Author: Mitchell Manware
Date: 2026-01-26

# NOAA North American Regional Reanalysis (NARR)

#### Mitchell Manware

#### 2026-01-26

This vignette demonstrates how to download, process, and calculate covariates from the NOAA North American Regional Reanalysis (NARR) dataset using `amadeus` functions. Details are provided for each function’s parameters and outputs. The examples utilize daily air temperature at 2m height (“air.2m”) data. The messages returned by `amadeus` functions have been omitted for brevity.

### Download

Start by downloading the netCDF data files with `download_data`.

- `dataset_name = "narr"`: NARR dataset acronym.
- `variable = "air.2m"`: air temperature at 2m height variable code.
- `year = c(2021, 2022)`: 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 = "narr",
      variable = "air.2m",
      year = c(2021, 2022),
      directory_to_save = dir,
      acknowledgement = TRUE,
      download = TRUE,
      remove_command = TRUE,
      hash = TRUE
    )

    [1] "3a382ac1c383c1d048f4044214cb450f"

Check the downloaded netCDF files.

    list.files(dir, recursive = TRUE, pattern = "air.2m")

    [1] "air.2m/air.2m.2021.nc" "air.2m/air.2m.2022.nc"

### Process

Import and process the downloaded netCDF files with `process_covariates`.

- `covariate = "narr"`: NARR dataset acronym.
- `variable = "air.2m"`: air temperature at 2m height variable code.
- `date = c("2021-12-28", "2022-01-03")`: date range of interest.
- `path = paste0(dir, "/air.2m")`: directory containing the downloaded files.

    air2m_process <- amadeus::process_covariates(
      covariate = "narr",
      variable = "air.2m",
      date = c("2021-12-28", "2022-01-03"),
      path = file.path(dir, "/air.2m")
    )

Check the processed `SpatRaster` object.

    air2m_process

    class       : SpatRaster
    dimensions  : 277, 349, 7  (nrow, ncol, nlyr)
    resolution  : 32462.99, 32463  (x, y)
    extent      : -16231.49, 11313351, -16231.5, 8976020  (xmin, xmax, ymin, ymax)
    coord. ref. : +proj=lcc +lat_0=50 +lon_0=-107 +lat_1=50 +lat_2=50 +x_0=5632642.22547 +y_0=4612545.65137 +datum=WGS84 +units=m +no_defs
    sources     : air.2m.2021.nc:air  (4 layers)
                  air.2m.2022.nc:air  (3 layers)
    varnames    : air (Daily Air Temperature at 2 m)
                  air (Daily Air Temperature at 2 m)
    names       : air.2~11228, air.2~11229, air.2~11230, air.2~11231, air.2~20101, air.2~20102, ...
    unit        :           K,           K,           K,           K,           K,           K, ...
    time        : 2021-12-28 to 2022-01-03 UTC

    terra::plot(air2m_process[[1]])

### Calculate covariates

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

- `covariate = "narr"`: NARR dataset acronym.
- `from = air2m_process`: processed `SpatRaster` object.
- `locs = tigris::counties("NC", year = 2021)`: North Carolina county boundaries.
- `locs_id = "NAME"`: county name identifier.
- `radius = 0`: size of buffer radius around each county.
- `geom = "terra"`: return covariates as a `SpatVector` object.

    library(tigris)
    air2m_covar <- amadeus::calculate_covariates(
      covariate = "narr",
      from = air2m_process,
      locs = tigris::counties("NC", year = 2021),
      locs_id = "NAME",
      radius = 0,
      geom = "terra"
    )

Check the calculated covariates `SpatVector` object.

    air2m_covar

    class       : SpatVector
    geometry    : polygons
    dimensions  : 700, 3  (geometries, attributes)
    extent      : 7731783, 8506154, 3248490, 3694532  (xmin, xmax, ymin, ymax)
    coord. ref. : +proj=lcc +lat_0=50 +lon_0=-107 +lat_1=50 +lat_2=50 +x_0=5632642.22547 +y_0=4612545.65137 +datum=WGS84 +units=m +no_defs
    names       :     NAME       time air.2m_0
    type        :    <chr>   <POSIXt>    <num>
    values      :  Chatham 2021-12-28    289.3
                  Alamance 2021-12-28    288.8
                  Davidson 2021-12-28    289.1

### Temporal summaries

The `aggregate` function can be used to calculate a summary statistic for each unique spatial point or polygon. In the following example, average `air.2m_0` is calculated for each county for the time period December 28, 2021 to January 3, 2022. `air.2m_0 ~ NAME` directs the function to summarize `air.2m_0` values per unique `NAME`. The `FUN = mean` directs the function to take the mean value. The `head()` function is applied to show only the first few entries, as the entire `data.frame` is 100 rows long.

    head(aggregate(air.2m_0 ~ NAME, data = air2m_covar, FUN = mean))

           NAME air.2m_0
    1  Alamance 289.5930
    2 Alexander 289.1961
    3 Alleghany 286.9486
    4     Anson 290.5306
    5      Ashe 285.5771
    6     Avery 285.2288