obpg

Access OPeNDAP OBPG data from R. Extract point or bounded boxes (as raster).

Find a catalog of the available OBPG data offerings here

Requirements

• R v4.1+

Packages from CRAN:

• rlang
• dplyr
• sf
• stars
• tidyr
• ncdf4

Packages from Github:

• xyzt

Installation

remotes::install_github("BigelowLab/obpg")

Usage

suppressPackageStartupMessages({
  library(dplyr)
  library(sf)
  library(stars)
  
  library(xyzt)
  library(obpg)
})

Working with points.

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

Working with bounding boxes

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)

Climatologies

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)