adw_vector {adw}

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)

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.

Value

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

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)