Perform ordinary kriging interpolation of INMET observations

Interpolates meteorological observations from INMET weather stations using ordinary kriging and predicts values for a set of target geometries, such as Brazilian municipalities.

Usage

kriging_inmet(stations_df, mun_geo, var = "total_rainfall")

Arguments

stations_df

An `sf` object containing weather station observations. The object must include point geometries and a numeric column corresponding to the variable specified in `var`.

mun_geo

An `sf` object containing the target geometries where predictions will be generated.

var

Character. Name of the numeric variable to interpolate. Defaults to `"total_rainfall"`.

Value

An `sf` object containing the geometries from `mun_geo` together with the kriging predictions:

* `var1.pred` - Predicted values. * `var1.var` - Prediction variance.

Details

The empirical variogram is estimated with [gstat::variogram()] and a spherical variogram model is fitted using [gstat::fit.variogram()]. Ordinary kriging is then performed with [gstat::krige()].

Both `stations_df` and `mun_geo` must use the same projected coordinate reference system (CRS). Using geographic coordinates (longitude/latitude) is not recommended for kriging because distance calculations are performed in map units.

See also

[gstat::krige()], [gstat::variogram()], [gstat::fit.variogram()]

Examples

# Requires spatial data (e.g., municipal boundaries) together with
# INMET stations observations. The example dataset `floods_rs`
# illustrates the required input format for the `stations_df` parameter. 
# See the vignette "Spatial Interpolation Using Ordinary Kriging" 
# for the complete workflow of this function.

if (FALSE) { # \dontrun{

krig_df <- kriging_inmet(
  stations_df = inmet_data,
  mun_geo = municipalities_sf,
  var = "total_rainfall"
)

head(krig_df)
} # }