ModelGrid: Transform model data into an array

Description

This function takes output from ReadGrib or DODSGrab and produces an array with dimensions: levels x variables x longitudes x latitudes. This greatly reduces the size of the data set as well as makes it easier to manipulate. The data must be in a regular latitude/longitude grid (like the GFS model, for example).

Usage

ModelGrid(model.data, resolution, 
    levels = NULL, variables = NULL, 
    model.domain = NULL)

Arguments

model.data

Output from ReadGrib.

resolution

Resolution of grid, in degrees if grid.type = "latlon", in kilometers if grid.type = "cartesian", as a 2 element vector c(East-West, North-South).

levels

The model levels to include in the grid, if NULL, include all of them.

variables

The model variables to include in grid, if NULL, include all of them.

model.domain

A vector c(LEFT LON, RIGHT LON, TOP LAT, BOTTOM LAT) of the region to include in output. If NULL, include everything.

Value

An array of dimensions levels x variables x lon x lat; each level x variable contains the model grid of data from that variable and level

Vector of longitudes

Vector of latitudes

variables

The variables contained in the grid

levels

The levels contained in the grid

model.run.date

When the forecast model was run

fcst.date

The date of the forecast

Details

If you set the spacing of lon.grid and/or lat.grid coarser than the downloaded model grid, you can reduce the resolution of your model, possibly making it easier to handle.

Examples

Run this code

# NOT RUN {
#Get some example data 
urls.out <- CrawlModels(abbrev = "gfs_0p50", depth = 1)
model.parameters <- ParseModelPage(urls.out[1])
levels <- c("2 m above ground", "100 mb")
variables <- c("TMP", "RH") #Temperature and relative humidity
grib.info <- GribGrab(urls.out[1], model.parameters$pred[1], levels, variables)
#Extract the data
model.data <- ReadGrib(grib.info[[1]]$file.name, levels, variables)

#Make it into an array
gfs.array <- ModelGrid(model.data, c(0.5, 0.5))

#What variables and levels we have
print(gfs.array$levels)
print(gfs.array$variables)

#Find minimum temperature at the ground surface, and where it is
min.temp <- min(gfs.array$z[2, 1,,] - 273.15)
sprintf("%.1f", min.temp) #in Celsius 

ti <- which(gfs.array$z[2, 1,,] == min.temp + 273.15, arr.ind = TRUE)

lat <- gfs.array$y[ti[1,2]] #Lat of minimum temp
lon <- gfs.array$x[ti[1,1]] #Lon of minimum temp

#Find maximum temperature at 100 mb atmospheric pressure
max.temp <- max(gfs.array$z[1, 1,,]) - 273.15
sprintf("%.1f", max.temp) #Brrr!
# }