# Surface plots for forecast information. Produces contour, image, or perspective plot of a forecast using loess prediction on a grid. ```r plotProbcast( forecast, longitude, latitude, nGrid = 65, type = c("image", "contour", "persp"), ..., interpolate = FALSE, span = 0.75, maps = 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`. - `maps`: 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. ## Returns 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. ## See Also `quantileForecast` ## 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. 516R, Department of Statistics, University of Washington, 2007 (revised 2010). ## Examples ```r 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") ## Not run: # R check bmaFit <- ensembleBMA( srftData, date = "2004012900", trainingDays = 25, model = "normal") bmaForc <- quantileForecast( bmaFit, srftData, date = "2004012900", quantiles = c(.1, .5, .9)) obs <- srftData$date == "2004012900" lat <- srftData$latitude[obs] lon <- srftData$longitude[obs] plotProbcast( bmaForc[,"0.5"], lat, lon, type = "contour", interpolate = TRUE) title("Median Forecast") plotProbcast( srftData$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"], forecastHour = 48, initializationTime = "00") gridForc <- quantileForecast( bmaFit, srftGridData, date = "2004021400", quantiles = c( .1, .5, .9)) library(fields) plotProbcast(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 <- cdf( bmaFit, srftGridData, date = "2004021400", value = 273.15) plotProbcast(probFreeze, lon=srftGridData$lon, lat=srftGridData$lat, type="image",col=gray((32:0)/32)) title("Probability of Freezing", cex = 0.5) ## End(Not run) ```

plotProbcast function