,
Guoyu Ren [ctb],
Yun Qin [ctb],
Chenchen Ren [ctb],
Xiang Zheng [ctb]| Title: | Angular Distance Weighting Interpolation |
|---|---|
| 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. In addition to this, a simple method which gridding the irregularly-spaced data points onto regular latitude-longitude grids by averaging all points in grid-boxes was also provided. |
| Authors: | Panfeng Zhang [aut, cre] ,
Guoyu Ren [ctb],
Yun Qin [ctb],
Chenchen Ren [ctb],
Xiang Zheng [ctb] |
| Maintainer: | Panfeng Zhang <[email protected]> |
| License: | GPL-3 |
| Version: | 0.4.0 |
| Built: | 2025-01-26 05:41:48 UTC |
| Source: | https://github.com/panfengzhang/adw |
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.
adw(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)adw(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)
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), or a polygon object with class 'sf' (package 'sf'), or a polygon object with class 'SpatVector' (package 'terra'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326). |
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. |
a regular latitude-longitude dataframe grid (interpoled values).
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.
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 1 grd <- adw(dd, extent = c(110, 117, 31, 37), gridsize = 0.5, cdd = 500) head(grd) # example 2 hmap <- cnmap::getMap(code = "410000") |> sf::st_make_valid() # return a 'sf' object. grd <- adw(dd, extent = hmap, gridsize = 0.5, cdd = 500) head(grd) # example 3 hmap <- cnmap::getMap(code = "410000", returnClass = "sv") # return a 'SpatVector' object. grd <- adw(dd, extent = hmap, gridsize = 0.5, cdd = 500) head(grd)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 1 grd <- adw(dd, extent = c(110, 117, 31, 37), gridsize = 0.5, cdd = 500) head(grd) # example 2 hmap <- cnmap::getMap(code = "410000") |> sf::st_make_valid() # return a 'sf' object. grd <- adw(dd, extent = hmap, gridsize = 0.5, cdd = 500) head(grd) # example 3 hmap <- cnmap::getMap(code = "410000", returnClass = "sv") # return a 'SpatVector' object. grd <- adw(dd, extent = hmap, gridsize = 0.5, cdd = 500) head(grd)
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.
adw_sf(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)adw_sf(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)
ds |
a input dataframe which contains the column names of lon, lat, value. |
extent |
a polygon object with class 'sf' (package 'sf'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326). |
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. |
a regular latitude-longitude dataframe grid (interpoled values).
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.
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) hmap <- cnmap::getMap(code = "410000") |> sf::st_make_valid() # return a 'sf' object. grd <- adw_sf(dd, extent = hmap, gridsize = 0.5, cdd = 500) head(grd)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) hmap <- cnmap::getMap(code = "410000") |> sf::st_make_valid() # return a 'sf' object. grd <- adw_sf(dd, extent = hmap, gridsize = 0.5, cdd = 500) head(grd)
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.
adw_sv(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)adw_sv(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)
ds |
a input dataframe which contains the column names of lon, lat, value. |
extent |
a polygon object with class 'SpatVector' (package 'terra'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326). |
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. |
a regular latitude-longitude dataframe grid (interpoled values).
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.
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 hmap <- cnmap::getMap(code = "410000", returnClass = "sv") # return a 'SpatVector' object. grd <- adw_sv(dd, extent = hmap, gridsize = 0.5, cdd = 500) head(grd)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 hmap <- cnmap::getMap(code = "410000", returnClass = "sv") # return a 'SpatVector' object. grd <- adw_sv(dd, extent = hmap, gridsize = 0.5, cdd = 500) head(grd)
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.
adw_vector(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)adw_vector(ds, extent, gridsize = 5, cdd = 1000, m = 4, nmin = 3, nmax = 10)
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. |
a regular latitude-longitude dataframe grid (interpoled values).
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.
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)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)
The large area, or hemispheric, or global averages can be calculated dependent on the area represented by the grid-point or grid-box. The weight of latitude-longitude grid-points-boxes should be the cosine of the latitude of the ith grid-point-box.
awa(dat, lat)awa(dat, lat)
dat |
a numeric vector of grid data. The missing values are not allowed. |
lat |
a latitude numeric vector of grid data. The cosine of latitude is used as the weight coefficient. |
a scalar value, i.e the value of area weighted average.
Jones, P. D., and M. Hulme, 1996: Calculating regional climatic time series for temperature and precipitation: Methods and illustrations. Int. J. Climatol., 16, 361–377, https://doi.org/10.1002/(SICI)1097-0088(199604)16:4<361::AID-JOC53>3.0.CO;2-F.
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)) grd <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5) grd <- na.omit(grd) awa(grd$value, grd$lat) # area weighted averageset.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)) grd <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5) grd <- na.omit(grd) awa(grd$value, grd$lat) # area weighted average
the irregularly-spaced data of points are converted onto regular latitude-longitude grids by averaging all stations in grid-boxes.
points2grid(dd, extent, gridsize = 0.5)points2grid(dd, extent, gridsize = 0.5)
dd |
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), or a polygon object with class 'sf' (package 'sf'), or a polygon object with class 'SpatVector' (package 'terra'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326). |
gridsize |
the grid size, i.e. the grid resolution. units: degree. |
a regular latitude-longitude dataframe grid (grid values).
Jones, P. D., and M. Hulme, 1996: Calculating regional climatic time series for temperature and precipitation: Methods and illustrations. Int. J. Climatol., 16, 361–377, https://doi.org/10.1002/(SICI)1097-0088(199604)16:4<361::AID-JOC53>3.0.CO;2-F.
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 1 grd <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5) head(grd) # example 2 hmap <- cnmap::getMap(code = "410000", return = "sf") |> sf::st_make_valid() # return a 'sf' object. grd <- points2grid(dd, extent = hmap, gridsize = 0.5) head(grd) # example 3 hmap <- cnmap::getMap(code = "410000", return = "sv") # return a 'SpatVector' object. grd <- points2grid(dd, extent = hmap, gridsize = 0.5) head(grd)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 1 grd <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5) head(grd) # example 2 hmap <- cnmap::getMap(code = "410000", return = "sf") |> sf::st_make_valid() # return a 'sf' object. grd <- points2grid(dd, extent = hmap, gridsize = 0.5) head(grd) # example 3 hmap <- cnmap::getMap(code = "410000", return = "sv") # return a 'SpatVector' object. grd <- points2grid(dd, extent = hmap, gridsize = 0.5) head(grd)
the irregularly-spaced data of points are converted onto regular latitude-longitude grids by averaging all stations in grid-boxes.
points2grid_sf(dd, extent, gridsize = 5)points2grid_sf(dd, extent, gridsize = 5)
dd |
a input dataframe which contains the column names of lon, lat, value. |
extent |
a polygon object of simple feature (come from package 'sf'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326). |
gridsize |
the grid size, i.e. the grid resolution. units: degree. |
a regular latitude-longitude dataframe grid (grid values).
Jones, P. D., and M. Hulme, 1996: Calculating regional climatic time series for temperature and precipitation: Methods and illustrations. Int. J. Climatol., 16, 361–377, https://doi.org/10.1002/(SICI)1097-0088(199604)16:4<361::AID-JOC53>3.0.CO;2-F.
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 hmap <- cnmap::getMap(code = 410000) |> sf::st_make_valid() grd <- points2grid_sf(dd, extent = hmap, gridsize = 0.5) head(grd)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 hmap <- cnmap::getMap(code = 410000) |> sf::st_make_valid() grd <- points2grid_sf(dd, extent = hmap, gridsize = 0.5) head(grd)
the irregularly-spaced data of points are converted onto regular latitude-longitude grids by averaging all stations in grid-boxes.
points2grid_sv(dd, extent, gridsize = 5)points2grid_sv(dd, extent, gridsize = 5)
dd |
a input dataframe which contains the column names of lon, lat, value. |
extent |
a polygon object of SpatVector (from package 'terra'). Assume that the coordinate reference system is WGS1984 (EPSG: 4326). |
gridsize |
the grid size, i.e. the grid resolution. units: degree. |
a regular latitude-longitude dataframe grid (grid values).
Jones, P. D., and M. Hulme, 1996: Calculating regional climatic time series for temperature and precipitation: Methods and illustrations. Int. J. Climatol., 16, 361–377, https://doi.org/10.1002/(SICI)1097-0088(199604)16:4<361::AID-JOC53>3.0.CO;2-F.
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 hmap <- cnmap::getMap(code = 410000, returnClass = "sv") grd <- points2grid_sv(dd, extent = hmap, gridsize = 0.5) head(grd)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 hmap <- cnmap::getMap(code = 410000, returnClass = "sv") grd <- points2grid_sv(dd, extent = hmap, gridsize = 0.5) head(grd)
The irregularly-spaced data of points are converted onto regular latitude-longitude grids by averaging all stations in grid-boxes.
points2grid_vector(dd, extent, gridsize = 5)points2grid_vector(dd, extent, gridsize = 5)
dd |
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. |
a regular latitude-longitude dataframe grid (grid values).
Jones, P. D., and M. Hulme, 1996: Calculating regional climatic time series for temperature and precipitation: Methods and illustrations. Int. J. Climatol., 16, 361–377, https://doi.org/10.1002/(SICI)1097-0088(199604)16:4<361::AID-JOC53>3.0.CO;2-F.
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 <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5) head(grd)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 <- points2grid(dd, extent = c(110, 117, 31, 37), gridsize = 0.5) head(grd)