Awesome
<!-- README.md is generated from README.Rmd. Please edit that file -->climateR-catalogs
<!-- badges: start --> <!-- badges: end -->The goal of climateR-catalogs is to “automate” a collection of data
catalogs usable with R(e.g. terra, climateR, gdalio, geoknife,
stars) and in Python (e.g. gdptools)
The catalog(s) are built using targets in this repo, and deployed as
JSON/parquet/rds artifacts at
“https://mikejohnson51.github.io/climateR-catalogs/catalog.{ext}”.
Scope
The catalog will hope to support 4 types of data source and will limit itself to non-authenticated public data assets. The included types will be:
- Aggregated NetCDF (via OpenDap)
- Non aggregated NetCDF (via OpenDap)
- tif/COG/vrt hosted by s3/FTP/ect
- STAC catalog (WIP)
Targets
Targets will be used in an attempt to ensure maintenance is possible. The interactive version of the latest run can be seen here here
Schema
To align the 4 categories of data a WIP schema can be found here
Catalog to date:
The catalog looks like this on 2023-03-08 reading local version (same as
pushed one found in docs)
cat = read_parquet("docs/catalog.parquet")
# Unique datasets
nrow(cat)
#> [1] 107857
# Unique products
length(unique(cat$asset))
#> [1] 4653
Examples
Here is a minimal example with the base information added:
pacman::p_load(dplyr, climateR, AOI, terra)
nlcd = filter(cat, id == "NLCD", asset == '2019 Land Cover L48')
t(nlcd)
#> [,1]
#> id "NLCD"
#> asset "2019 Land Cover L48"
#> URL "/vsicurl/https://storage.googleapis.com/feddata-r/nlcd/2019_Land_Cover_L48.tif"
#> type "vrt"
#> varname "Land_Cover"
#> variable "Land_Cover"
#> description "USGS NLCD Land Cover 2019 L48"
#> units ""
#> model NA
#> ensemble NA
#> scenario NA
#> T_name NA
#> duration NA
#> interval NA
#> nT NA
#> X_name NA
#> Y_name NA
#> X1 "-2493045"
#> Xn "2342655"
#> Y1 "-2493045"
#> Yn "3310005"
#> resX "30"
#> resY "30"
#> ncols "161190"
#> nrows "104424"
#> crs "+proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
#> toptobottom NA
#> tiled ""
(output = dap(catalog = nlcd, AOI = aoi_get("Fort Collins")))
#> $`2019 Land Cover L48`
#> class : SpatRaster
#> dimensions : 667, 548, 1 (nrow, ncol, nlyr)
#> resolution : 30, 30 (x, y)
#> extent : -768615, -752175, 1975575, 1995585 (xmin, xmax, ymin, ymax)
#> coord. ref. : Albers Conical Equal Area
#> source(s) : memory
#> color table : 1
#> categories : NLCD Land Cover Class, Histogram, Red, Green, Blue, Opacity
#> name : NLCD Land Cover Class
#> min value : Open Water
#> max value : Emergent Herbaceous Wetlands
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This is still a little clunky but you can pass multi-row “catalog” data.frames straight to dap! For example say you want soil sand content, land cover and elevation for the city of Fort Collins:
multilayer = filter(cat, asset %in% c("2019 Land Cover L48", "30m CONUS DEM", "sand_mean_0_5"))
output = sapply(1:nrow(multilayer), function(x){
dap(catalog = multilayer[x,], AOI = aoi_get("Fort Collins"))
})
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Hitting OpenDap resources!
dap_resource = filter(cat,
id == 'bcca',
variable == 'tasmin',
model == 'MPI-ESM-LR',
ensemble == 'r1i1p1',
scenario == "historical")
t(dap_resource)
#> [,1]
#> id "bcca"
#> asset "historical"
#> URL "https://cida.usgs.gov/thredds/dodsC/cmip5_bcca/historical"
#> type "opendap"
#> varname "BCCA_0-125deg_tasmin_day_MPI-ESM-LR_historical_r1i1p1"
#> variable "tasmin"
#> description "Daily Minimum Near-Surface Air Temperature"
#> units "C"
#> model "MPI-ESM-LR"
#> ensemble "r1i1p1"
#> scenario "historical"
#> T_name "time"
#> duration "1950-01-01 12:00:00/2005-12-31 12:00:00"
#> interval "1 days"
#> nT "20454"
#> X_name "longitude"
#> Y_name "latitude"
#> X1 "-124.6875"
#> Xn "-67.0625"
#> Y1 "25.1875"
#> Yn "52.8125"
#> resX "0.125"
#> resY "0.125"
#> ncols "462"
#> nrows "222"
#> crs "+proj=longlat +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs"
#> toptobottom "TRUE"
#> tiled "T"
data = dap(URL = dap_resource$URL,
AOI = aoi_get(state = "FL"),
varname = dap_resource$varname,
startDate = "2000-10-01",
endDate = "2000-10-04")
#> source: https://cida.usgs.gov/thredds/dodsC/cmip5_bcca/historical
#> varname(s):
#> > BCCA_0-125deg_tasmin_day_MPI-ESM-LR_historical_r1i1p1 [C] (Daily Minimum Near-Surface Air Temperature)
#> ==================================================
#> diminsions: 63, 48, 4 (names: longitude,latitude,time)
#> resolution: 0.125, 0.125, 1 days
#> extent: -87.75, -79.88, 25.12, 31.12 (xmin, xmax, ymin, ymax)
#> crs: +proj=longlat +a=6378137 +f=0.00335281066474748 +p...
#> time: 2000-09-30 12:00:00 to 2000-10-03 12:00:00
#> ==================================================
#> values: 12,096 (vars*X*Y*T)
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Global
nz_soil = filter(cat, id == "ISRIC Soil Grids", variable == 'silt_0-5cm_mean')
data = dap(URL = nz_soil$URL, AOI = aoi_get(country = "New Zealand"))
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