Welcome!

climateR simplifies the steps needed to get climate data into R. At its core it provides three main things:

1. A catalog of over 100,000k datasets from over 2,000 data providers/archives. See (climateR::params)

nrow(params)
#> [1] 107857
length(unique(params$id))
#> [1] 2075
length(unique(params$asset))
#> [1] 4653

This catalog is an evolving, federated collection of datasets that can be accessed by the data access utilities.

2. A general toolkit for accessing remote and local gridded data files bounded by space, time, and variable constraints (dap, dap_crop, read_dap_file)

3. A set of shortcuts that implement these methods for a core set of selected catalog elements

⚠️ Python Users: Data catalog access is available through the USGS gdptools package. Directly analogous climateR functionality can be found in climatePy

Installation

remotes::install_github("mikejohnson51/AOI") # suggested!
remotes::install_github("mikejohnson51/climateR")

Basic Usage

Finding rainfall in Colorado between October 29,1991 - November 6, 1991. The source dataset for this example uses the getGridMET shortcut.

library(AOI)
library(terra)
library(climateR)

AOI = aoi_get(state = "CO", county = "all")

system.time({
  d = getGridMET(AOI,
               varname = "pr",
               startDate = "1991-10-29",
               endDate  = "1991-11-06")
})
#>    user  system elapsed 
#>   0.245   0.054   0.982

Basic Animation

animation(d$precipitation_amount, AOI = AOI, outfile = "man/figures/rast_gif.gif")

Integration with zonal

library(zonal)

system.time({
  county = execute_zonal(d, geom = AOI, ID = "fip_code")
})
#>    user  system elapsed 
#>   0.328   0.018   0.366

animation(county, feild_pattern = "pr_", outfile = "man/figures/vect_gif.gif")