adw (version 0.4.0)

adw_vector: Angular Distance Weighting Interpolation for the extent of vector.

Description

The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius.

Usage

adw_vector(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)

Value

a regular latitude-longitude dataframe grid (interpoled values).

Arguments

ds

a input dataframe which contains the column names of lon, lat, value.

extent

a extent numeric vector (latitude and longitude) of length 4 in the order c(xmin, xmax, ymin, ymax).

gridsize

the grid size, i.e. the grid resolution. units: degree.

cdd

correlation decay distance, i.e. the maximum search radius. unit: kilometer. default value: 1000km.

m

is used to adjust the weighting function further, higher values of m increase the rate at which the weight decays with distance. default value 4.

nmin

the minimum number of observation points required to interpolate a grid within the search radius (i.e. cdd); if the number of stations within the search ridius (cdd) is less than nmin, a missing value will be generated to fill this grid. default value 3.

nmax

The number of nearest points within the search radius to use for interpolation. default value 10.

References

Caesar, J., L. Alexander, and R. Vose, 2006: Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. Journal of Geophysical Research, 111, https://doi.org/10.1029/2005JD006280.

Examples

set.seed(2)
dd <- data.frame(lon = runif(100, min = 110, max = 117),
                 lat = runif(100, min = 31, max = 37),
                 value = runif(100, min = -10, max = 10))
head(dd)
# example
grd <- adw_vector(dd, extent = c(110, 117, 31, 37), gridsize = 0.5, cdd = 500)
head(grd)