ReadNetCDF: Read NetCDF files.

In metR: Tools for Easier Analysis of Meteorological Fields

Read NetCDF files.

Description

Using the ncdf4-package package, it reads a NetCDF file. The advantage over using ncvar_get is that the output is a tidy data.table with proper dimensions.

Usage

ReadNetCDF(
  file,
  vars = NULL,
  out = c("data.frame", "vector", "array"),
  subset = NULL,
  key = FALSE
)

GlanceNetCDF(file, ...)

Arguments

file	source to read from. Must be one of: A string representing a local file with read access. A string representing a URL readable by ncdf4::nc_open(). (this includes DAP urls). A netcdf object returned by ncdf4::nc_open().
vars	one of: NULL: reads all variables. a character vector with the name of the variables to read. a function that takes a vector with all the variables and returns either a character vector with the name of variables to read or a numeric/logical vector that indicates a subset of variables.
out	character indicating the type of output desired
subset	a list of subsetting objects. See below.
key	if TRUE, returns a data.table keyed by the dimensions of the data.
...	in GlanceNetCDF(), ignored. Is there for convenience so that a call to ReadNetCDF() can be also valid for GlanceNetCDF().

Value

The return format is specified by out. It can be a data table in which each column is a variable and each row, an observation; an array with named dimensions; or a vector. Since it's possible to return multiple arrays or vectors (one for each variable), for consistency the return type is always a list. Either of these two options are much faster than the first since the most time consuming part is the melting of the array returned by ncdf4::ncvar_get. out = "vector" is particularly useful for adding new variables to an existing data frame with the same dimensions.

When not all variables specified in vars have the same number of dimensions, the shorter variables will be recycled. E.g. if reading a 3D pressure field and a 2D surface temperature field, the latter will be turned into a 3D field with the same values in each missing dimension.

GlanceNetCDF() returns a list of variables and dimensions included in the file with a nice printing method.

Subsetting

In the most basic form, subset will be a named list whose names must match the dimensions specified in the NetCDF file and each element must be a vector whose range defines a contiguous subset of data. You don't need to provide and exact range that matches the actual gridpoints of the file; the closest gridpoint will be selected. Furthermore, you can use NA to refer to the existing minimum or maximum.

So, if you want to get Southern Hemisphere data from the from a file that defines latitude as lat, then you can use:

subset = list(lat = -90:0)

More complex subsetting operations are supported. If you want to read non-contiguous chunks of data, you can specify each chunk into a list inside subset. For example this subset

subset = list(list(lat = -90:-70, lon = 0:60),
              list(lat = 70:90, lon = 300:360))

will return two contiguous chunks: one on the South-West corner and one on the North-East corner. Alternatively, if you want to get the four corners that are combination of those two conditions,

subset = list(lat = list(-90:-70, 70:90),
              lon = list(0:60, 300:360))

Both operations can be mixed together. So for example this

subset = list(list(lat = -90:-70,
                   lon = 0:60),
              time = list(c("2000-01-01", "2000-12-31"),
                          c("2010-01-01", "2010-12-31")))

returns one spatial chunk for each of two temporal chunks.

The general idea is that named elements define 'global' subsets ranges that will be applied to every other subset, while each unnamed element define one contiguous chunk. In the above example, time defines two temporal ranges that every subset of data will have.

The above example, then, is equivalent to

subset = list(list(lat = -90:-70,
                   lon = 0:60,
                   time = c("2000-01-01", "2000-12-31")),
              list(lat = -90:-70,
                   lon = 0:60,
                   time = c("2010-01-01", "2010-12-31")))

but demands much less typing.

Examples

file <- system.file("extdata", "temperature.nc", package = "metR")
# Get a list of variables.
variables <- GlanceNetCDF(file)
print(variables)

# The object returned by GlanceNetCDF is a list with lots
# of information
str(variables)

# Read only the first one, with name "var".
field <- ReadNetCDF(file, vars = c(var = names(variables$vars[1])))
# Add a new variable.
# ¡Make sure it's on the same exact grid!
field[, var2 := ReadNetCDF(file, out = "vector")]

## Not run: 
# Using a DAP url
url <- "http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.GMAO/.GEOS_V2p1/.hindcast/.ua/dods"
field <- ReadNetCDF(url, subset = list(M = 1,
                                       P = 10,
                                       S = "1999-01-01"))

# In this case, opening the netcdf file takes a non-neglible
# amount of time. So if you want to iterate over many dimensions,
# then it's more efficient to open the file first and then read it.

ncfile <- ncdf4::nc_open(url)
field <- ReadNetCDF(ncfile, subset = list(M = 1,
                                       P = 10,
                                       S = "1999-01-01"))


# Using a function in `vars` to read all variables that
# start with "radar_".
ReadNetCDF(radar_file, vars = \(x) startsWith(x, "radar_"))


## End(Not run)

metR documentation built on Oct. 14, 2024, 5:09 p.m.