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Climatology Lab gridMET

Kyle Messier, with assistance from GitHub Copilot

2026-05-21

Source: vignettes/gridmet_workflow.Rmd
gridmet_workflow.Rmd

This article demonstrates a compact workflow for Climatology Lab gridMET data using multiple variables, multiple summaries, and both point and polygon extraction.

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 = "gridmet", ...) accepts either the full variable names below or their gridMET codes.

Code Variable
sph Near-Surface Specific Humidity
vpd Mean Vapor Pressure Deficit
pr Precipitation
rmin Minimum Near-Surface Relative Humidity
rmax Maximum Near-Surface Relative Humidity
srad Surface Downwelling Solar Radiation
tmmn Minimum Near-Surface Air Temperature
tmmx Maximum Near-Surface Air Temperature
vs Wind speed at 10 m
th Wind direction at 10 m
pdsi Palmer Drought Severity Index
pet Reference grass evapotranspiration
etr Reference alfalfa evapotranspiration
ERC Energy Release Component
BI Burning Index
FM100 100-hour dead fuel moisture
FM1000 1000-hour dead fuel moisture
  • Temporal resolution: daily; each download is an annual NetCDF file for one variable.
  • Year input: use a single year or a start/end pair such as c(2018, 2022).
  • Availability check: the wrapper validates the first requested variable-year URL and stops if that request returns HTTP 404.
  • Major constraint: gridMET downloads do not require authentication.

Download representative requests 

directory_to_save <- file.path(tempdir(), "gridmet_workflow")
download_data(
  dataset_name = "gridmet",
  variables = c("tmmx"),
  year = 2020,
  directory_to_save = directory_to_save,
  acknowledgement = TRUE
)

Process workflow-ready data product 

processed_data <- process_covariates(
  covariate = "gridmet",
  variable = "tmmx",
  date = c("2020-01-01", "2020-01-31"),
  path = file.path(directory_to_save, "tmmx"),
  extent = terra::ext(-79.2, -78.6, 35.8, 36.3)
)

Calculate covariates at points using native daily temporal resolution and use .by_time to calculate monthly means 



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 = "gridmet",
  from = processed_data,
  locs = example_points_sf,
  locs_id = "site_id",
  radius = 0,
  fun = "mean",
  geom = "sf"
)

point_values_month <- calculate_covariates(
  covariate = "gridmet",
  from = processed_data,
  locs = example_points_sf,
  locs_id = "site_id",
  .by_time = "month",
  radius = 0,
  fun = "mean",
  geom = "sf"
)

print(point_values)
print(point_values_month)

Visualize the point outputs 


ggplot(data = point_values) + geom_sf(aes(color = `tmmx_0`)) + ggtitle("Daily values")