Computes quantiles for the probability distribution function (PDF) for ensemble forecasting models.
quantileForecast( fit, ensembleData, quantiles = 0.5, dates=NULL, ...)A vector of forecasts corresponding to the desired quantiles.
A model fit to ensemble forecasting data.
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.
The vector of desired quantiles for the PDF of the BMA mixture model.
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.
This method is generic, and can be applied to any ensemble forecasting
model.
Note the model may have been applied to a power transformation of the data,
but that information is included in the input fit, and
the output is transformed appropriately.
This can be used to compute prediction intervals for the PDF.
For the bivariate normal model for wind speed and direction, the
CRPS is computed for the marginal wind speed distribution.
A. E. Raftery, T. Gneiting, F. Balabdaoui and M. Polakowski, Using Bayesian model averaging to calibrate forecast ensembles, Monthly Weather Review 133:1155--1174, 2005.
J. M. Sloughter, A. E. Raftery, T. Gneiting and C. Fraley, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review 135:3209--3220, 2007.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
ensembleBMA: An R Package for Probabilistic Forecasting
using Ensembles and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, 2007 (revised 2010).
C. Fraley, A. E. Raftery, T. Gneiting, Calibrating Multi-Model Forecast Ensembles with Exchangeable and Missing Members using Bayesian Model Averaging, Monthly Weather Review 138:190--202, 2010.
J. M. Sloughter, T. Gneiting and A. E. Raftery, Probabilistic wind speed forecasting using ensembles and Bayesian model averaging, Journal of the American Statistical Association, 105:25--35, 2010.
ensembleBMA,
fitBMA,
cdf
data(ensBMAtest)
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")
if (FALSE) # R check
tempTestFit <- ensembleBMAnormal( tempTestData, trainingDays = 30)
tempTestForc <- quantileForecast( tempTestFit, tempTestData)
if (FALSE) # R check
data(srft)
labels <- c("CMCG","ETA","GASP","GFS","JMA","NGPS","TCWB","UKMO")
srftData <- ensembleData( forecasts = srft[ ,labels],
dates = srft$date,
observations = srft$obs,
latitude = srft$lat,
longitude = srft$lon,
forecastHour = 48,
initializationTime = "00")
srftFit <- ensembleBMAnormal(srftData, date = "2004013100",
trainingDays = 25)
data(srftGrid)
srftGridData <- ensembleData(forecasts = srftGrid[ ,labels],
latitude = srftGrid$lat,
longitude = srftGrid$lon,
forecastHour = 48,
initializationTime = "00")
srftGridForc <- quantileForecast( srftFit, srftGridData,
date = "2004013100")
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