Climatology Lab gridMET
In amadeus: Accessing and Analyzing Large-Scale Environmental Data

# packages
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = ""
)
library(amadeus)

This vignette demonstrates how to download, process, and calculate covariates from the Climatology Lab's gridMET dataset using amadeus functions. Details are provided for each function's parameters and outputs. The examples utilize daily specific humidity data. See https://www.climatologylab.org/wget-gridmet.html for full variable names and acronyms. The messages returned by amadeus functions have been omitted for brevity.

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
)

cat('[1] "aa5116525468299d1fc483b108b3e841fc40d7e5"')

Check the downloaded netCDF files.

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

cat('[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

cat('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]])

{style="display: block; margin-left: auto; margin-right: auto;"}

Calculate covariates

Calculate covariates for California county boundaries with calculate_covariates. County boundaries are accessed with the tigris::counties function.\insertRef{package_tigris}

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

cat('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
')