plotfields: Plot of weather fields
In ProbForecastGOP: Probabilistic weather forecast using the GOP method

Description Usage Arguments Value Note Author(s) References See Also Examples

Plot weather fields.

1	plotfields(field, x.lim, y.lim, country.outline="US", title)

`field`	numeric square matrix containing the values of the weather field on a grid.
`x.lim`	numeric vector giving the smallest and the largest longitude to be displayed.
`y.lim`	numeric vector giving the smallest and the largest latitude to be displayed.
`country.outline`	character string indicating which country to outline in the plot. Possible values are "US", "world" or "both". If "US" is specified, a medium resolution outline of the US with the states and bodies of water is added to plot. If "world" is specified, a medium resolution of bodies of land and mass of water delimited by the smallest and largest latitude and longitude specified in `x.lim` and `y.lim` is added to the plot. If `country.outline` is set equal to `both`, medium resolution of both the US and of the bodies of land and water enclosed between the specified latitude and longitude are added to the plot. Default value is "US".
`title`	character string with the title for the plot.

The function returns a graphical display of the weather field on a region delimited by the lower and upper bound for the longitude and the latitude.

This function uses the package fields.

Gel, Y., Raftery, A. E., Gneiting, T., Berrocal, V. J. veronica@stat.washington.edu.

Gel, Y., Raftery, A. E., Gneiting, T. (2004). Calibrated probabilistic mesoscale weather field forecasting: The Geostatistical Output Perturbation (GOP) method (with discussion). Journal of the American Statistical Association, Vol. 99 (467), 575–583.

Nychka, D. (2004). The fields package. Available at: http:lib.stat.cmu.edu/R/CRAN/doc/package/fields.pdf.

The package fields for display of spatial data, and US and world for a map of the US and the world.

## Loading data
library(fields)
library(RandomFields)
data(slp)
data(gridlong)
data(gridlat)
data(forecast.grid)
day <- slp$date.obs
id <- slp$id.stat
coord1 <- slp$lon.stat
coord2 <- slp$lat.stat
obs <- slp$obs
forecast <- slp$forecast
coord1.grid <- gridlong$gridded.long
coord2.grid <- gridlat$gridded.lat
forecast.grid <- forecast.grid$gridded.forecast

## Computing the empirical variogram
variogram <- Emp.variog(day,obs,forecast,id,coord1,coord2,cut.points=NULL,
max.dist=NULL,nbins=NULL)

## Estimating parameters
param.est <- Variog.fit(variogram,"exponential",max.dist.fit=NULL,
init.val=NULL,fix.nugget=FALSE)

## Simulating realizations of the weather random field
simul <- Field.sim(obs, forecast, coord1.grid, coord2.grid, forecast.grid,
variog.model="exponential", param.est=c(param.est$nugget,param.est$variance,
param.est$range), n.sim=4, n.displ=0, qt.displ=c(10,50,90))

##Plotting one of the simulated weather random fields
par(mfrow=c(1,1),mai=c(0.8,0.8,0.8,0.8))
plotfields(simul$sim.fields[,,1],x.lim=c(min(coord1.grid),max(coord1.grid)),
y.lim=c(min(coord2.grid),max(coord2.grid)),country.outline="US",title="Simulated weather field")

## Plotting one of the percentiles of the weather field
par(mfrow=c(1,1),mai=c(0.8,0.8,0.8,0.8))
plotfields(simul$pct.fields[,,1],x.lim=c(min(coord1.grid),max(coord1.grid)),
y.lim=c(min(coord2.grid),max(coord2.grid)),country.outline="US",title="10th percentile")