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).

1 2 3 |

`model.data` |
Output from |

`resolution` |
Resolution of grid, in degrees if |

`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. |

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.

`z` |
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 |

`x` |
Vector of longitudes |

`y` |
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 |

Only use this function when the model grid is regular. For example, the GFS high resolution model is 0.5 x 0.5 degree across its domain. I have provided this function as a convenience since I only use it for manipulating GFS model data. I am not sure how well it works for other models. Consider yourself warned!

Daniel C. Bowman daniel.bowman@unc.edu

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
## 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!
## End(Not run)
``` |

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