Hourly ERA5-Land meteorology averaged over a lake polygon — extract_era5_lake

Thin convenience wrapper around extract_era5_hourly_met(): it takes a single lake outline, defaults method to "area" (the area-weighted mean of every overlapping ERA5 grid cell, best when a lake straddles several cells) and tags the result with optional id / name labels.

Usage

extract_era5_lake_met(
  polygon,
  path,
  method = c("area", "area_mean", "bilinear", "nearest"),
  id = NULL,
  name = NULL,
  ...
)

Arguments

polygon: the lake outline: an sf / sfc polygon in any CRS, a path to a vector file sf::st_read() can open (.gpkg, .shp, ...), or a path to an .rds holding an sf / sfc. Several features are unioned into one.
path: directory holding the ERA5-Land files (netCDF or GRIB); see extract_era5_hourly_met() for how files are matched and the reader backend chosen.
method: spatial sampling, default "area". See extract_era5_hourly_met() for "area", "area_mean", "bilinear" and "nearest".
id, name: optional labels stored on the result as attributes lake_id / lake_name - handy when binding many lakes together.
...: further arguments for extract_era5_hourly_met() (years, months, variables, tz, format, precip_units, pressure_units, pattern, max_dist_km, outfile, fill_gaps, verbose).

Value

the data frame from extract_era5_hourly_met(), with extra attributes lake_id and lake_name.

Details

For a point, a raw geometry, or full control use extract_era5_hourly_met() directly.

Examples

if (FALSE) { # \dontrun{
poly <- sf::st_read("gis/rotorua.gpkg")

met <- extract_era5_lake_met(
  poly,
  path    = "era5_land",
  years   = 2023:2024,
  name    = "Rotorua",
  outfile = "rotorua_era5_hourly_met.csv")

## batch over a multi-lake layer
lakes <- sf::st_read("gis/lakes.gpkg")
mets  <- lapply(seq_len(nrow(lakes)), function(i)
  extract_era5_lake_met(lakes[i, ], path = "era5_land", years = 2024,
                        id = lakes$id[i], name = lakes$name[i]))
} # }