Description Usage Arguments Details Value References See Also Examples

Fits a censored generalized extreme value distribution EMOS model to a given training set.

1 2 | ```
fitMOSgev0(ensembleData, control = controlMOSgev0(),
exchangeable = NULL)
``` |

`ensembleData` |
An |

`control` |
A list of control values for the fitting functions specified via the function controlMOSgev0. For details and default values, see controlMOSgev0. |

`exchangeable` |
An optional numeric or character vector or factor indicating groups of
ensemble members that are exchangeable (indistinguishable).
The models have equal EMOS coefficients within each group.
If supplied, this argument will override any specification of
exchangeability in |

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.

`controlMOSgev0`

,
`ensembleMOSgev0`

,

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
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")
prcpTrain <- trainingData(prcpTestData, trainingDays = 30,
date = "2008010100")
prcpTestFit <- fitMOSgev0(prcpTrain)
``` |

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