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Koppen-Geiger Climate Classes

Kyle Messier, with assistance from GitHub Copilot

2026-05-20

Source: vignettes/koppen_workflow.Rmd
koppen_workflow.Rmd

This article demonstrates a compact workflow for Koppen-Geiger climate classification rasters.

This vignette runs its live workflow when rendered locally. The heavy download, processing, extraction, and plotting chunks are skipped automatically on CI, CRAN checks, and pkgdown builds; set AMADEUS_RUN_VIGNETTES=true to force live execution in those environments.

Available inputs and data availability

  • download_data(dataset_name = "koppen", ...) accepts data_resolution = "0.0083", "0.083", or "0.5".
  • time_period is either "Present" for the 1980-2016 classification or "Future" for the 2071-2100 classification.
  • Downloads arrive as zipped climate-classification raster files; the wrapper exposes period-level products rather than individual years or scenarios.
  • Köppen-Geiger outputs are categorical climate classes, so treat the raster values as class codes instead of a continuous climate surface.

Download representative requests 

directory_to_save <- file.path(tempdir(), "koppen_workflow")
download_data(
  dataset_name = "koppen",
  data_resolution = "0.5",
  time_period = "Present",
  directory_to_save = directory_to_save,
  acknowledgement = TRUE,
  unzip = TRUE,
  remove_zip = FALSE
)

Process one workflow-ready data product 

processed_data <- process_covariates(
  covariate = "koppen",
  path = list.files(
    directory_to_save,
    pattern = "\\.tif$",
    recursive = TRUE,
    full.names = TRUE
  )[1],
  extent = terra::ext(-114.9, -102.0, 31.3, 41.1)
)

plot(processed_data, main = "Koppen-Geiger Climate Classes (0.5° resolution, 1980-2016)")

Calculate covariates at points. Demonstrate the dummy variable and fraction options for categorical covariates. 


domain_x <- c(terra::xmin(processed_data), terra::xmax(processed_data))
domain_y <- c(terra::ymin(processed_data), terra::ymax(processed_data))
domain_dx <- diff(domain_x)
domain_dy <- diff(domain_y)

candidate_xy <- expand.grid(
  lon = seq(domain_x[1] + 0.12 * domain_dx, domain_x[2] - 0.12 * domain_dx, length.out = 5),
  lat = seq(domain_y[1] + 0.12 * domain_dy, domain_y[2] - 0.12 * domain_dy, length.out = 5)
)
example_points_sf <- sf::st_as_sf(
  candidate_xy,
  coords = c("lon", "lat"),
  crs = 4326
)
example_points_sf$site_id <- paste0("site_", seq_len(nrow(example_points_sf)))


point_values <- calculate_covariates(
  covariate = "koppen",
  from = processed_data,
  locs = example_points_sf,
  locs_id = "site_id",
  geom = "sf"
)

print(point_values, n = 25)

frac_values <- calculate_covariates(
  covariate = "koppen",
  from = processed_data,
  locs = example_points_sf,
  locs_id = "site_id",
  frac = TRUE,
  radius = 100000,
  geom = "sf"
)

print(frac_values, n = 25)