Value References See Also Examples

Fits a Censored generalized extreme value distribution EMOS model to ensemble forecasts for specified dates.

1 2 3 | ```
ensembleMOSgev0(ensembleData, trainingDays, consecutive = FALSE,
dates = NULL, control = controlMOSgev0(),
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 controlMOSgev0. For details and default values, see controlMOSgev0. |

`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 generalized extreme value distribution EMOS
model left-censored at 0 is fit by `ensembleMOSgev0`

:

* Y ~ GEV_0(μ,σ,q)*

where *GEV_0* denotes the generalized extreme value distribution
left-censored at zero,
with location *μ*, scale *σ* and shape *q*. The model is
parametrized such that the mean *m* is a linear function
*a + b_1 X_1 + … + b_m X_m + s p_0*
of the ensemble forecats, where *p_0* denotes the ratio of ensemble forecasts
that are exactly 0, and the shape parameter *σ* is a linear
function of the ensemble variance *c + d MD(X_1,…,X_m)*, where
*MD(X_1,…,X_m)* is Gini's mean difference.
See ensembleMOSgev0 for details.

`B`

is a vector of fitted regression coefficients: *b_1,
…, b_m*. Specifically, *a, b_1,…, b_m, s, c, d, q* are
fitted to optimize
the mean CRPS over the specified training period using
`optim`

.

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. |

`s` |
A vector of fitted EMOS coefficients for |

`c,d` |
The fitted coefficients for the shape parameter, see details. |

`q` |
Fitted shape parameter, see details. |

M. Scheuerer, Probabilistic quantitative precipitation forecasting using ensemble
model output statistics. *Quarterly Journal of the Royal Meteorological
Society* 140:1086–1096, 2014.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
data("ensBMAtest", package = "ensembleBMA")
ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")
obs <- paste("PCP24","obs", sep = ".")
ens <- paste("PCP24", ensMemNames, sep = ".")
prcpTestData <- ensembleData(forecasts = ensBMAtest[,ens],
dates = ensBMAtest[,"vdate"],
observations = ensBMAtest[,obs],
station = ensBMAtest[,"station"],
forecastHour = 48,
initializationTime = "00")
prcpTestFitGEV0 <- ensembleMOSgev0(prcpTestData, trainingDays = 25,
dates = "2008010100")
``` |

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