PRISM Climate Data

Kyle Messier, with assistance from GitHub Copilot

2026-05-20

This article demonstrates a compact workflow for PRISM climate 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 = "prism", ...) combines time, element, and data_type.

data_type	Resolution	Supported element values	time expectations
ts	4 km time series	ppt, tmin, tmax, tmean, tdmean, vpdmin, vpdmax	YYYYMMDD for daily data (1981-01-01 through yesterday), YYYYMM for monthly data (1981-01 through last month), or YYYY for annual data (1981 through last year)
normals_800	800 m normals	ppt, tmin, tmax, tmean, tdmean, vpdmin, vpdmax, solslope, soltotal, solclear, soltrans	Monthly normals use MM or 14 for annual normal; daily normals use MMDD
normals	4 km normals	ppt, tmin, tmax, tmean, tdmean, vpdmin, vpdmax, solslope, soltotal, solclear, soltrans	Monthly normals use MM or 14 for annual normal; daily normals use MMDD

• Historical PRISM bundles also accept time = "YYYY" for years 1895-1980 and return a zip file containing 12 monthly grids plus the annual grid.
• format is only used for data_type = "ts" and can be nc, asc, or grib2.
• Major constraints:
  • sol* elements are available for normals only, not for ts.
  • For normals, format is ignored.
  • The PRISM API always returns a zip file, even when a time-series format is requested.

Download representative requests

directory_to_save <- file.path(tempdir(), "prism_ts_workflow")
download_data(
  dataset_name = "prism",
  time = "201005",
  element = "tmean",
  data_type = "ts",
  directory_to_save = directory_to_save,
  acknowledgement = TRUE,
  unzip = TRUE,
  remove_zip = FALSE
)

Process one workflow-ready data product

processed_data <- process_covariates(
  covariate = "prism",
  path = list.files(
    paste0(directory_to_save,"/data_files/"),
    pattern = ".nc",
    recursive = TRUE,
    full.names = TRUE
  )[1],
  element = "tmean",
  time = "201005",
  extent = terra::ext(-114.9, -102.0, 31.3, 41.1)
)

Calculate covariates at points

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 = "prism",
  from = processed_data,
  locs = example_points_sf,
  locs_id = "site_id",
  radius = 1000,
  geom = "sf"
)

Visualize the point outputs

point_basemap <-
  sf::st_as_sf(maps::map("world", plot = FALSE, fill = TRUE))

ggplot() +  
  geom_sf(data = point_basemap, fill = "gray80", color = "white") +
  geom_sf(data = point_values, aes(color = tmean_1000), size = 3) +
  scale_color_viridis_c() + labs(title = "PRISM tmean at example points") +
  coord_sf(xlim = domain_x, ylim = domain_y)