gridForecastBMA {ensembleBMA} | R Documentation |
Computes quantiles for the probability distribution function (PDF) of an ensemble BMA mixture model at a grid of locations.
gridForecastBMA( object, gridData, quantiles = 0.5, date = NULL, popData = NULL, ...)
object |
An ensemble BMA model fit. |
gridData |
A two- or three dimensional array. If two dimensional, the rows represents spatial locations on a grid the columns represent the ensemble forecasts at each location. If three dimensional, the for two dimension represents spatial locations on a grid and the thrid dimension represents the ensemble forecasts at each location. |
quantiles |
The vector of desired quantiles for the PDF of the BMA mixture model. |
date |
For ensembleBMAgamma0 and ensembleBMAnormal objects, there
is an additional date argument, giving a character representation
of the date for which the grid forecast is desired.
date must be chosen from names(object$dateTable) .
|
popData |
For ensembleBMAgamma0 and fitBMAgamma0 objects, there
is an additional popData argument for providing predictors
in the logistic regression for probability of precipitation.
The value of popData (if any) used to create object
should be supplied here.
|
... |
Included for generic function compatibility. |
This method is generic, and can be applied to any ensemble BMA forecasting
model.
Note the model may have been applied to a transformation of the data,
but that information is included in the input object
, and
the output is transformed appropriately.
This can be used to compute confidence intervals for the PDF.
A three dimensional grid in which the first two dimensions represent spatial locations as in the input and the third dimension gives the desired quantile forecasts at each location.
A. E. Raftery, T. Gneiting, F. Balabdaoui and M. Polakowski, Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Monthly Weather Review 133:1155-1174, 2005.
V. J. Berrocal, A. E. Raftery and T. Gneiting, Combining Spatial and Ensemble Information in Probabilistic Weather Forecasts, Technical Report no. 495, Department of Statistics, University of Washington, February 2006.
ensembleBMA
,
fitBMA
,
quantileForecastBMA
data(srft) data(srftGrid) srftData <- ensembleData(dates = srft$date, observations = srft$observation, forecasts = srft[c("CMCG","ETA","GASP","GFS","JMA","NGPS","TCWB","UKMO")]) srftFeb14fit1 <- ensembleBMAnormal( srftData, date = "2004021400", trainingRule = list(length=25, lag=2)) gridForecastBMA(srftFeb14fit1, srftGrid[,1:ensembleSize(srftData)], date = "2004021400") trainingFeb14data <- trainingData( srftData, date = "2004021400", trainingRule = list(length=25, lag=2)) srftFeb14fit2 <- fitBMAnormal( trainingFeb14data) gridForecastBMA(srftFeb14fit2, srftGrid[,1:ensembleSize(srftData)], date = "2004021400")