rvn_met_interpolate: Interpolate meteorological data using IDW

In rchlumsk/RavenR: Raven Hydrological Modelling Framework R Support and Analysis

Interpolate meteorological data using IDW

Description

Interpolates/infills missing meteorological data by using an inverse-distance weighting scheme to infill using data from nearby meteorological stations; issues where maximum temperature is less than minimum temperature can also be resolved.

Usage

rvn_met_interpolate(
  weather_data = NULL,
  cc = c("max_temp", "min_temp", "total_precip"),
  key_stn_ids = NULL,
  ppexp = 2,
  fix_interp_temp = TRUE,
  fix_base_temp = FALSE,
  min_dist = 100
)

Arguments

weather_data	data frame of input meteorological data from multiple stations
cc	columns from weather_data to infill missing values in
key_stn_ids	station IDs in which to perform the interpolation
ppexp	exponent to use in inverse distance weighting calculation (default 2)
fix_interp_temp	function will swap interpolated min and max temp if appropriate
fix_base_temp	function will swap any min and max temp if appropriate
min_dist	minimum distance used in IDW to avoid issues with stations of different IDs but exact same coordinates (default 100m)

Details

This function takes a meteorological data set with multiple station data in one data frame and interpolates the missing values for key stations, for the specified meteorological variables to interpolate.

The format of the weather_data input is consistent with that from the weathercan::weather_dl function, which is the recommended tool to gather this input (see the examples).

This function does not guarantee to infill all missing values, since this depends on the availability of data at other locations when it lacks at a given station, although a warning is issued if missing values remain following the interpolation. If this is encountered, it may be prudent to consider including additional stations in the available data for interpolation and/or applying alternative interpolation schemes in conjunction with rvn_met_interpolate.

This function does not (currently) perform checks for the quality of the supplied or infilled data, such as checking for maximum temperature less than minimum temperature, unreasonable precipitation values, etc.

The key_stn_ids indicates which stations should have their missing values interpolated. It is likely that the user will require more data to perform a proper interpolation than the user cares to have gauge records at, i.e. some stations are only provided for the purposes of infilling missing data at other stations. Since the interpolation of all stations provided can become computationally expensive, the user can specify which stations they want to interpolate data for with the key_stn_ids parameter. Station IDs that are not within the key_stn_ids (if not NULL) will still be used, but not themselves infilled.

fix_interp_temp is a boolean for the function to swap any max_temp and min_temp fields where the maximum temperature is less than the minimum temperature, a common fix in messy environmental data. This function will only apply if the columns max_temp and min_temp are present, and will only apply to the interpolated fields. By default this is enabled, and automatic fixes to interpolated data will be done; supplied data where the max temp is less than min temp will not be fixed unless fix_base_temp is enabled.

The distance calculation, estimating the distance between stations, is performed using the rvn_dist_lonlat function, which is based on the geosphere package. The min_dist is included to provide a minimum distance used in IDW, and avoid issues with stations of different names and IDs but the same coordinates. This can occur when stations upgrade or are reinstated at a later time in the same location, which would otherwise result in a div/0 error in the IDW calculation.

Value

new_wd

infilled meteorological data set

See Also

rvn_rvt_write_met for writing meteorological data sets to rvt format.

Examples

## Not run: 
## NOT RUN (downloads data + long runtime)

# example to create infilled data sets

library(weathercan)
stn <- weathercan::stations_search(name="Glen allan", interval = "day")
dl_stn <- stn
all_stns <- weathercan::stations_search(coords=c(stn$lat, stn$lon), dist=40,
          interval="day", starts_latest = 2002,
          ends_earliest = 2010)

# download data with weathercan::weather_dl()
weather_data <- weather_dl(station_ids = all_stns$station_id,
                            start = "2002-10-01",
                            end = "2005-10-01",
                            interval="day")

# define first three as key stations of interest for infilling
dl_stn <- all_stns[1:3,]

# confirm missing data in key columns
key_cols <- c("min_temp","max_temp","total_precip")
length(which(is.na(weather_data[,key_cols])))

# perform interpolation for key stns (3) using all stations downloaded (5)
new_wd <- rvn_met_interpolate(weather_data = weather_data,
                               key_stn_ids = dl_stn$station_id,
                               cc = key_cols)

# no warning - confirm no missing values in key columns
length(which(is.na(new_wd[,key_cols])))

## End(Not run)

rchlumsk/RavenR documentation built on April 19, 2024, 5:15 a.m.