quantileForecast {ensembleMOS}

Quantile forecasts at observation locations

Description

Computes quantiles for the probability distribution function (PDF) for ensemble forecasting models.

Usage

quantileForecast(fit, ensembleData, quantiles = 0.5, dates = NULL, ...)

Arguments

fit

A model fit to ensemble forecasting data.

ensembleData

An ensembleData object that includes ensemble forecasts, verification observations and possibly dates. Missing values (indicated by NA) are allowed. This need not be the data used for the model fit, although it must include the same ensemble members.

quantiles

The vector of desired quantiles for the PDF of the EMOS model.

dates

The dates for which the quantile forecasts will be computed. These dates must be consistent with fit and ensembleData. The default is to use all of the dates in fit. If ensembleData does not include dates, they will be inferred from fit and dates.

...

Included for generic function compatibility.

Details

This method is generic, and can be applied to any ensemble forecasting model. This can be used to compute prediction intervals for the PDF.

Value

A matrix of forecasts corresponding to the desired quantiles.

References

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.

See Also

ensembleMOS, fitMOS, cdf

Examples

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, 
                           dates = c("2008010100", "2008010200"),
                           model = "normal")

tempTestForc <- quantileForecast(tempTestFit, tempTestData)