CDORemap: Interpolates arrays with longitude and latitude dimensions...

In s2dverification: Set of Common Tools for Forecast Verification

Interpolates arrays with longitude and latitude dimensions using CDO

Description

This function takes as inputs a multidimensional array (optional), a vector or matrix of longitudes, a vector or matrix of latitudes, a destination grid specification, and the name of a method to be used to interpolate (one of those available in the 'remap' utility in CDO). The interpolated array is returned (if provided) together with the new longitudes and latitudes.

CDORemap() permutes by default the dimensions of the input array (if needed), splits it in chunks (CDO can work with data arrays of up to 4 dimensions), generates a file with the data of each chunk, interpolates it with CDO, reads it back into R and merges it into a result array. If no input array is provided, the longitude and latitude vectors will be transformed only. If the array is already on the desired destination grid, no transformation is performed (this behvaiour works only for lonlat and gaussian grids).

Any metadata attached to the input data array, longitudes or latitudes will be preserved or accordingly modified.

Usage

CDORemap(
  data_array = NULL,
  lons,
  lats,
  grid,
  method,
  avoid_writes = TRUE,
  crop = TRUE,
  force_remap = FALSE,
  write_dir = tempdir()
)

Arguments

data_array	Multidimensional numeric array to be interpolated. If provided, it must have at least a longitude and a latitude dimensions, identified by the array dimension names. The names for these dimensions must be one of the recognized by s2dverification (can be checked with s2dverification:::.KnownLonNames() and s2dverification:::.KnownLatNames()).
lons	Numeric vector or array of longitudes of the centers of the grid cells. Its size must match the size of the longitude/latitude dimensions of the input array.
lats	Numeric vector or array of latitudes of the centers of the grid cells. Its size must match the size of the longitude/latitude dimensions of the input array.
grid	Character string specifying either a name of a target grid (recognized by CDO; e.g.: 'r256x128', 't106grid') or a path to another NetCDF file which to read the target grid from (a single grid must be defined in such file).
method	Character string specifying an interpolation method (recognized by CDO; e.g.: 'con', 'bil', 'bic', 'dis'). The following long names are also supported: 'conservative', 'bilinear', 'bicubic' and 'distance-weighted'.
avoid_writes	The step of permutation is needed when the input array has more than 3 dimensions and none of the longitude or latitude dimensions in the right-most position (CDO would not accept it without permuting previously). This step, executed by default when needed, can be avoided for the price of writing more intermediate files (whis usually is unconvenient) by setting the parameter avoid_writes = TRUE.
crop	Whether to crop the data after interpolation with 'cdo sellonlatbox' (TRUE) or to extend interpolated data to the whole world as CDO does by default (FALSE). If crop = TRUE then the longitude and latitude borders which to crop at are taken as the limits of the cells at the borders ('lons' and 'lats' are perceived as cell centers), i.e. the resulting array will contain data that covers the same area as the input array. This is equivalent to specifying crop = 'preserve', i.e. preserving area. If crop = 'tight' then the borders which to crop at are taken as the minimum and maximum cell centers in 'lons' and 'lats', i.e. the area covered by the resulting array may be smaller if interpolating from a coarse grid to a fine grid. The parameter 'crop' also accepts a numeric vector of custom borders which to crop at: c(western border, eastern border, southern border, northern border).
force_remap	Whether to force remapping, even if the input data array is already on the target grid.
write_dir	Path to the directory where to create the intermediate files for CDO to work. By default, the R session temporary directory is used (tempdir()).

Value

A list with the following components:

'data_array'	The interpolated data array (if an input array is provided at all, NULL otherwise).
'lons'	The longitudes of the data on the destination grid.
'lats'	The latitudes of the data on the destination grid.

Author(s)

History:
0.0 - 2017-01 (N. Manubens) - Original code.

Examples

 ## Not run: 
# Interpolating only vectors of longitudes and latitudes
lon <- seq(0, 360 - 360/50, length.out = 50)
lat <- seq(-90, 90, length.out = 25)
tas2 <- CDORemap(NULL, lon, lat, 't170grid', 'bil', TRUE)

# Minimal array interpolation
tas <- array(1:50, dim = c(25, 50))
names(dim(tas)) <- c('lat', 'lon')
lon <- seq(0, 360 - 360/50, length.out = 50)
lat <- seq(-90, 90, length.out = 25)
tas2 <- CDORemap(tas, lon, lat, 't170grid', 'bil', TRUE)

# Metadata can be attached to the inputs. It will be preserved and 
# accordignly modified.
tas <- array(1:50, dim = c(25, 50))
names(dim(tas)) <- c('lat', 'lon')
lon <- seq(0, 360 - 360/50, length.out = 50)
metadata <- list(lon = list(units = 'degrees_east'))
attr(lon, 'variables') <- metadata
lat <- seq(-90, 90, length.out = 25)
metadata <- list(lat = list(units = 'degrees_north'))
attr(lat, 'variables') <- metadata
metadata <- list(tas = list(dim = list(lat = list(len = 25,
                                                 vals = lat),
                                      lon = list(len = 50,
                                                 vals = lon)
                                     )))
attr(tas, 'variables') <- metadata
tas2 <- CDORemap(tas, lon, lat, 't170grid', 'bil', TRUE)

# Arrays of any number of dimensions in any order can be provided.
num_lats <- 25
num_lons <- 50
tas <- array(1:(10*num_lats*10*num_lons*10), 
            dim = c(10, num_lats, 10, num_lons, 10))
names(dim(tas)) <- c('a', 'lat', 'b', 'lon', 'c')
lon <- seq(0, 360 - 360/num_lons, length.out = num_lons)
metadata <- list(lon = list(units = 'degrees_east'))
attr(lon, 'variables') <- metadata
lat <- seq(-90, 90, length.out = num_lats)
metadata <- list(lat = list(units = 'degrees_north'))
attr(lat, 'variables') <- metadata
metadata <- list(tas = list(dim = list(a = list(),
                                      lat = list(len = num_lats,
                                                 vals = lat),
                                      b = list(),
                                      lon = list(len = num_lons,
                                                 vals = lon),
                                      c = list()
                                     )))
attr(tas, 'variables') <- metadata
tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', TRUE)
# The step of permutation can be avoided but more intermediate file writes
# will be performed.
tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', FALSE)

# If the provided array has the longitude or latitude dimension in the 
# right-most position, the same number of file writes will be performed,
# even if avoid_wrties = FALSE.
num_lats <- 25
num_lons <- 50
tas <- array(1:(10*num_lats*10*num_lons*10), 
            dim = c(10, num_lats, 10, num_lons))
names(dim(tas)) <- c('a', 'lat', 'b', 'lon')
lon <- seq(0, 360 - 360/num_lons, length.out = num_lons)
metadata <- list(lon = list(units = 'degrees_east'))
attr(lon, 'variables') <- metadata
lat <- seq(-90, 90, length.out = num_lats)
metadata <- list(lat = list(units = 'degrees_north'))
attr(lat, 'variables') <- metadata
metadata <- list(tas = list(dim = list(a = list(),
                                      lat = list(len = num_lats,
                                                 vals = lat),
                                      b = list(),
                                      lon = list(len = num_lons,
                                                 vals = lon)
                                     )))
attr(tas, 'variables') <- metadata
tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', TRUE)
tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', FALSE)

# An example of an interpolation from and onto a rectangular regular grid
num_lats <- 25
num_lons <- 50
tas <- array(1:(1*num_lats*num_lons), dim = c(num_lats, num_lons))
names(dim(tas)) <- c('y', 'x')
lon <- array(seq(0, 360 - 360/num_lons, length.out = num_lons), 
            dim = c(num_lons, num_lats))
metadata <- list(lon = list(units = 'degrees_east'))
names(dim(lon)) <- c('x', 'y')
attr(lon, 'variables') <- metadata
lat <- t(array(seq(-90, 90, length.out = num_lats), 
              dim = c(num_lats, num_lons)))
metadata <- list(lat = list(units = 'degrees_north'))
names(dim(lat)) <- c('x', 'y')
attr(lat, 'variables') <- metadata
tas2 <- CDORemap(tas, lon, lat, 'r100x50', 'bil')

# An example of an interpolation from an irregular grid onto a gaussian grid
num_lats <- 25
num_lons <- 50
tas <- array(1:(10*num_lats*10*num_lons*10), 
            dim = c(10, num_lats, 10, num_lons))
names(dim(tas)) <- c('a', 'j', 'b', 'i')
lon <- array(seq(0, 360 - 360/num_lons, length.out = num_lons), 
            dim = c(num_lons, num_lats))
metadata <- list(lon = list(units = 'degrees_east'))
names(dim(lon)) <- c('i', 'j')
attr(lon, 'variables') <- metadata
lat <- t(array(seq(-90, 90, length.out = num_lats), 
        dim = c(num_lats, num_lons)))
metadata <- list(lat = list(units = 'degrees_north'))
names(dim(lat)) <- c('i', 'j')
attr(lat, 'variables') <- metadata
tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil')

# Again, the dimensions can be in any order
num_lats <- 25
num_lons <- 50
tas <- array(1:(10*num_lats*10*num_lons), 
            dim = c(10, num_lats, 10, num_lons))
names(dim(tas)) <- c('a', 'j', 'b', 'i')
lon <- array(seq(0, 360 - 360/num_lons, length.out = num_lons), 
            dim = c(num_lons, num_lats))
names(dim(lon)) <- c('i', 'j')
lat <- t(array(seq(-90, 90, length.out = num_lats), 
              dim = c(num_lats, num_lons)))
names(dim(lat)) <- c('i', 'j')
tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil')
tas2 <- CDORemap(tas, lon, lat, 't17grid', 'bil', FALSE)
# It is ossible to specify an external NetCDF file as target grid reference
tas2 <- CDORemap(tas, lon, lat, 'external_file.nc', 'bil')

## End(Not run)

s2dverification documentation built on April 20, 2022, 9:06 a.m.