Access OPeNDAP OBPG data from R. Extract point or bounded boxes (as raster).
Find a catalog of the available OBPG data offerings here
Packages from CRAN:
Packages from Github:
remotes::install_github("BigelowLab/obpg")
suppressPackageStartupMessages({
library(dplyr)
library(sf)
library(stars)
library(xyzt)
library(obpg)
})See the xyzt package for more details on the example Gulf of Maine data.
# read in example GOM points
x <- xyzt::read_gom() |>
dplyr::select(-time, -depth) |>
xyzt::as_POINT()
# generate a MUR url for a given date
url <- obpg_url("2020-07-12")
# open the resource
X <- ncdf4::nc_open(url)
# extract the data
covars <- obpg::extract(x, X, varname = obpg::obpg_vars(X))
# bind to the input
(y <- dplyr::bind_cols(x, covars))## Simple feature collection with 5 features and 3 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -70.17 ymin: 40.5 xmax: -66.59 ymax: 43.5
## Geodetic CRS: WGS 84
## # A tibble: 5 × 4
## id name geometry sst
## * <chr> <chr> <POINT [°]> <dbl>
## 1 44098 Jeffreys Ledge (-70.17 42.81) 19.8
## 2 44005 Cashes Ledge (-69.22 43.17) 19.9
## 3 44037 Jordan Basin (-67.87 43.5) 17.6
## 4 44011 Georges Bank (-66.59 41.09) 23.9
## 5 44008 Nantucket SE (-69.24 40.5) 17.3
x <- xyzt::read_gom() |>
dplyr::select(-time, -depth) |>
xyzt::as_BBOX()
covars <- obpg::extract(x, X, varname = obpg::obpg_vars(X))
covars## stars object with 2 dimensions and 1 attribute
## attribute(s):
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## sst 11.1 17.505 18.855 18.72126 19.915 25.1 61
## dimension(s):
## from to offset delta refsys x/y
## x 1 45 -70.17 0.0795556 WGS 84 [x]
## y 1 38 43.5 -0.0789474 WGS 84 [y]
Plot the bounding box of data with the points we pulled above:
x <- xyzt::read_gom() |>
dplyr::select(-time, -depth) |>
xyzt::as_POINT()
par(mfrow = c(1,2))
plot(covars, attr = 'sst', col = sf.colors(n=16), axes = TRUE, reset = FALSE)
plot(sf::st_geometry(x), add = TRUE, col = "green", pch = 19, cex = 2)
# cleanup
ncdf4::nc_close(X)Mission-length climatologies are available (for the entire mission, a particular season or month.) You can search for these. Here we find the seasonal (northern) summer.
uri <- query_obpg_climatology(climatology = "SCSU", res = "9km", param = "SST.sst")
uri## [1] "https://oceandata.sci.gsfc.nasa.gov/opendap/MODISA/L3SMI/2002/0621/AQUA_MODIS.20020621_20220920.L3m.SCSU.SST.sst.9km.nc"
x <- xyzt::read_gom() |>
dplyr::select(-time, -depth) |>
xyzt::as_BBOX()
# open the resource
X <- ncdf4::nc_open(uri)
covars <- obpg::extract(x, X, varname = obpg::obpg_vars(X), flip = "none")
covars## stars object with 2 dimensions and 1 attribute
## attribute(s):
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## sst 8.78 18.585 24.02 22.66712 26.755 31.91 6988
## dimension(s):
## from to offset delta refsys x/y
## x 1 155 -70.17 0.0230968 WGS 84 [x]
## y 1 148 43.5 -0.0202703 WGS 84 [y]
x <- xyzt::read_gom() |>
dplyr::select(-time, -depth) |>
xyzt::as_POINT()
par(mfrow = c(1,2))
plot(covars, attr = 'sst', col = sf.colors(n=16), axes = TRUE, reset = FALSE)
plot(sf::st_geometry(x), add = TRUE, col = "green", pch = 19, cex = 2)
# cleanup
ncdf4::nc_close(X)