In mikejohnson51/opendap.catalog: R OpenDap Client

knitr::opts_chunk$set(
  collapse = TRUE,
  warning  = FALSE,
  message  = FALSE,
  comment = "#>"
)

library(opendap.catalog)
library(dplyr)
library(terra)

Intro

The initial Vignette highlighted how to extract subsets of data when the source data set was known. A second goal of opendap.catalog is to provide an evolving catalog of available data resources with the information needed to access them. As of r format(Sys.Date(), format = "%m/%Y") there are r nrow(opendap.catalog::params) datasets identified and stored in opendap.catalog::params. In these there are r length(unique(opendap.catalog::params$id)) unique data sources, and r length(unique(opendap.catalog::params$varname)) unique variables.

dplyr::glimpse(opendap.catalog::params)

For use in other applications (e.g. stars proxy, geoknife, climateR or python/go/Rust applciations) this catalog is available as a JSON artifact here.

read_json('https://mikejohnson51.github.io/opendap.catalog/cat_params.json', simplifyVector = TRUE)

Contents

| Name | Description | Details | |---------- | ----------- | -------- | | id | opendap.catalog unique identifier | | | grid.id | opendap.catalog unique grid identifier | See opendap.catalog::grids | | URL | Location of resource | | | tiled | Is the resource temporally (T) or spatially (XY) tiled | See Vignette 3| | variable | name of the variable in data description | | | varname | name of variable in data set description | | | long_name | NetCDF Long Name attribute | | | units | NetCDF Long Name units attribute | | | model | Model Type/version | Often GCM | | ensemble | Model ensemble member | | | scenario | Model scenario | Often RCPs | | T_name | Name of time dimension | | | duration | start and end date of data (start/end) | | | interval | time step between duration | | | nT | number of time slices | |

Example

search <- filter(params, 
                 grepl("NLDAS", id),
                 grepl("FOR", model),
                 grepl("potential evaporation", long_name),
                 scenario == 'M.2.0',
                 grepl("month", interval))


glimpse(search)

Catalog item rather then URL!

tx_2021_pet = dap(catolog = search, 
                  AOI = AOI::aoi_get(state = "TX"),
                  startDate = "2021-01-01", 
                  endDate = "2021-12-31")

plot(tx_2021_pet$potevap_M.2.0)

Grid Relation

glimpse(grids)

| Name | Description | Details | |----- | ----------- | -------- | | grid.id | opendap.catalog unique grid identifier | | | X_name | X dimension name in data set | | | Y_name | Y dimension name in data set | | | X1 | The first X value in the X_name array | | | Xn | The last X value in the X_name array | | | Y1 | The first Y value in the X_name array | | | Yn | The last Y value in the X_name array | | | resX | Resolution of X_name array | | | resY | Resolution of Y_name array | | | ncols | Number of columns in the XY array | | | nrows | Number of rows in the XY array | | | proj | Data set projection | | | toptobottom | It is organized in left to right,top to bottom pixel order. | | | tile | If the dataset has a tiled XY partition, what is the tile name? | |

glimpse(merge(search, grids))

Like parms, grids attributes can also be accessed as a JSON:

read_json('https://mikejohnson51.github.io/opendap.catalog/cat_grids.json', simplifyVector = TRUE)

Challenge

(search <- filter(params, 
                  grepl("MOD16A2.006", id), 
                  grepl("PET_500m", varname)))

glimpse(merge(search, grids))

mikejohnson51/opendap.catalog documentation built on Jan. 27, 2023, 1:25 a.m.