| Title: | Accessing and Analyzing Large-Scale Environmental Data |
|---|---|
| Description: | Functions are designed to facilitate access to and utility with large scale, publicly available environmental data in R. The package contains functions for downloading raw data files from web URLs (download_data()), processing the raw data files into clean spatial objects (process_covariates()), and extracting values from the spatial data objects at point and polygon locations (calculate_covariates()). These functions call a series of source-specific functions which are tailored to each data sources/datasets particular URL structure, data format, and spatial/temporal resolution. The functions are tested, versioned, and open source and open access. For sum_edc() method details, see Messier, Akita, and Serre (2012) <doi:10.1021/es203152a>. |
| Authors: | Mitchell Manware [aut, ctb]
 , Insang Song [aut,
ctb] , Eva Marques
[aut, ctb] , Mariana
Alifa Kassien [aut, ctb]
, Elizabeth Scholl
[ctb] , Kyle Messier
[aut, cre] ,
Spatiotemporal Exposures and Toxicology Group [cph] |
| Maintainer: | Kyle Messier <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.1.6 |
| Built: | 2024-11-14 11:12:33 UTC |
| Source: | CRAN |
sftime objectCreate a sftime object from one of data.frame,
data.table, sf, sftime, SpatRaster,
SpatRasterDataset, SpatVector
as_mysftime(x, ...)as_mysftime(x, ...)
x |
an object of class |
... |
if x is a data.frame or data.table: lonname, latname, timename and crs arguments are required. If x is a sf or sftime, timename argument is required. If x is a terra::SpatRaster, varname argument is required. |
an sftime object with constrained time column name
Eva Marques
check_mysftime, sf_as_mysftime, data.frame, data.table::data.table, terra::rast, terra::sds, terra::vect
The calculate_covariates() function extracts values at point
locations from a SpatRaster or SpatVector object returned from
process_covariates(). calculate_covariates() and the underlying
source-specific covariate functions have been designed to operate on the
processed objects. To avoid errors, do not edit the processed
SpatRaster or SpatVector objects before passing to
calculate_covariates().
calculate_covariates( covariate = c("modis", "koppen-geiger", "koeppen-geiger", "koppen", "koeppen", "geos", "dummies", "gmted", "sedac_groads", "groads", "roads", "ecoregions", "ecoregion", "hms", "smoke", "gmted", "narr", "geos", "sedac_population", "population", "nlcd", "merra", "merra2", "gridmet", "terraclimate", "tri", "nei"), from, locs, locs_id = "site_id", ... )calculate_covariates( covariate = c("modis", "koppen-geiger", "koeppen-geiger", "koppen", "koeppen", "geos", "dummies", "gmted", "sedac_groads", "groads", "roads", "ecoregions", "ecoregion", "hms", "smoke", "gmted", "narr", "geos", "sedac_population", "population", "nlcd", "merra", "merra2", "gridmet", "terraclimate", "tri", "nei"), from, locs, locs_id = "site_id", ... )
covariate |
character(1). Covariate type. |
from |
character. Single or multiple from strings. |
locs |
sf/SpatVector. Unique locations. Should include
a unique identifier field named |
locs_id |
character(1). Name of unique identifier.
Default is |
... |
Arguments passed to each covariate calculation function. |
Calculated covariates as a data.frame or SpatVector object
covariate argument value is converted to lowercase.
Insang Song
calculate_modis_par: "modis", "MODIS"
calculate_koppen_geiger: "koppen-geiger", "koeppen-geiger", "koppen"
calculate_ecoregion: "ecoregion", "ecoregions"
calculate_temporal_dummies: "dummies", "Dummies"
calculate_hms: "hms", "smoke", "HMS"
calculate_gmted: "gmted", "GMTED"
calculate_narr: "narr", "NARR"
calculate_geos: "geos", "geos_cf", "GEOS"
calculate_sedac_population: "population", "sedac_population"
calculate_sedac_groads: "roads", "groads", "sedac_groads"
calculate_nlcd: "nlcd", "NLCD"
calculate_tri: "tri", "TRI"
calculate_nei: "nei", "NEI"
calculate_merra2: "merra", "MERRA", "merra2", "MERRA2"
calculate_gridmet: "gridMET", "gridmet"
calculate_terraclimate: "terraclimate", "TerraClimate"
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_covariates( covariate = "narr", from = narr, # derived from process_covariates() example locs = loc, locs_id = "id", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_covariates( covariate = "narr", from = narr, # derived from process_covariates() example locs = loc, locs_id = "id", geom = FALSE ) ## End(Not run)
Extract ecoregions covariates (U.S. EPA Ecoregions Level 2/3) at point
locations. Returns a data.frame object containing locs_id and
binary (0 = point not in ecoregion; 1 = point in ecoregion) variables for
each ecoregion.
calculate_ecoregion(from = NULL, locs, locs_id = "site_id", geom = FALSE, ...)calculate_ecoregion(from = NULL, locs, locs_id = "site_id", geom = FALSE, ...)
from |
SpatVector(1). Output of |
locs |
sf/SpatVector. Unique locs. Should include
a unique identifier field named |
locs_id |
character(1). Name of unique identifier. |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object object with dummy variables and attributes of:
attr(., "ecoregion2_code"): Ecoregion lv.2 code and key
attr(., "ecoregion3_code"): Ecoregion lv.3 code and key
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_ecoregion( from = ecoregion, # derived from process_ecoregion() example locs = loc, locs_id = "id", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_ecoregion( from = ecoregion, # derived from process_ecoregion() example locs = loc, locs_id = "id", geom = FALSE ) ## End(Not run)
Extract atmospheric composition values at point locations. Returns a
data.frame object containing locs_id, date and hour, vertical
pressure level, and atmospheric composition variable. Atmospheric
composition variable column name reflects variable and circular buffer
radius.
calculate_geos( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )calculate_geos( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )
from |
SpatRaster(1). Output of |
locs |
data.frame, characater to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 0). |
fun |
character(1). Function used to summarize multiple raster cells
within sites location buffer (Default = |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_geos( from = geos, # derived from process_geos() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_geos( from = geos, # derived from process_geos() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)
Extract elevation values at point locations. Returns a data.frame
object containing locs_id, year of release, and elevation variable.
Elevation variable column name reflects the elevation statistic, spatial
resolution of from, and circular buffer radius (ie. Breakline Emphasis
at 7.5 arc-second resolution with 0 meter buffer: breakline_emphasis_r75_0).
calculate_gmted( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )calculate_gmted( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )
from |
SpatRaster(1). Output from |
locs |
data.frame. character to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 0). |
fun |
character(1). Function used to summarize multiple raster cells
within sites location buffer (Default = |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders |
a data.frame or SpatVector object
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_gmted( from = gmted, # derived from process_gmted() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_gmted( from = gmted, # derived from process_gmted() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)
Extract gridMET values at point locations. Returns a data.frame
object containing locs_id and gridMET variable. gridMET variable
column name reflects the gridMET variable and circular buffer radius.
calculate_gridmet( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )calculate_gridmet( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )
from |
SpatRaster(1). Output from |
locs |
data.frame. character to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 0). |
fun |
character(1). Function used to summarize multiple raster cells
within sites location buffer (Default = |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_gridmet( from = gridmet, # derived from process_gridmet() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_gridmet( from = gridmet, # derived from process_gridmet() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)
Extract wildfire smoke plume values at point locations. Returns a
data.frame object containing locs_id, date, and binary variable
for wildfire smoke plume density inherited from from (0 = point not
covered by wildfire smoke plume; 1 = point covered by wildfire smoke plume).
calculate_hms(from, locs, locs_id = NULL, radius = 0, geom = FALSE, ...)calculate_hms(from, locs, locs_id = NULL, radius = 0, geom = FALSE, ...)
from |
SpatVector(1). Output of |
locs |
data.frame, characater to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 0). |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_hms( from = hms, # derived from process_hms() example locs = loc, locs_id = "id", radius = 0, geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_hms( from = hms, # derived from process_hms() example locs = loc, locs_id = "id", radius = 0, geom = FALSE ) ## End(Not run)
Extract climate classification values at point locations. Returns a
data.frame object containing locs_id and
binary (0 = point not in climate region; 1 = point in climate region)
variables for each climate classification region.
calculate_koppen_geiger( from = NULL, locs = NULL, locs_id = "site_id", geom = FALSE, ... )calculate_koppen_geiger( from = NULL, locs = NULL, locs_id = "site_id", geom = FALSE, ... )
from |
SpatVector(1). Output of |
locs |
sf/SpatVector. Unique locs. Should include
a unique identifier field named |
locs_id |
character(1). Name of unique identifier. |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
The returned object contains a
$description column to represent the temporal range covered by the
dataset. For more information, see
https://www.nature.com/articles/sdata2018214.
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_koppen_geiger( from = kg, # derived from process_koppen_geiger() example locs = loc, locs_id = "id", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_koppen_geiger( from = kg, # derived from process_koppen_geiger() example locs = loc, locs_id = "id", geom = FALSE ) ## End(Not run)
The calculate_lagged() function calculates daily temporal lagged covariates
from the output of calculate_covariates() or calc_*().
calculate_lagged(from, date, lag, locs_id, time_id = "time", geom = FALSE)calculate_lagged(from, date, lag, locs_id, time_id = "time", geom = FALSE)
from |
data.frame(1). A |
date |
character(2). Start and end dates of desired lagged covariates. Length of 10 each, format YYYY-MM-DD (ex. September 1, 2023 = "2023-09-01"). |
lag |
integer(1). Number of lag days. |
locs_id |
character(1). Name of unique identifier. |
time_id |
character(1). Column containing time values. |
geom |
logical(1). Should the function return a |
a data.frame object
In order to calculate temporally lagged covariates, from must contain at
least the number of lag days before the desired start date. For example, if
date = c("2024-01-01", "2024-01-31) and lag = 1, from must contain data
starting at 2023-12-31.
If from contains geometry features, calculate_lagged will return a column
with geometry features of the same name.
calculate_lagged() assumes that all columns other than time_id,
locs_id, and fixed columns of "lat" and "lon", follow the genre, variable,
lag, buffer radius format adopted in calc_setcolumns().
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) terracliamte_covar <- calculate_terraclimate( from = terraclimate, # derived from process_terraclimate() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) calculate_lagged( from = terracliamte_covar, locs_id = "id", date = c("2023-01-02", "2023-01-10"), lag = 1, time_id = "time" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) terracliamte_covar <- calculate_terraclimate( from = terraclimate, # derived from process_terraclimate() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) calculate_lagged( from = terracliamte_covar, locs_id = "id", date = c("2023-01-02", "2023-01-10"), lag = 1, time_id = "time" ) ## End(Not run)
Extract meteorological and atmospheric values at point locations. Returns a
data.frame object containing locs_id, date and hour, vertical
pressure level, and meteorological or atmospheric variable. Variable column
name reflects variable and circular buffer radius.
calculate_merra2( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )calculate_merra2( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )
from |
SpatRaster(1). Output of |
locs |
data.frame, characater to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 0). |
fun |
character(1). Function used to summarize multiple raster cells
within sites location buffer (Default = |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders |
a data.frame or SpatVector object
Mitchell Manware
calculate_geos(), process_merra2()
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_merra2( from = merra2, # derived from process_merra2() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_merra2( from = merra2, # derived from process_merra2() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)
The function operates at MODIS/VIIRS products on a daily basis. Given that the raw hdf files are downloaded from NASA, standard file names include a data retrieval date flag starting with letter "A". Leveraging that piece of information, the function will select files of scope on the date of interest. Please note that this function does not provide a function to filter swaths or tiles, so it is strongly recommended to check and pre-filter the file names at users' discretion.
calculate_modis_daily( from = NULL, locs = NULL, locs_id = "site_id", radius = 0L, date = NULL, name_extracted = NULL, fun_summary = "mean", max_cells = 3e+07, geom = FALSE, ... )calculate_modis_daily( from = NULL, locs = NULL, locs_id = "site_id", radius = 0L, date = NULL, name_extracted = NULL, fun_summary = "mean", max_cells = 3e+07, geom = FALSE, ... )
from |
SpatRaster. Preprocessed objects. |
locs |
SpatVector/sf/sftime object. Locations where MODIS values are summarized. |
locs_id |
character(1). Field name where unique site identifiers
are stored. Default is |
radius |
numeric. Radius to generate circular buffers. |
date |
Date(1). date to query. |
name_extracted |
character. Names of calculated covariates. |
fun_summary |
function. Summary function for
multilayer rasters. Passed to |
max_cells |
integer(1). Maximum number of cells to be read at once.
Higher values will expedite processing, but will increase memory usage.
Maximum possible value is |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object.
Insang Song
Preprocessing: process_modis_merge(), process_modis_swath(),
process_blackmarble()
Parallelization: calculate_modis_par()
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: locs <- data.frame(lon = -78.8277, lat = 35.95013, id = "001") calculate_modis_daily( from = mod06l2_warp, # dervied from process_modis() example locs = locs, locs_id = "id", radius = 0, date = "2024-01-01", name_extracted = "cloud_fraction_0", fun_summary = "mean", max_cells = 3e7 ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: locs <- data.frame(lon = -78.8277, lat = 35.95013, id = "001") calculate_modis_daily( from = mod06l2_warp, # dervied from process_modis() example locs = locs, locs_id = "id", radius = 0, date = "2024-01-01", name_extracted = "cloud_fraction_0", fun_summary = "mean", max_cells = 3e7 ) ## End(Not run)
calculate_modis_par essentially runs calculate_modis_daily function
in each thread (subprocess). Based on daily resolution, each day's workload
will be distributed to each thread. With product argument,
the files are processed by a customized function where the unique structure
and/or characteristics of the products are considered. nthreads
argument should be carefully selected in consideration of the machine's
CPU and memory capacities as products have their own memory pressure.
locs should be sf object as it is exportable to parallel workers.
calculate_modis_par( from = NULL, locs = NULL, locs_id = "site_id", radius = c(0L, 1000L, 10000L, 50000L), preprocess = process_modis_merge, name_covariates = NULL, subdataset = NULL, fun_summary = "mean", nthreads = floor(length(parallelly::availableWorkers())/2), package_list_add = NULL, export_list_add = NULL, max_cells = 3e+07, geom = FALSE, ... )calculate_modis_par( from = NULL, locs = NULL, locs_id = "site_id", radius = c(0L, 1000L, 10000L, 50000L), preprocess = process_modis_merge, name_covariates = NULL, subdataset = NULL, fun_summary = "mean", nthreads = floor(length(parallelly::availableWorkers())/2), package_list_add = NULL, export_list_add = NULL, max_cells = 3e+07, geom = FALSE, ... )
from |
character. List of paths to MODIS/VIIRS files. |
locs |
sf/SpatVector object. Unique locs where covariates will be calculated. |
locs_id |
character(1). Site identifier. Default is |
radius |
numeric. Radii to calculate covariates.
Default is |
preprocess |
function. Function to handle HDF files. |
name_covariates |
character. Name header of covariates.
e.g., |
subdataset |
Indices, names, or search patterns for subdatasets. Find detail usage of the argument in notes. |
fun_summary |
character or function. Function to summarize extracted raster values. |
nthreads |
integer(1). Number of threads to be used to calculate covariates. |
package_list_add |
character. A vector with package names to load
these in each thread. Note that |
export_list_add |
character. A vector with object names to export to each thread. It should be minimized to spare memory. |
max_cells |
integer(1). Maximum number of cells to be read at once.
Higher values will expedite processing, but will increase memory usage.
Maximum possible value is |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Arguments passed to |
A data.frame or SpatVector with an attribute:
attr(., "dates_dropped"): Dates with insufficient tiles.
Note that the dates mean the dates with insufficient tiles,
not the dates without available tiles.
Overall, this function and dependent routines assume that the file
system can handle concurrent access to the (network) disk by multiple
processes. File system characteristics, package versions, and hardware
settings and specification can affect the processing efficiency.
locs is expected to be convertible to sf object. sf, SpatVector, and
other class objects that could be converted to sf can be used.
Common arguments in preprocess functions such as date and path are
automatically detected and passed to the function. Please note that
locs here and path in preprocess functions are assumed to have a
standard naming convention of raw files from NASA.
The argument subdataset should be in a proper format
depending on preprocess function:
process_modis_merge(): Regular expression pattern.
e.g., "^LST_"
process_modis_swath(): Subdataset names.
e.g., c("Cloud_Fraction_Day", "Cloud_Fraction_Night")
process_blackmarble(): Subdataset number.
e.g., for VNP46A2 product, 3L.
Dates with less than 80 percent of the expected number of tiles,
which are determined by the mode of the number of tiles, are removed.
Users will be informed of the dates with insufficient tiles.
The result data.frame will have an attribute with the dates with
insufficient tiles.
See details for setting parallelization:
This function leverages the calculation of single-day MODIS covariates:
Also, for preprocessing, please refer to:
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: locs <- data.frame(lon = -78.8277, lat = 35.95013, id = "001") locs <- terra::vect(locs, geom = c("lon", "lat"), crs = "EPSG:4326") calculate_modis_par( from = list.files("./data", pattern = "VNP46A2.", full.names = TRUE), locs = locs, locs_id = "site_id", radius = c(0L, 1000L), preprocess = process_modis_merge, name_covariates = "cloud_fraction_0", subdataset = "Cloud_Fraction", fun_summary = "mean", nthreads = 1 ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: locs <- data.frame(lon = -78.8277, lat = 35.95013, id = "001") locs <- terra::vect(locs, geom = c("lon", "lat"), crs = "EPSG:4326") calculate_modis_par( from = list.files("./data", pattern = "VNP46A2.", full.names = TRUE), locs = locs, locs_id = "site_id", radius = c(0L, 1000L), preprocess = process_modis_merge, name_covariates = "cloud_fraction_0", subdataset = "Cloud_Fraction", fun_summary = "mean", nthreads = 1 ) ## End(Not run)
Extract meteorological values at point locations. Returns a data.frame
object containing locs_id, date, vertical pressure level, and
meteorological variable. Meteorological variable column name reflects
variable and circular buffer radius.
calculate_narr( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )calculate_narr( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )
from |
SpatRaster(1). Output of |
locs |
data.frame, characater to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 0). |
fun |
character(1). Function used to summarize multiple raster cells
within sites location buffer (Default = |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders |
a data.frame or SpatVector object
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_narr( from = narr, # derived from process_narr() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_narr( from = narr, # derived from process_narr() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)
Calculate road emissions covariates
calculate_nei(from = NULL, locs = NULL, locs_id = "site_id", geom = FALSE, ...)calculate_nei(from = NULL, locs = NULL, locs_id = "site_id", geom = FALSE, ...)
from |
SpatVector(1). Output of |
locs |
sf/SpatVector. Locations at NEI values are joined. |
locs_id |
character(1). Unique site identifier column name. Unused but kept for compatibility. |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
Insang Song, Ranadeep Daw
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_nei( from = nei, # derived from process_nei example locs = loc, locs_id = "id" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_nei( from = nei, # derived from process_nei example locs = loc, locs_id = "id" ) ## End(Not run)
Compute ratio of land cover class in circle buffers around points. Returns
a data.frame object containing locs_id, longitude, latitude,
time (year), and computed ratio for each land cover class.
calculate_nlcd( from, locs, locs_id = "site_id", mode = c("exact", "terra"), radius = 1000, max_cells = 5e+07, geom = FALSE, nthreads = 1L, ... )calculate_nlcd( from, locs, locs_id = "site_id", mode = c("exact", "terra"), radius = 1000, max_cells = 5e+07, geom = FALSE, nthreads = 1L, ... )
from |
SpatRaster(1). Output of |
locs |
terra::SpatVector of points geometry |
locs_id |
character(1). Unique identifier of locations |
mode |
character(1). One of |
radius |
numeric (non-negative) giving the radius of buffer around points |
max_cells |
integer(1). Maximum number of cells to be read at once.
Higher values may expedite processing, but will increase memory usage.
Maximum possible value is |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
nthreads |
integer(1). Number of threads to be used |
... |
Placeholders. |
a data.frame or SpatVector object
NLCD is available in U.S. only. Users should be aware of
the spatial extent of the data. The results are different depending
on mode argument. The "terra" mode is less memory intensive
but less accurate because it counts the number of cells
intersecting with the buffer. The "exact" may be more accurate
but uses more memory as it will account for the partial overlap
with the buffer.
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_nlcd( from = nlcd, # derived from process_nlcd() example locs = loc, locs_id = "id", mode = "exact", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_nlcd( from = nlcd, # derived from process_nlcd() example locs = loc, locs_id = "id", mode = "exact", geom = FALSE ) ## End(Not run)
Prepared groads data is clipped with the buffer polygons
of radius. The total length of the roads are calculated.
Then the density of the roads is calculated by dividing
the total length from the area of the buffer. terra::linearUnits()
is used to convert the unit of length to meters.
calculate_sedac_groads( from = NULL, locs = NULL, locs_id = NULL, radius = 1000, fun = "sum", geom = FALSE, ... )calculate_sedac_groads( from = NULL, locs = NULL, locs_id = NULL, radius = 1000, fun = "sum", geom = FALSE, ... )
from |
SpatVector(1). Output of |
locs |
data.frame, characater to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 1000). |
fun |
function(1). Function used to summarize the length of roads
within sites location buffer (Default is |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
Unit is km / sq km. The returned data.frame object contains a
$time column to represent the temporal range covered by the
dataset. For more information, see https://earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-groads-v1-1.00.
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_sedac_groads( from = groads, # derived from process_sedac_groads() example locs = loc, locs_id = "id", radius = 1000, fun = "sum", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_sedac_groads( from = groads, # derived from process_sedac_groads() example locs = loc, locs_id = "id", radius = 1000, fun = "sum", geom = FALSE ) ## End(Not run)
Extract population density values at point locations. Returns a
data.frame object containing locs_id, year, and population
density variable. Population density variable column name reflects
spatial resolution of from and circular buffer radius.
calculate_sedac_population( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )calculate_sedac_population( from, locs, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )
from |
SpatRaster(1). Output of |
locs |
data.frame, characater to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 0). |
fun |
character(1). Function used to summarize multiple raster cells
within sites location buffer (Default = |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders |
a data.frame or SpatVector object
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_sedac_population( from = pop, # derived from process_sedac_population() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_sedac_population( from = pop, # derived from process_sedac_population() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)
Calculate temporal dummy covariates at point locations. Returns a
data.frame object with locs_id, year binary variable for each
value in year, and month and day of week binary variables.
calculate_temporal_dummies( locs, locs_id = "site_id", year = seq(2018L, 2022L), geom = FALSE, ... )calculate_temporal_dummies( locs, locs_id = "site_id", year = seq(2018L, 2022L), geom = FALSE, ... )
locs |
data.frame with a temporal field named |
locs_id |
character(1). Unique site identifier column name.
Default is |
year |
integer. Year domain to dummify.
Default is |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_temporal_dummies( locs = loc, locs_id = "id", year = seq(2018L, 2022L) ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_temporal_dummies( locs = loc, locs_id = "id", year = seq(2018L, 2022L) ) ## End(Not run)
Extract TerraClimate values at point locations. Returns a data.frame
object containing locs_id and TerraClimate variable. TerraClimate
variable column name reflects the TerraClimate variable and
circular buffer radius.
calculate_terraclimate( from = NULL, locs = NULL, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )calculate_terraclimate( from = NULL, locs = NULL, locs_id = NULL, radius = 0, fun = "mean", geom = FALSE, ... )
from |
SpatRaster(1). Output from |
locs |
data.frame. character to file path, SpatVector, or sf object. |
locs_id |
character(1). Column within |
radius |
integer(1). Circular buffer distance around site locations. (Default = 0). |
fun |
character(1). Function used to summarize multiple raster cells
within sites location buffer (Default = |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
TerraClimate data has monthly temporal resolution, so the $time column
will contain the year and month in YYYYMM format (ie. January, 2018 =
201801).
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_terraclimate( from = terraclimate, # derived from process_terraclimate() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_terraclimate( from = terraclimate, # derived from process_terraclimate() example locs = loc, locs_id = "id", radius = 0, fun = "mean", geom = FALSE ) ## End(Not run)
Extract toxic release values at point locations. Returns a data.frame
object containing locs_id and variables for each chemical in
from.
calculate_tri( from = NULL, locs, locs_id = "site_id", radius = c(1000L, 10000L, 50000L), geom = FALSE, ... )calculate_tri( from = NULL, locs, locs_id = "site_id", radius = c(1000L, 10000L, 50000L), geom = FALSE, ... )
from |
SpatVector(1). Output of |
locs |
sf/SpatVector. Locations where TRI variables are calculated. |
locs_id |
character(1). Unique site identifier column name.
Default is |
radius |
Circular buffer radius.
Default is |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
... |
Placeholders. |
a data.frame or SpatVector object
U.S. context.
Insang Song, Mariana Kassien
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_tri( from = tri, # derived from process_tri() example locs = loc, locs_id = "id", radius = c(1e3L, 1e4L, 5e4L) ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: loc <- data.frame(id = "001", lon = -78.90, lat = 35.97) calculate_tri( from = tri, # derived from process_tri() example locs = loc, locs_id = "id", radius = c(1e3L, 1e4L, 5e4L) ) ## End(Not run)
The download_aqs() function accesses and downloads Air Quality System (AQS) data from the U.S. Environmental Protection Agency's (EPA) Pre-Generated Data Files.
download_aqs( parameter_code = 88101, resolution_temporal = "daily", year = c(2018, 2022), url_aqs_download = "https://aqs.epa.gov/aqsweb/airdata/", directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )download_aqs( parameter_code = 88101, resolution_temporal = "daily", year = c(2018, 2022), url_aqs_download = "https://aqs.epa.gov/aqsweb/airdata/", directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )
parameter_code |
integer(1). length of 5. EPA pollutant parameter code. For details, please refer to AQS parameter codes |
resolution_temporal |
character(1).
Name of column containing POC values.
Currently, no value other than |
year |
character(1 or 2). length of 4. Year or start/end years for downloading data. |
url_aqs_download |
character(1). URL to the AQS pre-generated datasets. |
directory_to_save |
character(1). Directory to save data. Two sub-directories will be created for the downloaded zip files ("/zip_files") and the unzipped data files ("/data_files"). |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip zip files. Default |
remove_zip |
logical(1). Remove zip file from directory_to_download.
Default |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
directory_to_save.
Mariana Kassien, Insang Song, Mitchell Manware
U.S. Environmental Protection Agency (2023). “Air Quality System Data Mart [internet database].” https://www.epa.gov/outdoor-air-quality-data.
## Not run: download_aqs( parameter_code = 88101, resolution_temporal = "daily", year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_aqs( parameter_code = 88101, resolution_temporal = "daily", year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
Accesses and downloads United States Department of Agriculture CropScape Cropland Data Layer data from the USDA National Agricultural Statistics Service or the George Mason University website.
download_cropscape( year = seq(1997, 2023), source = c("USDA", "GMU"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, hash = FALSE )download_cropscape( year = seq(1997, 2023), source = c("USDA", "GMU"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, hash = FALSE )
year |
integer(1). Year of the data to download. |
source |
character(1). Data source, one of
|
directory_to_save |
character(1). Directory to download files. |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip the downloaded compressed files.
Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Yearly comma-separated value (CSV) files will be stored in
directory_to_save.
JSON files should be found at STAC catalog of OpenLandMap
Insang Song
## Not run: download_cropscape( year = 2020, source = "USDA", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_cropscape( year = 2020, source = "USDA", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
The download_data() function accesses and downloads atmospheric, meteorological, and environmental data from various open-access data sources.
download_data( dataset_name = c("aqs", "ecoregion", "ecoregions", "geos", "gmted", "koppen", "koppengeiger", "merra2", "merra", "modis", "narr", "nlcd", "noaa", "sedac_groads", "sedac_population", "groads", "population", "hms", "smoke", "tri", "nei", "gridmet", "terraclimate", "huc", "cropscape", "cdl", "prism"), directory_to_save = NULL, acknowledgement = FALSE, hash = FALSE, ... )download_data( dataset_name = c("aqs", "ecoregion", "ecoregions", "geos", "gmted", "koppen", "koppengeiger", "merra2", "merra", "modis", "narr", "nlcd", "noaa", "sedac_groads", "sedac_population", "groads", "population", "hms", "smoke", "tri", "nei", "gridmet", "terraclimate", "huc", "cropscape", "cdl", "prism"), directory_to_save = NULL, acknowledgement = FALSE, hash = FALSE, ... )
dataset_name |
character(1). Dataset to download. |
directory_to_save |
character(1). Directory to save / unzip (if zip files are downloaded) data. |
acknowledgement |
logical(1). By setting |
hash |
logical(1). By setting |
... |
Arguments passed to each download function. |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Data files will be downloaded and stored in respective
sub-directories within directory_to_save. File format and
sub-directory names depend on data source and dataset of interest.
All download function names are in download_* formats
Insang Song
For details of each download function per dataset, Please refer to:
download_aqs: "aqs", "AQS"
download_ecoregion: "ecoregions", "ecoregion"
download_geos: "geos"
download_gmted: "gmted", "GMTED"
download_koppen_geiger: "koppen", "koppengeiger"
download_merra2: "merra2", "merra", "MERRA", "MERRA2"
download_narr: "narr"
download_nlcd: "nlcd", "NLCD"
download_hms: "noaa", "smoke", "hms"
download_sedac_groads: "sedac_groads", "groads"
download_sedac_population: "sedac_population",
"population"
download_modis: "modis", "MODIS"
download_tri: "tri", "TRI"
download_nei: "nei", "NEI"
download_gridmet: "gridMET", "gridmet"
download_terraclimate: "TerraClimate", "terraclimate"
download_huc: "huc"
download_cropscape: "cropscape", "cdl"
download_prism: "prism"
## Not run: download_data( dataset_name = "narr", variables = "weasd", year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)## Not run: download_data( dataset_name = "narr", variables = "weasd", year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)
The download_ecoregion() function accesses and downloads United States Ecoregions data from the U.S. Environmental Protection Agency's (EPA) Ecorgions. Level 3 data, where all pieces of information in the higher levels are included, are downloaded.
download_ecoregion( epa_certificate_path = system.file("extdata/cacert_gaftp_epa.pem", package = "amadeus"), certificate_url = "http://cacerts.digicert.com/DigiCertGlobalG2TLSRSASHA2562020CA1-1.crt", directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )download_ecoregion( epa_certificate_path = system.file("extdata/cacert_gaftp_epa.pem", package = "amadeus"), certificate_url = "http://cacerts.digicert.com/DigiCertGlobalG2TLSRSASHA2562020CA1-1.crt", directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )
epa_certificate_path |
character(1). Path to the certificate file for EPA DataCommons. Default is 'extdata/cacert_gaftp_epa.pem' under the package installation path. |
certificate_url |
character(1). URL to certificate file. See notes for details. |
directory_to_save |
character(1). Directory to save data. Two sub-directories will be created for the downloaded zip files ("/zip_files") and the unzipped data files ("/data_files"). |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip zip files. Default |
remove_zip |
logical(1). Remove zip file from
|
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
directory_to_save.
For EPA Data Commons certificate errors, follow the steps below:
Click Lock icon in the address bar at https://gaftp.epa.gov
Click Show Certificate
Access Details
Find URL with *.crt extension Currently we bundle the pre-downloaded crt and its PEM (which is accepted in wget command) file in ./inst/extdata. The instruction above is for certificate updates in the future.
Insang Song
Omernik JM, Griffith GE (2014). “Ecoregions of the Conterminous United States: Evolution of a Hierarchical Spatial Framework.” Environmental Management, 54(6), 1249–1266. ISSN 0364-152X, 1432-1009, doi:10.1007/s00267-014-0364-1, https://link.springer.com/article/10.1007/s00267-014-0364-1.
## Not run: download_ecoregion( directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_ecoregion( directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
The download_geos() function accesses and downloads various
atmospheric composition collections from NASA's Global Earth Observing System (GEOS) model.
download_geos( collection = c("aqc_tavg_1hr_g1440x721_v1", "chm_tavg_1hr_g1440x721_v1", "met_tavg_1hr_g1440x721_x1", "xgc_tavg_1hr_g1440x721_x1", "chm_inst_1hr_g1440x721_p23", "met_inst_1hr_g1440x721_p23"), date = c("2018-01-01", "2018-01-01"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )download_geos( collection = c("aqc_tavg_1hr_g1440x721_v1", "chm_tavg_1hr_g1440x721_v1", "met_tavg_1hr_g1440x721_x1", "xgc_tavg_1hr_g1440x721_x1", "chm_inst_1hr_g1440x721_p23", "met_inst_1hr_g1440x721_p23"), date = c("2018-01-01", "2018-01-01"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )
collection |
character(1). GEOS-CF data collection file name. |
date |
character(1 or 2). length of 10. Date or start/end dates for downloading data.
Format "YYYY-MM-DD" (ex. January 1, 2018 = |
directory_to_save |
character(1). Directory to save data.
Sub-directories will be created within |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
netCDF (.nc4) files will be stored in a
collection-specific folder within directory_to_save.
Mitchell Manware, Insang Song
Keller CA, Knowland KE, Duncan BN, Liu J, Anderson DC, Das S, Lucchesi RA, Lundgren EW, Nicely JM, Nielsen E, Ott LE, Saunders E, Strode SA, Wales PA, Jacob DJ, Pawson S (2021). “Description of the NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0.” Journal of Advances in Modeling Earth Systems, 13(4), e2020MS002413. ISSN 1942-2466, 1942-2466, doi:10.1029/2020MS002413.
## Not run: download_geos( collection = "aqc_tavg_1hr_g1440x721_v1", date = "2024-01-01", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)## Not run: download_geos( collection = "aqc_tavg_1hr_g1440x721_v1", date = "2024-01-01", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)
The download_gmted() function accesses and downloads Global
Multi-resolution Terrain Elevation Data (GMTED2010) from
U.S. Geological Survey and National Geospatial-Intelligence Agency.
download_gmted( statistic = c("Breakline Emphasis", "Systematic Subsample", "Median Statistic", "Minimum Statistic", "Mean Statistic", "Maximum Statistic", "Standard Deviation Statistic"), resolution = c("7.5 arc-seconds", "15 arc-seconds", "30 arc-seconds"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )download_gmted( statistic = c("Breakline Emphasis", "Systematic Subsample", "Median Statistic", "Minimum Statistic", "Mean Statistic", "Maximum Statistic", "Standard Deviation Statistic"), resolution = c("7.5 arc-seconds", "15 arc-seconds", "30 arc-seconds"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )
statistic |
character(1). Available statistics include |
resolution |
character(1). Available resolutions include |
directory_to_save |
character(1). Directory to save data. Two sub-directories will be created for the downloaded zip files ("/zip_files") and the unzipped data files ("/data_files"). |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip zip files. Default is |
remove_zip |
logical(1). Remove zip file from directory_to_download.
Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
directory_to_save.
Mitchell Manware, Insang Song
Danielson JJ, Gesch DB (2011). “Global multi-resolution terrain elevation data 2010 (GMTED2010).” Open-File Report 2011-1073, U.S. Geological Survey. Series: Open-File Report, https://doi.org/10.3133/ofr20111073.
## Not run: download_gmted( statistic = "Breakline Emphasis", resolution = "7.5 arc-seconds", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_gmted( statistic = "Breakline Emphasis", resolution = "7.5 arc-seconds", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
The download_gridmet function accesses and downloads gridded surface meteorological data from the University of California Merced Climatology Lab's gridMET dataset.
download_gridmet( variables = NULL, year = c(2018, 2022), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )download_gridmet( variables = NULL, year = c(2018, 2022), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )
variables |
character(1). Variable(s) name(s). See gridMET Generate Wget File for variable names and acronym codes. (Note: variable "Burning Index" has code "bi" and variable "Energy Release Component" has code "erc"). |
year |
character(1 or 2). length of 4. Year or start/end years for downloading data. |
directory_to_save |
character(1). Directory(s) to save downloaded data files. |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
netCDF (.nc) files will be stored in a variable-specific
folder within directory_to_save.
Mitchell Manware
Abatzoglou JT (2013). “Development of gridded surface meteorological data for ecological applications and modelling.” International journal of climatology, 33(1), 121–131.
## Not run: download_gridmet( variables = "Precipitation", year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)## Not run: download_gridmet( variables = "Precipitation", year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)
The download_hms() function accesses and downloads
wildfire smoke plume coverage data from NOAA's Hazard Mapping System Fire and Smoke Product.
download_hms( data_format = "Shapefile", date = c("2018-01-01", "2018-01-01"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )download_hms( data_format = "Shapefile", date = c("2018-01-01", "2018-01-01"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )
data_format |
character(1). "Shapefile" or "KML". |
date |
character(1 or 2). length of 10. Date or start/end dates for downloading data.
Format "YYYY-MM-DD" (ex. January 1, 2018 = |
directory_to_save |
character(1). Directory to save data. If
|
acknowledgement |
logical(1).
By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip zip files. Default is |
remove_zip |
logical(1). Remove zip files from
directory_to_download. Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
respective sub-directories within directory_to_save.
Mitchell Manware, Insang Song
(????). “Hazard Mapping System Fire and Smoke Product: Hazard Mapping System.” https://www.ospo.noaa.gov/products/land/hms.html#about. https://www.ospo.noaa.gov/products/land/hms.html#about.
## Not run: download_hms( data_format = "Shapefile", date = "2024-01-01", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_hms( data_format = "Shapefile", date = "2024-01-01", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
NHDPlus data provides the most comprehensive and high-resolution hydrography data. This function downloads national dataset from NHDPlus Version 2.1 on USGS Amazon S3 storage.
download_huc( region = c("Lower48", "Islands"), type = c("Seamless", "OceanCatchment"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = FALSE, hash = FALSE )download_huc( region = c("Lower48", "Islands"), type = c("Seamless", "OceanCatchment"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = FALSE, hash = FALSE )
region |
character(1). One of |
type |
character(1). One of |
directory_to_save |
character(1). Directory to download files. |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip the downloaded compressed files.
Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Downloaded files will be stored in directory_to_save.
For HUC, set type = "Seamless". HUC12 layer presents in the seamless
geodatabase. Users can aggregate HUC12 layer to make HUC6, HUC8, HUC10, etc.
For whom wants to download a specific region,
please visit Get NHDPlus Data
Insang Song
U.S. Geological Survey (2023). “National Hydrography Dataset (NHD) – USGS National Map Downloadable Data Collection.” https://www.usgs.gov/national-hydrography.
## Not run: download_huc( region = "Lower48", type = "Seamless", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_huc( region = "Lower48", type = "Seamless", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
The download_koppen_geiger() function accesses and downloads
climate classification data from the Present and future
Köppen-Geiger climate classification maps at
1-km resolution(link for article; link for data).
download_koppen_geiger( data_resolution = c("0.0083", "0.083", "0.5"), time_period = c("Present", "Future"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )download_koppen_geiger( data_resolution = c("0.0083", "0.083", "0.5"), time_period = c("Present", "Future"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )
data_resolution |
character(1). Available resolutions are |
time_period |
character(1). Available times are |
directory_to_save |
character(1). Directory to save data. Two sub-directories will be created for the downloaded zip files ("/zip_files") and the unzipped shapefiles ("/data_files"). |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip zip files. Default is |
remove_zip |
logical(1). Remove zip files from directory_to_download.
Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
respective sub-directories within directory_to_save.
Mitchell Manware, Insang Song
Beck HE, McVicar TR, Vergopolan N, Berg A, Lutsko NJ, Dufour A, Zeng Z, Jiang X, Van Dijk AIJM, Miralles DG (2023). “High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections.” Scientific Data, 10(1), 724. ISSN 2052-4463, doi:10.1038/s41597-023-02549-6, https://www.nature.com/articles/s41597-023-02549-6.
Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018). “Present and future Köppen-Geiger climate classification maps at 1-km resolution.” Scientific data, 5(1), 1–12. doi:10.1038/sdata.2018.214.
## Not run: download_koppen_geiger( data_resolution = "0.0083", time_period = "Present", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_koppen_geiger( data_resolution = "0.0083", time_period = "Present", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
The download_merra2() function accesses and downloads various
meteorological and atmospheric collections from NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model.
download_merra2( collection = c("inst1_2d_asm_Nx", "inst1_2d_int_Nx", "inst1_2d_lfo_Nx", "inst3_3d_asm_Np", "inst3_3d_aer_Nv", "inst3_3d_asm_Nv", "inst3_3d_chm_Nv", "inst3_3d_gas_Nv", "inst3_2d_gas_Nx", "inst6_3d_ana_Np", "inst6_3d_ana_Nv", "statD_2d_slv_Nx", "tavg1_2d_adg_Nx", "tavg1_2d_aer_Nx", "tavg1_2d_chm_Nx", "tavg1_2d_csp_Nx", "tavg1_2d_flx_Nx", "tavg1_2d_int_Nx", "tavg1_2d_lfo_Nx", "tavg1_2d_lnd_Nx", "tavg1_2d_ocn_Nx", "tavg1_2d_rad_Nx", "tavg1_2d_slv_Nx", "tavg3_3d_mst_Ne", "tavg3_3d_trb_Ne", "tavg3_3d_nav_Ne", "tavg3_3d_cld_Np", "tavg3_3d_mst_Np", "tavg3_3d_rad_Np", "tavg3_3d_tdt_Np", "tavg3_3d_trb_Np", "tavg3_3d_udt_Np", "tavg3_3d_odt_Np", "tavg3_3d_qdt_Np", "tavg3_3d_asm_Nv", "tavg3_3d_cld_Nv", "tavg3_3d_mst_Nv", "tavg3_3d_rad_Nv", "tavg3_2d_glc_Nx"), date = c("2018-01-01", "2018-01-01"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )download_merra2( collection = c("inst1_2d_asm_Nx", "inst1_2d_int_Nx", "inst1_2d_lfo_Nx", "inst3_3d_asm_Np", "inst3_3d_aer_Nv", "inst3_3d_asm_Nv", "inst3_3d_chm_Nv", "inst3_3d_gas_Nv", "inst3_2d_gas_Nx", "inst6_3d_ana_Np", "inst6_3d_ana_Nv", "statD_2d_slv_Nx", "tavg1_2d_adg_Nx", "tavg1_2d_aer_Nx", "tavg1_2d_chm_Nx", "tavg1_2d_csp_Nx", "tavg1_2d_flx_Nx", "tavg1_2d_int_Nx", "tavg1_2d_lfo_Nx", "tavg1_2d_lnd_Nx", "tavg1_2d_ocn_Nx", "tavg1_2d_rad_Nx", "tavg1_2d_slv_Nx", "tavg3_3d_mst_Ne", "tavg3_3d_trb_Ne", "tavg3_3d_nav_Ne", "tavg3_3d_cld_Np", "tavg3_3d_mst_Np", "tavg3_3d_rad_Np", "tavg3_3d_tdt_Np", "tavg3_3d_trb_Np", "tavg3_3d_udt_Np", "tavg3_3d_odt_Np", "tavg3_3d_qdt_Np", "tavg3_3d_asm_Nv", "tavg3_3d_cld_Nv", "tavg3_3d_mst_Nv", "tavg3_3d_rad_Nv", "tavg3_2d_glc_Nx"), date = c("2018-01-01", "2018-01-01"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )
collection |
character(1). MERRA-2 data collection file name. |
date |
character(1 or 2). length of 10. Date or start/end dates for downloading data.
Format "YYYY-MM-DD" (ex. January 1, 2018 = |
directory_to_save |
character(1). Directory to save data. |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
netCDF (.nc4) files will be stored in a
collection-specific folder within directory_to_save.
Mitchell Manware, Insang Song
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst1_2d_ asm_ Nx: 2d,3-Hourly,Instantaneous,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4.” doi:10.5067/3Z173KIE2TPD, https://disc.gsfc.nasa.gov/datasets/M2I1NXASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst1_2d_ int_ Nx: 2d,1-Hourly,Instantaneous,Single-Level,Assimilation,Vertically Integrated Diagnostics V5.12.4.” doi:10.5067/G0U6NGQ3BLE0, https://disc.gsfc.nasa.gov/datasets/M2I1NXINT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst1_2d_ lfo_ Nx: 2d,1-Hourly,Instantaneous,Single-Level,Assimilation,Land Surface Forcings V5.12.4.” doi:10.5067/RCMZA6TL70BG, https://disc.gsfc.nasa.gov/datasets/M2I1NXLFO_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst3_3d_ asm_ Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4.” doi:10.5067/QBZ6MG944HW0, https://disc.gsfc.nasa.gov/datasets/M2I3NPASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst3_3d_ aer_ Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio V5.12.4.” doi:10.5067/LTVB4GPCOTK2, https://disc.gsfc.nasa.gov/datasets/M2I3NVAER_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst3_3d_ asm_ Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields V5.12.4.” doi:10.5067/WWQSXQ8IVFW8, https://disc.gsfc.nasa.gov/datasets/M2I3NVASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst3_3d_ chm_ Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Carbon Monoxide and Ozone Mixing Ratio V5.12.4.” doi:10.5067/HO9OVZWF3KW2, https://disc.gsfc.nasa.gov/datasets/M2I3NVCHM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst3_3d_ gas_ Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio Analysis Increments V5.12.4.” doi:10.5067/96BUID8HGGX5, https://disc.gsfc.nasa.gov/datasets/M2I3NVGAS_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst3_2d_ gas_ Nx: 2d,3-Hourly,Instantaneous,Single-Level,Assimilation,Aerosol Optical Depth Analysis V5.12.4.” doi:10.5067/HNGA0EWW0R09, https://disc.gsfc.nasa.gov/datasets/M2I3NXGAS_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst6_3d_ ana_ Np: 3d,6-Hourly,Instantaneous,Pressure-Level,Analysis,Analyzed Meteorological Fields V5.12.4.” doi:10.5067/A7S6XP56VZWS, https://disc.gsfc.nasa.gov/datasets/M2I6NPANA_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 inst6_3d_ ana_ Nv: 3d,6-Hourly,Instantaneous,Model-Level,Analysis,Analyzed Meteorological Fields V5.12.4.” doi:10.5067/IUUF4WB9FT4W, https://disc.gsfc.nasa.gov/datasets/M2I6NVANA_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 statD_2d_ slv_ Nx: 2d,Monthly,Aggregated Statistics,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4.” doi:10.5067/KVIMOMCUO83U, https://disc.gsfc.nasa.gov/datasets/M2SMNXSLV_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 statD_2d_ slv_ Nx: 2d,Daily,Aggregated Statistics,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4.” doi:10.5067/9SC1VNTWGWV3, https://disc.gsfc.nasa.gov/datasets/M2SDNXSLV_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ adg_ Nx: 2d,3-Hourly,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics (extended) V5.12.4.” doi:10.5067/HM00OHQBHKTP, https://disc.gsfc.nasa.gov/datasets/M2T1NXADG_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ aer_ Nx: 2d,1-Hourly,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics V5.12.4.” doi:10.5067/KLICLTZ8EM9D, https://disc.gsfc.nasa.gov/datasets/M2T1NXAER_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ chm_ Nx: 2d,3-Hourly,Time-Averaged,Single-Level,Assimilation,Carbon Monoxide and Ozone Diagnostics V5.12.4.” doi:10.5067/3RQ5YS674DGQ, https://disc.gsfc.nasa.gov/datasets/M2T1NXCHM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ csp_ Nx: 2d,1-Hourly,Time-averaged,Single-Level,Assimilation,COSP Satellite Simulator V5.12.4.” doi:10.5067/H0VVAD8F6MX5, https://disc.gsfc.nasa.gov/datasets/M2T1NXCSP_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ flx_ Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4.” doi:10.5067/7MCPBJ41Y0K6, https://disc.gsfc.nasa.gov/datasets/M2T1NXFLX_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ int_ Nx: 2d,1-Hourly,Time-averaged,Single-Level,Assimilation,Vertically Integrated Diagnostics V5.12.4.” doi:10.5067/Q5GVUVUIVGO7, https://disc.gsfc.nasa.gov/datasets/M2T1NXINT_5.12.4/summary.
Pawson S (2020). “MERRA-2 tavg1_2d_ lfo_ Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Forcings V5.12.4.” doi:10.5067/L0T5GEG1NYFA, https://disc.gsfc.nasa.gov/datasets/M2T1NXLFO_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ lnd_ Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4.” doi:10.5067/RKPHT8KC1Y1T, https://disc.gsfc.nasa.gov/datasets/M2T1NXLND_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ ocn_ Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Ocean Surface Diagnostics V5.12.4.” doi:10.5067/Y67YQ1L3ZZ4R, https://disc.gsfc.nasa.gov/datasets/M2T1NXOCN_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ rad_ Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Radiation Diagnostics V5.12.4.” doi:10.5067/Q9QMY5PBNV1T, https://disc.gsfc.nasa.gov/datasets/M2T1NXRAD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg1_2d_ slv_ Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4.” doi:10.5067/VJAFPLI1CSIV, https://disc.gsfc.nasa.gov/datasets/M2T1NXSLV_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ mst_ Ne: 3d,3-Hourly,Time-Averaged,Model-Level Edge,Assimilation,Moist Processes Diagnostics V5.12.4.” doi:10.5067/JRUZ3SJ3ZJ72, https://disc.gsfc.nasa.gov/datasets/M2T3NEMST_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ trb_ Ne: 3d,3-Hourly,Time-Averaged,Model-Level Edge,Assimilation,Turbulence Diagnostics V5.12.4.” doi:10.5067/4I7ZI35QRH8K, https://disc.gsfc.nasa.gov/datasets/M2T3NETRB_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ nav_ Ne: 3d,3-Hourly,Time-Averaged, Vertical Coordinates V5.12.4.” doi:10.5067/N5WAKNS1UYQN, https://disc.gsfc.nasa.gov/datasets/M2T3NENAV_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ cld_ Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Cloud Diagnostics V5.12.4.” doi:10.5067/TX10URJSKT53, https://disc.gsfc.nasa.gov/datasets/M2T3NPCLD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ mst_ Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Moist Processes Diagnostics V5.12.4.” doi:10.5067/0TUFO90Q2PMS, https://disc.gsfc.nasa.gov/datasets/M2T3NPMST_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ rad_ Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Radiation Diagnostics V5.12.4.” doi:10.5067/3UGE8WQXZAOK, https://disc.gsfc.nasa.gov/datasets/M2T3NPRAD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ tdt_ Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Temperature Tendencies V5.12.4.” doi:10.5067/9NCR9DDDOPFI, https://disc.gsfc.nasa.gov/datasets/M2T3NPTDT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ trb_ Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Turbulence Diagnostics V5.12.4.” doi:10.5067/ZRRJPGWL8AVL, https://disc.gsfc.nasa.gov/datasets/M2T3NPTRB_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ udt_ Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Wind Tendencies V5.12.4.” doi:10.5067/CWV0G3PPPWFW, https://disc.gsfc.nasa.gov/datasets/M2T3NPUDT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ odt_ Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Ozone Tendencies V5.12.4.” doi:10.5067/S0LYTK57786Z, https://disc.gsfc.nasa.gov/datasets/M2T3NPODT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ qdt_ Np: 3d,3-Hourly,Time-Averaged,Pressure-Level,Assimilation,Moist Tendencies V5.12.4.” doi:10.5067/A9KWADY78YHQ, https://disc.gsfc.nasa.gov/datasets/M2T3NPQDT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ asm_ Nv: 3d,3-Hourly,Time-Averaged,Model-Level,Assimilation,Assimilated Meteorological Fields V5.12.4.” doi:10.5067/SUOQESM06LPK, https://disc.gsfc.nasa.gov/datasets/M2T3NVASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ cld_ Nv: 3d,3-Hourly,Time-Averaged,Model-Level,Assimilation,Cloud Diagnostics V5.12.4.” doi:10.5067/F9353J0FAHIH, https://disc.gsfc.nasa.gov/datasets/M2T3NVCLD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ mst_ Nv: 3d,3-Hourly,Time-Averaged,Model-Level,Assimilation,Moist Processes Diagnostics V5.12.4.” doi:10.5067/ZXTJ28TQR1TR, https://disc.gsfc.nasa.gov/datasets/M2T3NVMST_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_3d_ rad_ Nv: 3d,3-Hourly,Time-Averaged,Model-Level,Assimilation,Radiation Diagnostics V5.12.4.” doi:10.5067/7GFQKO1T43RW, https://disc.gsfc.nasa.gov/datasets/M2T3NVRAD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavg3_2d_ glc_ Nx: 2d,3-Hourly,Time-Averaged,Single-Level,Assimilation,Land Ice Surface Diagnostics V5.12.4.” doi:10.5067/9ETB4TT5J6US, https://disc.gsfc.nasa.gov/datasets/M2T3NXGLC_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instM_2d_ asm_ Nx: 2d,Monthly mean,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4.” doi:10.5067/5ESKGQTZG7FO, https://disc.gsfc.nasa.gov/datasets/M2IMNXASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instM_2d_ int_ Nx: 2d,Monthly mean,Instantaneous,Single-Level,Assimilation,Vertically Integrated Diagnostics V5.12.4.” doi:10.5067/KVTU1A8BWFSJ, https://disc.gsfc.nasa.gov/datasets/M2IMNXINT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instM_2d_ lfo_ Nx: 2d,Monthly mean,Instantaneous,Single-Level,Assimilation,Land Surface Forcings V5.12.4.” doi:10.5067/11F99Y6TXN99, https://disc.gsfc.nasa.gov/datasets/M2IMNXLFO_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instM_2d_ gas_ Nx: 2d,Monthly mean,Instantaneous,Single-Level,Assimilation,Aerosol Optical Depth Analysis V5.12.4.” doi:10.5067/XOGNBQEPLUC5, https://disc.gsfc.nasa.gov/datasets/M2IMNXGAS_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instM_3d_ asm_ Np: 3d,Monthly mean,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4.” doi:10.5067/2E096JV59PK7, https://disc.gsfc.nasa.gov/datasets/M2IMNPASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instM_3d_ ana_ Np: 3d,Monthly mean,Instantaneous,Pressure-Level,Analysis,Analyzed Meteorological Fields V5.12.4.” doi:10.5067/V92O8XZ30XBI, https://disc.gsfc.nasa.gov/datasets/M2IMNPANA_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ adg_ Nx: 2d,Monthly mean,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics (extended) V5.12.4.” doi:10.5067/RZIK2TV7PP38, https://disc.gsfc.nasa.gov/datasets/M2TMNXADG_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ aer_ Nx: 2d,Monthly mean,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics V5.12.4.” doi:10.5067/FH9A0MLJPC7N, https://disc.gsfc.nasa.gov/datasets/M2TMNXAER_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ chm_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Carbon Monoxide and Ozone Diagnostics V5.12.4.” doi:10.5067/WMT31RKEXK8I, https://disc.gsfc.nasa.gov/datasets/M2TMNXCHM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ csp_ Nx: 2d,Monthly mean,Time-averaged,Single-Level,Assimilation,COSP Satellite Simulator V5.12.4.” doi:10.5067/BZPOTGJOQKLU, https://disc.gsfc.nasa.gov/datasets/M2TMNXCSP_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ flx_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4.” doi:10.5067/0JRLVL8YV2Y4, https://disc.gsfc.nasa.gov/datasets/M2TMNXFLX_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ int_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Vertically Integrated Diagnostics V5.12.4.” doi:10.5067/FQPTQ4OJ22TL, https://disc.gsfc.nasa.gov/datasets/M2TMNXINT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ lfo_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Land Surface Forcings V5.12.4.” doi:10.5067/5V7K6LJD44SY, https://disc.gsfc.nasa.gov/datasets/M2TMNXLFO_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ lnd_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4.” doi:10.5067/8S35XF81C28F, https://disc.gsfc.nasa.gov/datasets/M2TMNXLND_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ ocn_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Ocean Surface Diagnostics V5.12.4.” doi:10.5067/4IASLIDL8EEC, https://disc.gsfc.nasa.gov/datasets/M2TMNXOCN_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ rad_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Radiation Diagnostics V5.12.4.” doi:10.5067/OU3HJDS973O0, https://disc.gsfc.nasa.gov/datasets/M2TMNXRAD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ slv_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4.” doi:10.5067/AP1B0BA5PD2K, https://disc.gsfc.nasa.gov/datasets/M2TMNXSLV_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_2d_ glc_ Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Land Ice Surface Diagnostics V5.12.4.” doi:10.5067/5W8Q3I9WUFGX, https://disc.gsfc.nasa.gov/datasets/M2TMNXGLC_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_3d_ cld_ Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Cloud Diagnostics V5.12.4.” doi:10.5067/J9R0LXGH48JR, https://disc.gsfc.nasa.gov/datasets/M2TMNPCLD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_3d_ mst_ Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Moist Processes Diagnostics V5.12.4.” doi:10.5067/ZRZGD0DCK1CG, https://disc.gsfc.nasa.gov/datasets/M2TMNPMST_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_3d_ rad_ Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Radiation Diagnostics V5.12.4.” doi:10.5067/H3YGROBVBGFJ, https://disc.gsfc.nasa.gov/datasets/M2TMNPRAD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_3d_ tdt_ Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Temperature Tendencies V5.12.4.” doi:10.5067/VILT59HI2MOY, https://disc.gsfc.nasa.gov/datasets/M2TMNPTDT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_3d_ trb_ Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Turbulence Diagnostics V5.12.4.” doi:10.5067/2YOIQB5C3ACN, https://disc.gsfc.nasa.gov/datasets/M2TMNPTRB_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_3d_ udt_ Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Wind Tendencies V5.12.4.” doi:10.5067/YSR6IA5057XX, https://disc.gsfc.nasa.gov/datasets/M2TMNPUDT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_3d_ odt_ Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Ozone Tendencies V5.12.4.” doi:10.5067/Z2KCWAV4GPD2, https://disc.gsfc.nasa.gov/datasets/M2TMNPODT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgM_3d_ qdt_ Np: 3d,Monthly mean,Time-Averaged,Pressure-Level,Assimilation,Moist Tendencies V5.12.4.” doi:10.5067/2ZTU87V69ATP, https://disc.gsfc.nasa.gov/datasets/M2TMNPQDT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 const_2d_ asm_ Nx: 2d, constants.” doi:10.5067/ME5QX6Q5IGGU, https://disc.gsfc.nasa.gov/datasets/M2C0NXASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instU_2d_ asm_ Nx: 2d,Diurnal,Instantaneous,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4.” doi:10.5067/BOJSTZAO2L8R, https://disc.gsfc.nasa.gov/datasets/M2IUNXASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instU_2d_ int_ Nx: 2d,Diurnal,Instantaneous,Single-Level,Assimilation,Vertically Integrated Diagnostics V5.12.4.” doi:10.5067/DGAB3HFEYMLY, https://disc.gsfc.nasa.gov/datasets/M2IUNXINT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instU_2d_ lfo_ Nx: 2d,Diurnal,Instantaneous,Single-Level,Assimilation,Land Surface Forcings V5.12.4.” doi:10.5067/FC3BVJ88Y8A2, https://disc.gsfc.nasa.gov/datasets/M2IUNXLFO_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instU_2d_ gas_ Nx: 2d,Diurnal,Instantaneous,Single-Level,Assimilation,Aerosol Optical Depth Analysis V5.12.4.” doi:10.5067/TVJ4MHBED39L, https://disc.gsfc.nasa.gov/datasets/M2IUNXGAS_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instU_3d_ asm_ Np: 3d,Diurnal,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4.” doi:10.5067/6EGRBNEBMIYS, https://disc.gsfc.nasa.gov/datasets/M2IUNPASM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 instU_3d_ ana_ Np: 3d,Diurnal,Instantaneous,Pressure-Level,Analysis,Analyzed Meteorological Fields V5.12.4.” doi:10.5067/TRD91YO9S6E7, https://disc.gsfc.nasa.gov/datasets/M2IUNPANA_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ adg_ Nx: 2d,Diurnal,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics (extended) V5.12.4.” doi:10.5067/YZJJXZTFCX6B, https://disc.gsfc.nasa.gov/datasets/M2TUNXADG_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ aer_ Nx: 2d,Diurnal,Time-averaged,Single-Level,Assimilation,Aerosol Diagnostics V5.12.4.” doi:10.5067/KPUMVXFEQLA1, https://disc.gsfc.nasa.gov/datasets/M2TUNXAER_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ chm_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Carbon Monoxide and Ozone Diagnostics V5.12.4.” doi:10.5067/5KFZ6GXRHZKN, https://disc.gsfc.nasa.gov/datasets/M2TUNXCHM_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ csp_ Nx: 2d,Diurnal,Time-averaged,Single-Level,Assimilation,COSP Satellite Simulator V5.12.4.” doi:10.5067/9PH5QU4CL9E8, https://disc.gsfc.nasa.gov/datasets/M2TUNXCSP_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ flx_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4.” doi:10.5067/LUHPNWAKYIO3, https://disc.gsfc.nasa.gov/datasets/M2TUNXFLX_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ int_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Vertically Integrated Diagnostics V5.12.4.” doi:10.5067/R2MPVU4EOSWT, https://disc.gsfc.nasa.gov/datasets/M2TUNXINT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ lfo_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Land Surface Forcings V5.12.4.” doi:10.5067/BTSNKAJND3ME, https://disc.gsfc.nasa.gov/datasets/M2TUNXLFO_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ lnd_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4.” doi:10.5067/W0J15047CF6N, https://disc.gsfc.nasa.gov/datasets/M2TUNXLND_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ ocn_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Ocean Surface Diagnostics V5.12.4.” doi:10.5067/KLNAVGAX7J66, https://disc.gsfc.nasa.gov/datasets/M2TUNXOCN_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ rad_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Radiation Diagnostics V5.12.4.” doi:10.5067/4SDCJYK8P9QU, https://disc.gsfc.nasa.gov/datasets/M2TUNXRAD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ slv_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4.” doi:10.5067/AFOK0TPEVQEK, https://disc.gsfc.nasa.gov/datasets/M2TUNXSLV_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_2d_ glc_ Nx: 2d,Diurnal,Time-Averaged,Single-Level,Assimilation,Land Ice Surface Diagnostics V5.12.4.” doi:10.5067/7VUPQC736SWX, https://disc.gsfc.nasa.gov/datasets/M2TUNXGLC_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_3d_ cld_ Np: 3d,Diurnal,Time-Averaged,Pressure-Level,Assimilation,Cloud Diagnostics V5.12.4.” doi:10.5067/EPW7T5UO0C0N, https://disc.gsfc.nasa.gov/datasets/M2TUNPCLD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_3d_ mst_ Np: 3d,Diurnal,Time-Averaged,Pressure-Level,Assimilation,Moist Processes Diagnostics V5.12.4.” doi:10.5067/ZRSN0JU27DK2, https://disc.gsfc.nasa.gov/datasets/M2TUNPMST_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_3d_ rad_ Np: 3d,Diurnal,Time-Averaged,Pressure-Level,Assimilation,Radiation Diagnostics V5.12.4.” doi:10.5067/H140JMDOWB0Y, https://disc.gsfc.nasa.gov/datasets/M2TUNPRAD_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_3d_ tdt_ Np: 3d,Diurnal,Time-Averaged,Pressure-Level,Assimilation,Temperature Tendencies V5.12.4.” doi:10.5067/QPO9E5TPZ8OF, https://disc.gsfc.nasa.gov/datasets/M2TUNPTDT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_3d_ trb_ Np: 3d,Diurnal,Time-Averaged,Pressure-Level,Assimilation,Turbulence Diagnostics V5.12.4.” doi:10.5067/2A99C60CG7WC, https://disc.gsfc.nasa.gov/datasets/M2TUNPTRB_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_3d_ udt_ Np: 3d,Diurnal,Time-Averaged,Pressure-Level,Assimilation,Wind Tendencies V5.12.4.” doi:10.5067/DO715T7T5PG8, https://disc.gsfc.nasa.gov/datasets/M2TUNPUDT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_3d_ odt_ Np: 3d,Diurnal,Time-Averaged,Pressure-Level,Assimilation,Ozone Tendencies V5.12.4.” doi:10.5067/M8OJ09GZP23E, https://disc.gsfc.nasa.gov/datasets/M2TUNPODT_5.12.4/summary.
Global Modeling And Assimilation Office, Pawson S (2015). “MERRA-2 tavgU_3d_ qdt_ Np: 3d,Diurnal,Time-Averaged,Pressure-Level,Assimilation,Moist Tendencies V5.12.4.” doi:10.5067/S8HJXIR0BFTS, https://disc.gsfc.nasa.gov/datasets/M2TUNPQDT_5.12.4/summary.
## Not run: download_merra2( collection = "inst1_2d_int_Nx", date = "2024-01-01", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, ) ## End(Not run)## Not run: download_merra2( collection = "inst1_2d_int_Nx", date = "2024-01-01", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, ) ## End(Not run)
Need maintenance for the directory path change in NASA EOSDIS. This function first retrieves the all hdf download links on a certain day, then only selects the relevant tiles from the retrieved links. Download is only done at the queried horizontal-vertical tile number combinations. An exception is MOD06_L2 product, which is produced every five minutes every day.
download_modis( product = c("MOD09GA", "MOD11A1", "MOD06_L2", "MCD19A2", "MOD13A2", "VNP46A2"), version = "61", horizontal_tiles = c(7, 13), vertical_tiles = c(3, 6), mod06_links = NULL, nasa_earth_data_token = NULL, date = c("2023-09-01", "2023-09-01"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )download_modis( product = c("MOD09GA", "MOD11A1", "MOD06_L2", "MCD19A2", "MOD13A2", "VNP46A2"), version = "61", horizontal_tiles = c(7, 13), vertical_tiles = c(3, 6), mod06_links = NULL, nasa_earth_data_token = NULL, date = c("2023-09-01", "2023-09-01"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )
product |
character(1).
One of |
version |
character(1). Default is |
horizontal_tiles |
integer(2). Horizontal tile numbers
|
vertical_tiles |
integer(2). Vertical tile numbers
|
mod06_links |
character(1). CSV file path to MOD06_L2 download links
from NASA LAADS MOD06_L2. Default is |
nasa_earth_data_token |
character(1). Token for downloading data from NASA. Should be set before trying running the function. |
date |
character(1 or 2). length of 10. Date or start/end dates for downloading data.
Format "YYYY-MM-DD" (ex. January 1, 2018 = |
directory_to_save |
character(1). Directory to save data. |
acknowledgement |
logical(1). By setting |
download |
logical(1). Download data or only save wget commands. |
remove_command |
logical(1). Remove ( |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
HDF (.hdf) files will be stored in year/day_of_year sub-directories within
directory_to_save.
Both dates in date should be in the same year.
Directory structure looks like
input/modis/raw/{version}/{product}/{year}/{day_of_year}.
Mitchell Manware, Insang Song
Lyapustin A, Wang Y (2022). “MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid V061.” doi:10.5067/MODIS/MCD19A2.061, https://lpdaac.usgs.gov/products/mcd19a2v061/.
MODIS Atmosphere Science Team (2017). “MODIS/Terra Clouds 5-Min L2 Swath 1km and 5km.” doi:10.5067/MODIS/MOD06_L2.061, https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06_L2.
Vermote E, Wolfe R (2021). “MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061.” doi:10.5067/MODIS/MOD09GA.061, https://lpdaac.usgs.gov/products/mod09gav061/.
Wan Z, Hook S, Hulley G (2021). “MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061.” doi:10.5067/MODIS/MOD11A1.061, https://lpdaac.usgs.gov/products/mod11a1v061/.
Didan K (2021). “MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061.” doi:10.5067/MODIS/MOD13A2.061, https://lpdaac.usgs.gov/products/mod13a2v061/.
Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC, Hall D, Oda T, Wolfe RE, Lin G, Golpayegani N, Devadiga S, Davidson C, Sarkar S, Praderas C, Schmaltz J, Boller R, Stevens J, Ramos González OM, Padilla E, Alonso J, Detrés Y, Armstrong R, Miranda I, Conte Y, Marrero N, MacManus K, Esch T, Masuoka EJ (2018). “NASA's Black Marble nighttime lights product suite.” Remote Sensing of Environment, 210, 113–143. ISSN 00344257, doi:10.1016/j.rse.2018.03.017, https://linkinghub.elsevier.com/retrieve/pii/S003442571830110X.
## Not run: ## NOTE: Examples are wrapped in `/dontrun{}` to avoid sharing sensitive ## NASA EarthData tokden information. # example with MOD09GA product download_modis( product = "MOD09GA", version = "61", horizontal_tiles = c(8, 8), vertical_tiles = c(4, 4), date = "2024-01-01", nasa_earth_data_token = "./pathtotoken/token.txt", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) # example with MOD06_L2 product download_modis( product = "MOD06_L2", version = "61", horizontal_tiles = c(8, 8), vertical_tiles = c(4, 4), date = "2024-01-01", mod06_links = system.file( "extdata", "nasa", "LAADS_query.2024-08-02T12_49.csv", package = "amadeus" ), nasa_earth_data_token = "./pathtotoken/token.txt", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) # example with VNP46A2 product download_modis( product = "VNP46A2", version = "61", horizontal_tiles = c(8, 8), vertical_tiles = c(4, 4), date = "2024-01-01", nasa_earth_data_token = "./pathtotoken/token.txt", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)## Not run: ## NOTE: Examples are wrapped in `/dontrun{}` to avoid sharing sensitive ## NASA EarthData tokden information. # example with MOD09GA product download_modis( product = "MOD09GA", version = "61", horizontal_tiles = c(8, 8), vertical_tiles = c(4, 4), date = "2024-01-01", nasa_earth_data_token = "./pathtotoken/token.txt", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) # example with MOD06_L2 product download_modis( product = "MOD06_L2", version = "61", horizontal_tiles = c(8, 8), vertical_tiles = c(4, 4), date = "2024-01-01", mod06_links = system.file( "extdata", "nasa", "LAADS_query.2024-08-02T12_49.csv", package = "amadeus" ), nasa_earth_data_token = "./pathtotoken/token.txt", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) # example with VNP46A2 product download_modis( product = "VNP46A2", version = "61", horizontal_tiles = c(8, 8), vertical_tiles = c(4, 4), date = "2024-01-01", nasa_earth_data_token = "./pathtotoken/token.txt", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)
The download_narr function accesses and downloads daily meteorological data from NOAA's North American Regional Reanalysis (NARR) model.
download_narr( variables = NULL, year = c(2018, 2022), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )download_narr( variables = NULL, year = c(2018, 2022), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )
variables |
character. Variable(s) name acronym. See List of Variables in NARR Files for variable names and acronym codes. |
year |
character(1 or 2). length of 4. Year or start/end years for downloading data. |
directory_to_save |
character(1). Directory(s) to save downloaded data files. |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
netCDF (.nc) files will be stored in
directory_to_save.
"Pressure levels" variables contain variable values at 29 atmospheric levels, ranging from 1000 hPa to 100 hPa. All pressure levels data will be downloaded for each variable.
Mitchell Manware, Insang Song
Mesinger F, DiMego G, Kalnay E, Mitchell K, Shafran PC, Ebisuzaki W, Jović D, Woollen J, Rogers E, Berbery EH, Ek MB, Fan Y, Grumbine R, Higgins W, Li H, Lin Y, Manikin G, Parrish D, Shi W (2006). “North American Regional Reanalysis.” Bulletin of the American Meteorological Society, 87(3), 343–360. ISSN 0003-0007, 1520-0477, doi:10.1175/BAMS-87-3-343.
## Not run: download_narr( variables = c("weasd", "omega"), year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)## Not run: download_narr( variables = c("weasd", "omega"), year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)
The download_nei() function accesses and downloads road emissions data from the U.S Environmental Protection Agency's (EPA) National Emissions Inventory (NEI).
download_nei( epa_certificate_path = system.file("extdata/cacert_gaftp_epa.pem", package = "amadeus"), certificate_url = "http://cacerts.digicert.com/DigiCertGlobalG2TLSRSASHA2562020CA1-1.crt", year = c(2017L, 2020L), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, hash = FALSE )download_nei( epa_certificate_path = system.file("extdata/cacert_gaftp_epa.pem", package = "amadeus"), certificate_url = "http://cacerts.digicert.com/DigiCertGlobalG2TLSRSASHA2562020CA1-1.crt", year = c(2017L, 2020L), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, hash = FALSE )
epa_certificate_path |
character(1). Path to the certificate file for EPA DataCommons. Default is 'extdata/cacert_gaftp_epa.pem' under the package installation path. |
certificate_url |
character(1). URL to certificate file. See notes for details. |
year |
Available years of NEI data.
Default is |
directory_to_save |
character(1). Directory to save data. Two sub-directories will be created for the downloaded zip files ("/zip_files") and the unzipped data files ("/data_files"). |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip the downloaded zip files.
Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
respective sub-directories within directory_to_save.
For EPA Data Commons certificate errors, follow the steps below:
Click Lock icon in the address bar at https://gaftp.epa.gov
Click Show Certificate
Access Details
Find URL with *.crt extension Currently we bundle the pre-downloaded crt and its PEM (which is accepted in wget command) file in ./inst/extdata. The instruction above is for certificate updates in the future.
Ranadeep Daw, Insang Song
United States Environmental Protection Agency (2024). “Air Emissions Inventories.” https://www.epa.gov/air-emissions-inventories.
## Not run: download_nei( year = c(2017L, 2020L), directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_nei( year = c(2017L, 2020L), directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
The download_nlcd() function accesses and downloads
land cover data from the
Multi-Resolution Land Characteristics (MRLC) Consortium's National Land Cover Database (NLCD) products data base.
download_nlcd( collection = "Coterminous United States", year = 2021, directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )download_nlcd( collection = "Coterminous United States", year = 2021, directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )
collection |
character(1). |
year |
integer(1). Available years for Coterminous United States
include |
directory_to_save |
character(1). Directory to save data. Two sub-directories will be created for the downloaded zip files ("/zip_files") and the unzipped shapefiles ("/data_files"). |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip zip files. Default is |
remove_zip |
logical(1). Remove zip files from directory_to_download.
Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
respective sub-directories within directory_to_save.
Mitchell Manware, Insang Song
Dewitz J (2023).
“National Land Cover Database (NLCD) 2021 Products.”
doi:10.5066/P9JZ7AO3.
Dewitz J (2024).
“National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024).”
doi:10.5066/P9KZCM54.
## Not run: download_nlcd( collection = "Coterminous United States", year = 2021, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_nlcd( collection = "Coterminous United States", year = 2021, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
Accesses and downloads Oregon State University's PRISM data from the PRISM Climate Group Web Service
download_prism( time, element = c("ppt", "tmin", "tmax", "tmean", "tdmean", "vpdmin", "vpdmax", "solslope", "soltotal", "solclear", "soltrans"), data_type = c("ts", "normals_800", "normals"), format = c("nc", "asc", "grib2"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )download_prism( time, element = c("ppt", "tmin", "tmax", "tmean", "tdmean", "vpdmin", "vpdmax", "solslope", "soltotal", "solclear", "soltrans"), data_type = c("ts", "normals_800", "normals"), format = c("nc", "asc", "grib2"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )
time |
character(1). Length of 2, 4, 6, or 8. Time period for time series or normals. According to the PRISM Web Service Guide, acceptable formats include (disclaimer: the following is a direct quote; minimal formatting is applied): Time Series:
Normals:
|
element |
character(1). Data element.
One of |
data_type |
character(1). Data type.
|
format |
character(1). Data format. Only applicable for |
directory_to_save |
character(1). Directory to download files. |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
.bil (normals) or single grid files depending on the format
choice will be stored in directory_to_save.
Insang Song
Daly C, Taylor GH, Gibson WP, Parzybok TW, Johnson GL, Pasteris PA (2000). “HIGH-QUALITY SPATIAL CLIMATE DATA SETS FOR THE UNITED STATES AND BEYOND.” Transactions of the ASAE, 43(6), 1957–1962. ISSN 2151-0059, doi:10.13031/2013.3101, http://elibrary.asabe.org/abstract.asp??JID=3&AID=3101&CID=t2000&v=43&i=6&T=1.
## Not run: download_prism( time = "202104", element = "ppt", data_type = "ts", format = "nc", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)## Not run: download_prism( time = "202104", element = "ppt", data_type = "ts", format = "nc", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)
The download_sedac_groads() function accesses and downloads
roads data from NASA's Global Roads Open Access Data Set (gROADS), v1 (1980-2010).
download_sedac_groads( data_region = c("Americas", "Global", "Africa", "Asia", "Europe", "Oceania East", "Oceania West"), data_format = c("Shapefile", "Geodatabase"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )download_sedac_groads( data_region = c("Americas", "Global", "Africa", "Asia", "Europe", "Oceania East", "Oceania West"), data_format = c("Shapefile", "Geodatabase"), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )
data_region |
character(1). Data can be downloaded for |
data_format |
character(1). Data can be downloaded as |
directory_to_save |
character(1). Directory to save data. Two sub-directories will be created for the downloaded zip files ("/zip_files") and the unzipped shapefiles ("/data_files"). |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip zip files. Default is |
remove_zip |
logical(1). Remove zip files from directory_to_download.
Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
respective sub-directories within directory_to_save.
Mitchell Manware, Insang Song
Center For International Earth Science Information Network-CIESIN-Columbia University, Information Technology Outreach Services-ITOS-University Of Georgia (2013). “Global Roads Open Access Data Set, Version 1 (gROADSv1).” doi:10.7927/H4VD6WCT, https://earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-groads-v1-1.00.
## Not run: download_sedac_groads( data_region = "Americas", data_format = "Shapefile", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_sedac_groads( data_region = "Americas", data_format = "Shapefile", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
The download_sedac_population() function accesses and downloads
population density data from NASA's UN WPP-Adjusted Population Density, v4.11.
download_sedac_population( data_resolution = "60 minute", data_format = c("GeoTIFF", "ASCII", "netCDF"), year = "2020", directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )download_sedac_population( data_resolution = "60 minute", data_format = c("GeoTIFF", "ASCII", "netCDF"), year = "2020", directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, unzip = TRUE, remove_zip = FALSE, hash = FALSE )
data_resolution |
character(1). Available resolutions are 30 second (approx. 1 km), 2.5 minute (approx. 5 km), 15 minute (approx. 30 km), 30 minute (approx. 55 km), and 60 minute (approx. 110 km). |
data_format |
character(1). Individual year data can be downloaded as
|
year |
character(1). Available years are |
directory_to_save |
character(1). Directory to save data. Two sub-directories will be created for the downloaded zip files ("/zip_files") and the unzipped shapefiles ("/data_files"). |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
unzip |
logical(1). Unzip zip files. Default is |
remove_zip |
logical(1). Remove zip files from directory_to_download.
Default is |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Zip and/or data files will be downloaded and stored in
respective sub-directories within directory_to_save.
Mitchell Manware, Insang Song
Center For International Earth Science Information Network-CIESIN-Columbia University (2017). “Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11.” doi:10.7927/H49C6VHW, https://earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-gpwv4-popdens-r11-4.11.
## Not run: download_sedac_population( data_resolution = "30 second", data_format = "GeoTIFF", year = "2020", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)## Not run: download_sedac_population( data_resolution = "30 second", data_format = "GeoTIFF", year = "2020", directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE, unzip = FALSE ) ## End(Not run)
The download_terraclimate function accesses and downloads climate and water balance data from the University of California Merced Climatology Lab's TerraClimate dataset.
download_terraclimate( variables = NULL, year = c(2018, 2022), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )download_terraclimate( variables = NULL, year = c(2018, 2022), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )
variables |
character(1). Variable(s) name(s). See TerraClimate Direct Downloads for variable names and acronym codes. |
year |
character(1 or 2). length of 4. Year or start/end years for downloading data. |
directory_to_save |
character(1). Directory(s) to save downloaded data files. |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1).
Remove ( |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
netCDF (.nc) files will be stored in a variable-specific
folder within directory_to_save.
Mitchell Manware, Insang Song
Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018). “TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015.” Scientific data, 5(1), 1–12.
## Not run: download_terraclimate( variables = "Precipitation", year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)## Not run: download_terraclimate( variables = "Precipitation", year = 2023, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)
The download_tri() function accesses and downloads toxic release data from the U.S. Environmental Protection Agency's (EPA) Toxic Release Inventory (TRI) Program.
download_tri( year = c(2018L, 2022L), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )download_tri( year = c(2018L, 2022L), directory_to_save = NULL, acknowledgement = FALSE, download = FALSE, remove_command = FALSE, hash = FALSE )
year |
character(1 or 2). length of 4. Year or start/end years for downloading data. |
directory_to_save |
character(1). Directory to download files. |
acknowledgement |
logical(1). By setting |
download |
logical(1). |
remove_command |
logical(1). Remove ( |
hash |
logical(1). By setting |
For hash = FALSE, NULL
For hash = TRUE, an rlang::hash_file character.
Comma-separated value (CSV) files will be stored in
directory_to_save.
Mariana Kassien, Insang Song
United States Environmental Protection Agency (2024). “TRI Basic Data Files: Calendar Years 1987 – Present.” https://www.epa.gov/toxics-release-inventory-tri-program/tri-data-action-0.
## Not run: download_tri( year = 2021L, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)## Not run: download_tri( year = 2021L, directory_to_save = tempdir(), acknowledgement = TRUE, download = FALSE, # NOTE: download skipped for examples, remove_command = TRUE ) ## End(Not run)
data.table to an sftime
Convert a data.table object to an sftime. x must be
a data.table object with "lon", "lat", and "time" columns to
describe the longitude, latitude, and time-orientation, respectively, of
x.
dt_as_mysftime(x, lonname, latname, timename, crs)dt_as_mysftime(x, lonname, latname, timename, crs)
x |
a |
lonname |
character for longitude column name |
latname |
character for latitude column name |
timename |
character for time column name |
crs |
coordinate reference system |
an sftime object
Eva Marques
The process_aqs() function cleans and imports raw air quality
monitoring sites from pre-generated daily CSV files, returning a single
SpatVector or sf object.
date is used to filter the raw data read from csv files.
Filtered rows are then processed according to mode argument.
Some sites report multiple measurements per day with and without
exceptional events
the internal procedure of this function keeps "Included" if there
are multiple event types per site-time.
process_aqs( path = NULL, date = c("2018-01-01", "2022-12-31"), mode = c("date-location", "available-data", "location"), data_field = "Arithmetic.Mean", return_format = c("terra", "sf", "data.table"), extent = NULL, ... )process_aqs( path = NULL, date = c("2018-01-01", "2022-12-31"), mode = c("date-location", "available-data", "location"), data_field = "Arithmetic.Mean", return_format = c("terra", "sf", "data.table"), extent = NULL, ... )
path |
character(1). Directory path to daily measurement data. |
date |
character(1 or 2). Date (1) or start and end dates (2).
Should be in |
mode |
character(1). One of
|
data_field |
character(1). Data field to extract. |
return_format |
character(1). |
extent |
numeric(4). Spatial extent of the resulting object.
The order should be |
... |
Placeholders. |
a SpatVector, sf, or data.table object depending on the return_format
Choose date and mode values with caution.
The function may return a massive data.table depending on the time range,
resulting in a long processing time or even a crash if data is too large
for your computing environment to process.
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: aqs <- process_aqs( path = "./data/aqs_daily_example.csv", date = c("2022-12-01", "2023-01-31"), mode = "full", return_format = "terra" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: aqs <- process_aqs( path = "./data/aqs_daily_example.csv", date = c("2022-12-01", "2023-01-31"), mode = "full", return_format = "terra" ) ## End(Not run)
This function will return a SpatRaster object with
georeferenced h5 files of Black Marble product. Referencing corner coordinates
are necessary as the original h5 data do not include such information.
process_blackmarble( path = NULL, date = NULL, tile_df = process_blackmarble_corners(), subdataset = 3L, crs = "EPSG:4326", ... )process_blackmarble( path = NULL, date = NULL, tile_df = process_blackmarble_corners(), subdataset = 3L, crs = "EPSG:4326", ... )
path |
character. Full paths of h5 files. |
date |
character(1). Date to query. |
tile_df |
data.frame. Contains four corner coordinates in fields named
|
subdataset |
integer(1). Subdataset number to process. Default is 3L. |
crs |
character(1). terra::crs compatible CRS.
Default is |
... |
For internal use. |
a SpatRaster object
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: vnp46a2 <- process_blackmarble( path = list.files("./data", pattern = "VNP46A2.", full.names = TRUE), date = "2024-01-01", tile_df = process_blackmarble_corners(hrange = c(8, 10), vrange = c(4, 5)), subdataset = 3L, crs = "EPSG:4326" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: vnp46a2 <- process_blackmarble( path = list.files("./data", pattern = "VNP46A2.", full.names = TRUE), date = "2024-01-01", tile_df = process_blackmarble_corners(hrange = c(8, 10), vrange = c(4, 5)), subdataset = 3L, crs = "EPSG:4326" ) ## End(Not run)
Tile corner generator for Black Marble products.
Black Marble products are in HDF5 format and are read without
georeference with typical R geospatial packages.
This function generates a data.frame of corner coordinates for assignment.
process_blackmarble_corners(hrange = c(5, 11), vrange = c(3, 6))process_blackmarble_corners(hrange = c(5, 11), vrange = c(3, 6))
hrange |
integer(2). Both should be in 0-35. |
vrange |
integer(2). Both should be in 0-17. |
data.frame with xmin, xmax, ymin, and ymax fields
Insang Song
process_blackmarble_corners(hrange = c(1, 2), vrange = c(1, 2))process_blackmarble_corners(hrange = c(1, 2), vrange = c(1, 2))
This function processes raw data files which have
been downloaded by download_data. process_covariates and
the underlying source-specific processing functions have been designed to
operate on the raw data files. To avoid errors, do not edit the raw
data files before passing to process_covariates.
process_covariates( covariate = c("modis_swath", "modis_merge", "koppen-geiger", "blackmarble", "koeppen-geiger", "koppen", "koeppen", "geos", "dummies", "gmted", "hms", "smoke", "sedac_population", "population", "sedac_groads", "groads", "roads", "nlcd", "tri", "narr", "nei", "ecoregions", "ecoregion", "merra", "merra2", "gridmet", "terraclimate", "huc", "cropscape", "cdl", "prism"), path = NULL, ... )process_covariates( covariate = c("modis_swath", "modis_merge", "koppen-geiger", "blackmarble", "koeppen-geiger", "koppen", "koeppen", "geos", "dummies", "gmted", "hms", "smoke", "sedac_population", "population", "sedac_groads", "groads", "roads", "nlcd", "tri", "narr", "nei", "ecoregions", "ecoregion", "merra", "merra2", "gridmet", "terraclimate", "huc", "cropscape", "cdl", "prism"), path = NULL, ... )
covariate |
character(1). Covariate type. |
path |
character(1). Directory or file path to raw data
depending on |
... |
Arguments passed to each raw data processing function. |
SpatVector, SpatRaster, sf, or character depending on
covariate type and selections.
Insang Song
process_modis_swath: "modis_swath"
process_modis_merge: "modis_merge"
process_blackmarble: "blackmarble"
process_koppen_geiger: "koppen-geiger", "koeppen-geiger", "koppen"
process_ecoregion: "ecoregion", "ecoregions"
process_nlcd: "nlcd", "NLCD"
process_tri: "tri", "TRI"
process_nei: "nei", "NEI"
process_geos: "geos", "GEOS"
process_gmted: "gmted", "GMTED"
process_aqs: "aqs", "AQS"
process_hms: "hms", "smoke", "HMS"
process_narr: "narr", "NARR"
process_sedac_groads: "sedac_groads", "roads", "groads"
process_sedac_population: "sedac_population", "population"
process_merra2: "merra", "merra2", "MERRA2"
process_gridmet: "gridmet", "gridMET"
process_terraclimate: "terraclimate", "TerraClimate"
process_huc: "huc", "HUC"
process_cropscape: "cropscape", "cdl"
process_prism: "prism", "PRISM"
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: process_covariates( covariate = "narr", date = c("2018-01-01", "2018-01-10"), variable = "weasd", path = system.file("extdata", "examples", "narr", "weasd") ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: process_covariates( covariate = "narr", date = c("2018-01-01", "2018-01-10"), variable = "weasd", path = system.file("extdata", "examples", "narr", "weasd") ) ## End(Not run)
This function imports and cleans raw CropScape data,
returning a single SpatRaster object.
Reads CropScape file of selected year.
process_cropscape(path = NULL, year = 2021, extent = NULL, ...)process_cropscape(path = NULL, year = 2021, extent = NULL, ...)
path |
character giving CropScape data path |
year |
numeric giving the year of CropScape data used |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: cropscape <- process_cropscape( path = "./data/cropscape_example.tif", year = 2020 ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: cropscape <- process_cropscape( path = "./data/cropscape_example.tif", year = 2020 ) ## End(Not run)
The process_ecoregion function imports and cleans raw ecoregion
data, returning a SpatVector object.
process_ecoregion(path = NULL, extent = NULL, ...)process_ecoregion(path = NULL, extent = NULL, ...)
path |
character(1). Path to Ecoregion Shapefiles |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatVector object
The function will fix Tukey's bridge in Portland, ME. This fix will ensure that the EPA air quality monitoring sites will be located within the ecoregion.
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: ecoregion <- process_ecoregion( path = "./data/epa_ecoregion.gpkg" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: ecoregion <- process_ecoregion( path = "./data/epa_ecoregion.gpkg" ) ## End(Not run)
Aggregate layers in a sub-dataset in sinusoidal MODIS products.
Some MODIS products consist of multi-layer subdatasets.
This function aggregates multiple layers into single layer SpatRaster.
fun_agg is applied at overlapping cells.
process_flatten_sds(path = NULL, subdataset = NULL, fun_agg = "mean", ...)process_flatten_sds(path = NULL, subdataset = NULL, fun_agg = "mean", ...)
path |
character(1). Full path to MODIS HDF4/HDF5 file. Direct sub-dataset access is supported, for example, HDF4_EOS:EOS_GRID:{filename}:{base_grid_information}:{sub-dataset} |
subdataset |
character(1). Exact or regular expression filter of sub-dataset. See process_modis_sds for details. |
fun_agg |
character(1). Function name to aggregate layers. Should be acceptable to terra::tapp. |
... |
Placeholders. |
a SpatRaster object
HDF values are read as original without scaling.
Users should consult MODIS product documentation to apply proper
scaling factor for post-hoc adjustment. If users have no preliminary
information about MODIS sub-datasets, consider running
terra::describe(__filename__, sds = TRUE) to navigate the full
list of sub-datasets in the input file then consult the documentation
of MODIS product.
Insang Song
terra::tapp, terra::rast, terra::describe
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: mod09ga_flatten <- process_flatten_sds( path = list.files("./data", pattern = "MOD09GA.", full.names = TRUE)[1], subdataset = process_modis_sds("MOD09GA"), fun_agg = "mean" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: mod09ga_flatten <- process_flatten_sds( path = list.files("./data", pattern = "MOD09GA.", full.names = TRUE)[1], subdataset = process_modis_sds("MOD09GA"), fun_agg = "mean" ) ## End(Not run)
The process_geos() function imports and cleans raw atmospheric
composition data, returning a single SpatRaster object.
process_geos( date = c("2018-01-01", "2018-01-10"), variable = NULL, path = NULL, extent = NULL, ... )process_geos( date = c("2018-01-01", "2018-01-10"), variable = NULL, path = NULL, extent = NULL, ... )
date |
character(1 or 2). Date (1) or start and end dates (2). Format YYYY-MM-DD (ex. September 1, 2023 = "2023-09-01"). |
variable |
character(1). GEOS-CF variable name(s). |
path |
character(1). Directory with downloaded netCDF (.nc4) files. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object;
Layer names of the returned SpatRaster object contain the variable,
pressure level, date, and hour.
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: geos <- process_geos( date = c("2024-01-01", "2024-01-10"), variable = "O3", path = "./data/aqc_tavg_1hr_g1440x721_v1" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: geos <- process_geos( date = c("2024-01-01", "2024-01-10"), variable = "O3", path = "./data/aqc_tavg_1hr_g1440x721_v1" ) ## End(Not run)
The process_gmted() function imports and cleans raw elevation data,
returning a single SpatRaster object.
process_gmted(variable = NULL, path = NULL, extent = NULL, ...)process_gmted(variable = NULL, path = NULL, extent = NULL, ...)
variable |
vector(1). Vector containing the GMTED statistic first and the resolution second. (Example: variable = c("Breakline Emphasis", "7.5 arc-seconds")).
|
path |
character(1). Directory with downloaded GMTED "*_grd" folder containing .adf files. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object
SpatRaster layer name indicates selected variable and resolution, and year
of release (2010).
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: gmted <- process_gmted( variable = c("Breakline Emphasis", "7.5 arc-seconds"), path = "./data/be75_grd" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: gmted <- process_gmted( variable = c("Breakline Emphasis", "7.5 arc-seconds"), path = "./data/be75_grd" ) ## End(Not run)
The process_gridmet() function imports and cleans raw gridded surface meteorological
data, returning a single SpatRaster object.
process_gridmet( date = c("2023-09-01", "2023-09-10"), variable = NULL, path = NULL, extent = NULL, ... )process_gridmet( date = c("2023-09-01", "2023-09-10"), variable = NULL, path = NULL, extent = NULL, ... )
date |
character(1 or 2). Date (1) or start and end dates (2). Format YYYY-MM-DD (ex. September 1, 2023 = "2023-09-01"). |
variable |
character(1). Variable name or acronym code. See gridMET Generate Wget File for variable names and acronym codes. (Note: variable "Burning Index" has code "bi" and variable "Energy Release Component" has code "erc"). |
path |
character(1). Directory with downloaded netCDF (.nc) files. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object
Layer names of the returned SpatRaster object contain the variable acronym,
and date.
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: gridmet <- process_gridmet( date = c("2023-01-01", "2023-01-10"), variable = "Precipitation", path = "./data/pr" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: gridmet <- process_gridmet( date = c("2023-01-01", "2023-01-10"), variable = "Precipitation", path = "./data/pr" ) ## End(Not run)
The process_hms() function imports and cleans raw wildfire smoke
plume coverage data, returning a single SpatVector object.
process_hms(date = "2018-01-01", path = NULL, extent = NULL, ...)process_hms(date = "2018-01-01", path = NULL, extent = NULL, ...)
date |
character(1 or 2). Date (1) or start and end dates (2). Format YYYY-MM-DD (ex. September 1, 2023 = "2023-09-01"). |
path |
character(1). Directory with downloaded NOAA HMS data files. |
extent |
numeric(4) or SpatExtent giving the extent of the output
if |
... |
Placeholders. |
a SpatVector or character object
process_hms() will return a character object if there are no wildfire
smoke plumes present for the selected dates and density. The returned
character will contain the density value and the sequence of dates
for which no wildfire smoke plumes were detected (see "Examples").
If multiple density polygons overlap, the function will return the
highest density value.
Mitchell Manware
hms <- process_hms( date = c("2018-12-30", "2019-01-01"), path = "../tests/testdata/hms/" )hms <- process_hms( date = c("2018-12-30", "2019-01-01"), path = "../tests/testdata/hms/" )
Retrieve Hydrologic Unit Code (HUC) data
process_huc( path, layer_name = NULL, huc_level = NULL, huc_header = NULL, extent = NULL, ... )process_huc( path, layer_name = NULL, huc_level = NULL, huc_header = NULL, extent = NULL, ... )
path |
character. Path to the file or the directory containing HUC data. |
layer_name |
character(1). Layer name in the |
huc_level |
character(1). Field name of HUC level |
huc_header |
character(1). The upper level HUC code header to extract lower level HUCs. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Arguments passed to |
a SpatVector object
Insang Song
## NOTE: Examples are wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: library(terra) getf <- "WBD_National_GDB.gdb" # check the layer name to read terra::vector_layers(getf) test1 <- process_huc( getf, layer_name = "WBDHU8", huc_level = "huc8" ) test2 <- process_huc( getf, layer_name = "WBDHU8", huc_level = "huc8" ) test3 <- process_huc( "", layer_name = NULL, huc_level = NULL, huc_header = NULL, id = "030202", type = "huc06" ) ## End(Not run)## NOTE: Examples are wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: library(terra) getf <- "WBD_National_GDB.gdb" # check the layer name to read terra::vector_layers(getf) test1 <- process_huc( getf, layer_name = "WBDHU8", huc_level = "huc8" ) test2 <- process_huc( getf, layer_name = "WBDHU8", huc_level = "huc8" ) test3 <- process_huc( "", layer_name = NULL, huc_level = NULL, huc_header = NULL, id = "030202", type = "huc06" ) ## End(Not run)
The process_koppen_geiger() function imports and cleans raw climate
classification data, returning a single SpatRaster object.
process_koppen_geiger(path = NULL, extent = NULL, ...)process_koppen_geiger(path = NULL, extent = NULL, ...)
path |
character(1). Path to Koppen-Geiger climate zone raster file |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: kg <- process_koppen_geiger( path = "./data/koppen_geiger_data.tif" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: kg <- process_koppen_geiger( path = "./data/koppen_geiger_data.tif" ) ## End(Not run)
The process_merra2() function imports and cleans raw atmospheric
composition data, returning a single SpatRaster object.
process_merra2( date = c("2018-01-01", "2018-01-10"), variable = NULL, path = NULL, extent = NULL, ... )process_merra2( date = c("2018-01-01", "2018-01-10"), variable = NULL, path = NULL, extent = NULL, ... )
date |
character(1 or 2). Date (1) or start and end dates (2). Format YYYY-MM-DD (ex. September 1, 2023 = "2023-09-01"). |
variable |
character(1). MERRA2 variable name(s). |
path |
character(1). Directory with downloaded netCDF (.nc4) files. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object;
Layer names of the returned SpatRaster object contain the variable,
pressure level, date, and hour. Pressure level values utilized for layer
names are taken directly from raw data and are not edited to retain
pressure level information.
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: merra2 <- process_merra2( date = c("2024-01-01", "2024-01-10"), variable = "CPT", path = "./data/inst1_2d_int_Nx" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: merra2 <- process_merra2( date = c("2024-01-01", "2024-01-10"), variable = "CPT", path = "./data/inst1_2d_int_Nx" ) ## End(Not run)
Get mosaicked or merged raster from multiple MODIS hdf files.
process_modis_merge( path = NULL, date = NULL, subdataset = NULL, fun_agg = "mean", ... )process_modis_merge( path = NULL, date = NULL, subdataset = NULL, fun_agg = "mean", ... )
path |
character. Full list of hdf file paths.
preferably a recursive search result from |
date |
character(1). date to query. Should be in
|
subdataset |
character(1). subdataset names to extract.
Should conform to regular expression. See |
fun_agg |
Function name or custom function to aggregate overlapping
cell values. See |
... |
For internal use. |
a SpatRaster object
Curvilinear products (i.e., swaths) will not be accepted.
MODIS products downloaded by functions in amadeus,
MODISTools,
and luna are accepted.
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: mod09ga_merge <- process_modis_merge( path = list.files("./data", pattern = "MOD09GA.", full.names = TRUE), date = "2024-01-01", subdataset = "sur_refl_b01_1", fun_agg = "mean" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: mod09ga_merge <- process_modis_merge( path = list.files("./data", pattern = "MOD09GA.", full.names = TRUE), date = "2024-01-01", subdataset = "sur_refl_b01_1", fun_agg = "mean" ) ## End(Not run)
Selected MODIS sinusoidal grid product subdataset name selector.
Four presets are supported. custom_sel supersedes
presets of product values.
process_modis_sds( product = c("MOD11A1", "MOD13A2", "MOD09GA", "MCD19A2"), custom_sel = NULL, ... )process_modis_sds( product = c("MOD11A1", "MOD13A2", "MOD09GA", "MCD19A2"), custom_sel = NULL, ... )
product |
character(1). Product code. |
custom_sel |
character(1). Custom filter. If this value is not NULL, preset filter is overridden. |
... |
Placeholders. |
A character object that conforms to the regular expression. Details of regular expression in R can be found in regexp.
Preset product codes and associated variables include
"MOD11A1" - Land surface temperature (LST)
"MOD13A2" - Normalized Difference Vegetation Index (NDVI)
"MOD09GA" - Surface reflectance, and
"MCD19A2" - Aerosol optical depth (AOD).
For a full list of available
MODIS product codes, see the "Short Name" column at
NASA LP DAAC Search Data Catalog.
When utilizing a product code from this "Short Name" column, do
not include the version number following the period. For example, if "Short
Name" = MCD12C1.006, then product = "MCD12C1".
Insang Song
process_modis_sds(product = "MOD09GA")process_modis_sds(product = "MOD09GA")
This function will return a SpatRaster object with
values of selected subdatasets. Swath data include curvilinear
grids, which require warping/rectifying the original curvilinear grids
into rectilinear grids. The function internally warps each of inputs
then mosaic the warped images into one large SpatRaster object.
Users need to select a subdataset to process. The full path looks like
"HDF4_EOS:EOS_SWATH:{file_path}:mod06:subdataset", where file_path is
the full path to the hdf file.
process_modis_swath( path = NULL, date = NULL, subdataset = NULL, suffix = ":mod06:", resolution = 0.05, ... )process_modis_swath( path = NULL, date = NULL, subdataset = NULL, suffix = ":mod06:", resolution = 0.05, ... )
path |
character. Full paths of hdf files. |
date |
character(1). Date to query. |
subdataset |
character. Subdatasets to process.
Unlike other preprocessing functions, this argument should specify
the exact subdataset name. For example, when using MOD06_L2 product,
one may specify |
suffix |
character(1). Should be formatted |
resolution |
numeric(1). Resolution of output raster. Unit is degree (decimal degree in WGS84). |
... |
For internal use. |
a SpatRaster object (crs = "EPSG:4326"): if path is a single file with
full specification of subdataset.
a SpatRaster object (crs = "EPSG:4326"): if path is a list of files. In this case, the returned object will have the maximal extent of multiple warped layers
Insang Song
terra::describe(): to list the full subdataset list with sds = TRUE
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: mod06l2_swath <- process_modis_swath( path = list.files( "./data/mod06l2", full.names = TRUE, pattern = ".hdf" ), date = "2024-01-01", subdataset = "Cloud_Fraction", suffix = ":mod06:", resolution = 0.05 ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: mod06l2_swath <- process_modis_swath( path = list.files( "./data/mod06l2", full.names = TRUE, pattern = ".hdf" ), date = "2024-01-01", subdataset = "Cloud_Fraction", suffix = ":mod06:", resolution = 0.05 ) ## End(Not run)
Swath data is a type of MODIS data,
where curvilinear points are stored with varying resolution depending on
the relative position of the sensor axis. As this type of data
typically does not work well with planar spatial data, users
should warp or rectify this data into a rectilinear raster.
Main procedure is done with stars::st_warp, in which users are able to
customize the threshold to fill potential gaps that appear where
the target resolution is finer than the local resolution of curvilinear
grid points.
process_modis_warp( path = NULL, cellsize = 0.1, threshold = cellsize * 4, crs = 4326, ... )process_modis_warp( path = NULL, cellsize = 0.1, threshold = cellsize * 4, crs = 4326, ... )
path |
File path of MODIS swath with exact sub-dataset specification. |
cellsize |
numeric(1). Cell size (spatial resolution) of output rectilinear grid raster. |
threshold |
numeric(1). Maximum distance to fill gaps if occur. |
crs |
integer(1)/character(1). Coordinate system definition.
Should be compatible with EPSG codes or WKT2.
See |
... |
For internal use. |
a stars object
This function handles one file at a time.
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: mod06l2_warp <- process_modis_warp( path = paste0( "HDF4_EOS:EOS_SWATH:", list.files( "./data/mod06l2", full.names = TRUE, pattern = ".hdf" )[1], ":mod06:Cloud_Fraction" ), cellsize = 0.1, threshold = 0.4, crs = 4326 ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: mod06l2_warp <- process_modis_warp( path = paste0( "HDF4_EOS:EOS_SWATH:", list.files( "./data/mod06l2", full.names = TRUE, pattern = ".hdf" )[1], ":mod06:Cloud_Fraction" ), cellsize = 0.1, threshold = 0.4, crs = 4326 ) ## End(Not run)
The process_narr() function imports and cleans raw meteorological
data, returning a single SpatRaster object.
process_narr( date = "2023-09-01", variable = NULL, path = NULL, extent = NULL, ... )process_narr( date = "2023-09-01", variable = NULL, path = NULL, extent = NULL, ... )
date |
character(1 or 2). Date (1) or start and end dates (2). Format YYYY-MM-DD (ex. September 1, 2023 = "2023-09-01"). |
variable |
character(1). Variable name acronym. See List of Variables in NARR Files for variable names and acronym codes. |
path |
character(1). Directory with downloaded netCDF (.nc) files. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object
Layer names of the returned SpatRaster object contain the variable acronym,
pressure level, and date.
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: process_narr( date = c("2018-01-01", "2018-01-10"), variable = "weasd", path = "./tests/testdata/narr/weasd" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: process_narr( date = c("2018-01-01", "2018-01-10"), variable = "weasd", path = "./tests/testdata/narr/weasd" ) ## End(Not run)
The process_nei() function imports and cleans raw road emissions data,
returning a single SpatVector object.
NEI data comprises multiple csv files where emissions of 50+ pollutants are recorded at county level. With raw data files, this function will join a combined table of NEI data and county boundary, then perform a spatial join to target locations.
process_nei(path = NULL, county = NULL, year = c(2017, 2020), ...)process_nei(path = NULL, county = NULL, year = c(2017, 2020), ...)
path |
character(1). Directory with NEI csv files. |
county |
|
year |
integer(1) Year to use. Currently only 2017 or 2020 is accepted. |
... |
Placeholders. |
a SpatVector object
Base files for county argument can be downloaded directly from
U.S. Census Bureau
or by using tigris package. This function does not reproject census boundaries.
Users should be aware of the coordinate system of census boundary data for
other analyses.
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: nei <- process_nei( path = "./data", county = system.file("gpkg/nc.gpkg", package = "sf"), year = 2017 ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: nei <- process_nei( path = "./data", county = system.file("gpkg/nc.gpkg", package = "sf"), year = 2017 ) ## End(Not run)
The process_nlcd() function imports and cleans raw land cover data,
returning a single SpatRaster object.
Reads NLCD file of selected year.
process_nlcd(path = NULL, year = 2021, extent = NULL, ...)process_nlcd(path = NULL, year = 2021, extent = NULL, ...)
path |
character giving nlcd data path |
year |
numeric giving the year of NLCD data used |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object
Eva Marques, Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: nlcd <- process_nlcd( path = "./data/", year = 2021 ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: nlcd <- process_nlcd( path = "./data/", year = 2021 ) ## End(Not run)
This function imports and cleans raw PRISM data,
returning a single SpatRaster object.
Reads time series or 30-year normal PRISM data.
process_prism(path = NULL, element = NULL, time = NULL, extent = NULL, ...)process_prism(path = NULL, element = NULL, time = NULL, extent = NULL, ...)
path |
character giving PRISM data path Both file and directory path are acceptable. |
element |
character(1). PRISM element name |
time |
character(1). PRISM time name. Should be character in length of 2, 4, 6, or 8. "annual" is acceptable. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object with metadata of time and element.
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: prism <- process_prism( path = "./data/PRISM_ppt_stable_4kmM3_202104_nc.nc", element = "ppt", time = "202104" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: prism <- process_prism( path = "./data/PRISM_ppt_stable_4kmM3_202104_nc.nc", element = "ppt", time = "202104" ) ## End(Not run)
The process_sedac_groads() function imports and cleans raw road data,
returning a single SpatVector object.
process_sedac_groads(path = NULL, extent = NULL, ...)process_sedac_groads(path = NULL, extent = NULL, ...)
path |
character(1). Path to geodatabase or shapefiles. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatVector object
U.S. context. The returned SpatVector object contains a
$description column to represent the temporal range covered by the
dataset. For more information, see https://earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-groads-v1-1.00.
Insang Song
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: groads <- process_sedac_groads( path = "./data/groads_example.shp" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: groads <- process_sedac_groads( path = "./data/groads_example.shp" ) ## End(Not run)
The process_secac_population() function imports and cleans raw
population density data, returning a single SpatRaster object.
process_sedac_population(path = NULL, extent = NULL, ...)process_sedac_population(path = NULL, extent = NULL, ...)
path |
character(1). Path to GeoTIFF (.tif) or netCDF (.nc) file. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: pop <- process_sedac_population( path = "./data/sedac_population_example.tif" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: pop <- process_sedac_population( path = "./data/sedac_population_example.tif" ) ## End(Not run)
The process_terraclimate() function imports and cleans climate and water balance
data, returning a single SpatRaster object.
process_terraclimate( date = c("2023-09-01", "2023-09-10"), variable = NULL, path = NULL, extent = NULL, ... )process_terraclimate( date = c("2023-09-01", "2023-09-10"), variable = NULL, path = NULL, extent = NULL, ... )
date |
character(1 or 2). Date (1) or start and end dates (2). Format YYYY-MM-DD (ex. September 1, 2023 = "2023-09-01"). |
variable |
character(1). Variable name or acronym code. See TerraClimate Direct Downloads for variable names and acronym codes. |
path |
character(1). Directory with downloaded netCDF (.nc) files. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatRaster object
Layer names of the returned SpatRaster object contain the variable acronym, year,
and month.
TerraClimate data has monthly temporal resolution, so the first day of each month is used as a placeholder temporal value.
Mitchell Manware
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: terraclimate <- process_terraclimate( date = c("2023-01-01", "2023-01-10"), variable = "Precipitation", path = "./data/ppt" ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: terraclimate <- process_terraclimate( date = c("2023-01-01", "2023-01-10"), variable = "Precipitation", path = "./data/ppt" ) ## End(Not run)
This function imports and cleans raw toxic release data,
returning a single SpatVector (points) object for the selected year.
process_tri( path = NULL, year = 2018, variables = c(1, 13, 12, 14, 20, 34, 36, 47, 48, 49), extent = NULL, ... )process_tri( path = NULL, year = 2018, variables = c(1, 13, 12, 14, 20, 34, 36, 47, 48, 49), extent = NULL, ... )
path |
character(1). Path to the directory with TRI CSV files |
year |
integer(1). Single year to select. |
variables |
integer. Column index of TRI data. |
extent |
numeric(4) or SpatExtent giving the extent of the raster
if |
... |
Placeholders. |
a SpatVector object (points) in year
year is stored in a field named "year".
Visit TRI Data and Tools to view the available years and variables.
Insang Song, Mariana Kassien
https://www.epa.gov/toxics-release-inventory-tri-program/tri-toolbox
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: tri <- process_tri( path = "./data", year = 2020, variables = c(1, 13, 12, 14, 20, 34, 36, 47, 48, 49) ) ## End(Not run)## NOTE: Example is wrapped in `\dontrun{}` as function requires a large ## amount of data which is not included in the package. ## Not run: tri <- process_tri( path = "./data", year = 2020, variables = c(1, 13, 12, 14, 20, 34, 36, 47, 48, 49) ) ## End(Not run)
sf to an sftime
Convert an sf object to an sftime object. x must
contain a time-defining column, identified in timename.
sf_as_mysftime(x, timename)sf_as_mysftime(x, timename)
x |
an |
timename |
character: name of time column in x |
an sftime object
Eva Marques
sftime to a mysftime
Convert an sftime object to a mysftime object. x must
contain a time-defining column, identified in timename.
sftime_as_mysftime(x, timename)sftime_as_mysftime(x, timename)
x |
an |
timename |
character: name of time column in |
an sftime object with specific format
Eva Marques
sftime to an sf
Convert an sftime object to an sf object. x must
contain a time-defining column, identified in timename.
sftime_as_sf(x, keeptime = TRUE)sftime_as_sf(x, keeptime = TRUE)
x |
an |
keeptime |
boolean: TRUE if user wants to keep time column as simple column (default = TRUE) |
an sf object
Eva Marques
sftime to a SpatRaster
Convert an sftime object to a SpatRaster object. Returns a
SpatRatser with one layer for each time step in x.
sftime_as_spatraster(x, varname)sftime_as_spatraster(x, varname)
x |
an |
varname |
variable to rasterize |
a SpatRaster object
Running sftime_as_spatraster can take a long time if x is not
spatially structured.
Eva Marques
sftime to a SpatRasterDataset
Convert an sftime object to a SpatRasterDataset object.
sftime_as_spatrds(x)sftime_as_spatrds(x)
x |
an |
an SpatRasterDataset object
Running sftime_as_spatrds can take a long time if x is not
spatially and temporally structured.
Eva Marques
sftime to a SpatVector
Convert an sftime object to a SpatVector object.
sftime_as_spatvector(x)sftime_as_spatvector(x)
x |
an |
a SpatVector object
Eva Marques
SpatRaster to an sftime
Convert a SpatRaster object to an sftime object. x must
contain a time-defining column, identified in timename.
spatraster_as_sftime(x, varname, timename = "time")spatraster_as_sftime(x, varname, timename = "time")
x |
a |
varname |
character for variable column name in the sftime |
timename |
character for time column name in the sftime (default: "time") |
a sftime object
Eva Marques
SpatRasterDataset to an sftime
Convert a SpatRasterDataset object to an sftime object.
x must contain a time-defining column, identified in timename.
spatrds_as_sftime(x, timename = "time")spatrds_as_sftime(x, timename = "time")
x |
a |
timename |
character for time column name in the sftime (default: "time") |
an sftime object
Eva Marques
SpatVector to an sftime
Convert a SpatVector object to an sftime object. x must
contain a time-defining column, identified in timename.
spatvector_as_sftime(x, timename = "time")spatvector_as_sftime(x, timename = "time")
x |
a |
timename |
character for time column name in x (default: "time") |
an sftime object
Eva Marques
Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates
sum_edc( from = NULL, locs = NULL, locs_id = NULL, sedc_bandwidth = NULL, target_fields = NULL, geom = FALSE )sum_edc( from = NULL, locs = NULL, locs_id = NULL, sedc_bandwidth = NULL, target_fields = NULL, geom = FALSE )
from |
|
locs |
|
locs_id |
character(1). Name of the unique id field in |
sedc_bandwidth |
numeric(1).
Distance at which the source concentration is reduced to
|
target_fields |
character(varying). Field names in characters. |
geom |
FALSE/"sf"/"terra".. Should the function return with geometry?
Default is |
a data.frame (tibble) or SpatVector object with input field names with
a suffix "_sedc" where the sums of EDC are stored.
Additional attributes are attached for the EDC information.
'attr(result, "sedc_bandwidth")“: the bandwidth where concentration reduces to approximately five percent
'attr(result, "sedc_threshold")“: the threshold distance at which emission source points are excluded beyond that
The function is originally from
chopin
Distance calculation is done with terra functions internally.
Thus, the function internally converts sf objects in
point_* arguments to terra.
The threshold should be carefully chosen by users.
Insang Song
Messier KP, Akita Y, Serre ML (2012). “Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene.” Environmental Science & Technology, 46(5), 2772–2780. ISSN 0013-936X, doi:10.1021/es203152a.
Wiesner C (????). “Euclidean Sum of Exponentially Decaying Contributions Tutorial.”
set.seed(101) ncpath <- system.file("gpkg/nc.gpkg", package = "sf") nc <- terra::vect(ncpath) nc <- terra::project(nc, "EPSG:5070") pnt_locs <- terra::centroids(nc, inside = TRUE) pnt_locs <- pnt_locs[, "NAME"] pnt_from <- terra::spatSample(nc, 10L) pnt_from$pid <- seq(1, 10) pnt_from <- pnt_from[, "pid"] pnt_from$val1 <- rgamma(10L, 1, 0.05) pnt_from$val2 <- rgamma(10L, 2, 1) vals <- c("val1", "val2") sum_edc(pnt_locs, pnt_from, "NAME", 1e4, vals)set.seed(101) ncpath <- system.file("gpkg/nc.gpkg", package = "sf") nc <- terra::vect(ncpath) nc <- terra::project(nc, "EPSG:5070") pnt_locs <- terra::centroids(nc, inside = TRUE) pnt_locs <- pnt_locs[, "NAME"] pnt_from <- terra::spatSample(nc, 10L) pnt_from$pid <- seq(1, 10) pnt_from <- pnt_from[, "pid"] pnt_from$val1 <- rgamma(10L, 1, 0.05) pnt_from$val2 <- rgamma(10L, 2, 1) vals <- c("val1", "val2") sum_edc(pnt_locs, pnt_from, "NAME", 1e4, vals)