ensembleBMA (version 2.1)

plotBMAforecast: Surface plots for BMA forecasts.

Description

Produces contour, image, or perspective plot of a BMA forecast using prediction on a grid.

Usage

plotBMAforecast( forecast, longitude, latitude, nGrid = 65,
                 type = c("image", "contour", "persp"), ..., 
                 interpolate = FALSE, span = 0.75, map = NULL)

Arguments

forecast

Numeric vector of forecasts.

longitude

Numeric vector giving the longitude of each forecast location.

latitude

Numeric vector giving the latitude of each forecast location.

nGrid

Number of grid points for loess interpolation. (Binning and interpolation are done on an nGrid by nGrid grid).

type

A character string indicating the desired plot type. Should be one of either "contour", "image", or "persp".

...

Additional arguments to be passed to the plotting method.

interpolate

A logical variable indicating whether or not a loess fit should be used to interpolate the data to points on a grid. The default is to determine grid values by binning, rather than interpolation.

span

Smoothing parameter for loess (used only when interpolate = TRUE). The default value is 0.75, which is the default for loess.

map

A logical value indicating whether or not to include a map outline. The default is to include an outline if type = "image" and the fields library is loaded.

Value

• An image, contour, or perspective plot of the forecast.

Details

If the fields library is loaded, a legend (and optionally a map outline) will be included in image plots.

References

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. 516, Department of Statistics, University of Washington, August 2007.

See Also

quantileForecastBMA

Examples

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)

bmaFit <- ensembleBMA( srftData, date = "2004012900",  
             model = "normal",trainingRule = list(length = 25, lag = 2))

  bmaForc <- quantileForecastBMA( bmaFit, srftData, date = "2004021400",  
                                  quantiles = c(.1, .5. .9))

  obs <- srftData$date == "2004012900"
  lat <- srftData$latitude[obs]
  lon <- srftData$longitude[obs]

  plotBMAforecast( bmaForc[,"0.5"], lat, lon, 
                   type = "contour", interpolate = TRUE)
  title("Median Forecast")

  plotBMAforecast( srftData$obs[obs], lat, lon, 
                  type = "contour", interpolate = TRUE)
  title("Observed Surface Temperature")

  data(srftGrid)

  memberLabels <- c("CMCG","ETA","GASP","GFS","JMA","NGPS","TCWB","UKMO")
 
  srftGridData <- ensembleData(forecasts = srftGrid[,memberLabels],
      latitude = srftGrid[,"latitude"], longitude = srftGrid[,"longitude"])

  gridForc <- quantileForecastBMA( bmaFit, srftGridData, 
                    date = "2004021400", quantiles = c( .1, .5, .9))

  library(fields)

  plotBMAforecast(gridForc[,"0.5"],lon=srftGridData$lon,
     lat=srftGridData$lat,type="image",col=rev(rainbow(100,start=0,end=0.85)))
  title("Median Grid Forecast for Surface Temperature", cex = 0.5)

  probFreeze <- cdfBMA( bmaFit, srftGridData,  date = "2004021400", 
                             value = 273.15)

  plotBMAforecast(probFreeze, lon=srftGridData$lon, lat=srftGridData$lat,
                   type="image",col=gray((32:0)/32))
  title("Probability of Freezing", cex = 0.5)