Climatology Lab gridMET

Download

Start by downloading the netCDF data files with download_data.

• dataset_name = "gridmet": gridMET dataset name.
• variable = "Near-Surface Specific Humidity": specific humidity variable name.
• year = c(2019, 2020): years of interest.
• directory_to_save = dir: directory to save the downloaded files.
• acknowledgement = TRUE: acknowledge that the raw data files are large and may consume lots of local storage.
• download = TRUE: download the data files.
• remove_command = TRUE: remove the temporary command file used to download the data.
• hash = TRUE: generate unique SHA-1 hash for the downloaded files.

dir <- tempdir()
amadeus::download_data(
  dataset_name = "gridmet",
  variable = "Near-Surface Specific Humidity",
  year = c(2019, 2020),
  directory_to_save = dir,
  acknowledgement = TRUE,
  download = TRUE,
  remove_command = TRUE,
  hash = TRUE
)

[1] "aa5116525468299d1fc483b108b3e841fc40d7e5"

Check the downloaded netCDF files.

list.files(dir, recursive = TRUE, pattern = "sph")

[1] "sph/sph_2019.nc" "sph/sph_2020.nc"

Process

Import and process the downloaded netCDF files with process_covariates.

• covariate = "gridmet": gridMET dataset name.
• variable = "Near-Surface Specific Humidity": specific humidity variable name.
• date = c("2019-12-13", "2022-01-10"): date range of interest.
• path = paste0(dir, "/sph"): directory containing the downloaded files.

sph_process <- amadeus::process_covariates(
  covariate = "gridmet",
  variable = "Near-Surface Specific Humidity",
  date = c("2019-12-18", "2020-01-10"),
  path = file.path(dir, "/sph")
)

Check the processed SpatRaster object.

sph_process

class       : SpatRaster 
dimensions  : 585, 1386, 24  (nrow, ncol, nlyr)
resolution  : 0.04166667, 0.04166667  (x, y)
extent      : -124.7875, -67.0375, 25.04583, 49.42083  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
sources     : sph_2019.nc  (14 layers) 
              sph_2020.nc  (10 layers) 
varnames    : sph (near-surface specific humidity) 
              sph (near-surface specific humidity) 
names       : sph_20191218, sph_20191219, sph_20191220, sph_20191221, sph_20191222, sph_20191223, ... 
unit        :        kg/kg,        kg/kg,        kg/kg,        kg/kg,        kg/kg,        kg/kg, ... 
time (days) : 2019-12-18 to 2020-01-10 

terra::plot(sph_process[[1]])

Calculate covariates

Calculate covariates for California county boundaries with calculate_covariates. County boundaries are accessed with the tigris::counties function.

• covariate = "gridmet": gridMET dataset name.
• from = sph_process: processed SpatRaster object.
• locs = tigris::counties("CA", year = 2019): California county boundaries.
• locs_id = "NAME": county name identifier.
• radius = 0: size of buffer radius around each county.
• geom = "sf": return covariates as an sf object.

library(tigris)
sph_covar <- amadeus::calculate_covariates(
  covariate = "gridmet",
  from = sph_process,
  locs = tigris::counties("CA", year = 2019),
  locs_id = "NAME",
  radius = 0,
  geom = "terra"
)

Check the calculated covariates sf object.

sph_covar

class       : SpatVector 
geometry    : polygons 
dimensions  : 1392, 3  (geometries, attributes)
extent      : -124.482, -114.1312, 32.52883, 42.0095  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
names       :          NAME       time    sph_0
type        :         <chr>   <POSIXt>    <num>
values      :        Sierra 2019-12-18 0.003101
                 Sacramento 2019-12-18 0.005791
              Santa Barbara 2019-12-18 0.004594