Provides for access to NBS 2.0 from NOAA Coastwatch which replaces the first version called Blended Sea Winds online.
This package provides tools for querying, downloading and archiving blended sea wind raster files. Data are served on THREDDS catalog using OPeNDAP services. We hide all of the details and provide a simplified interface to download rasters (saved as GeoTIFF) and simple database organization.
Saha, K.; Huai-Min, Z. Hurricane and Typhoon Storm Wind Resolving NOAA NCEI Blended Sea Surface Wind (NBS) Product. Frontiers in Marine Sciences – Ocean Observation 2022, 9, 1–12. https://doi.org/10.3389/fmars.2022.935549.
From CRAN…
remotes::install_github("BigelowLab/nbs")
suppressPackageStartupMessages({
library(nbs)
library(dplyr)
library(sf)
library(stars)
})Our workflow requires a path where you will save your requested data. We
suggest that you store your root data path in a hidden configuration
file (defaults to ~/.nbsdata). This is a fix-and-forget step, but you
can override or change it later. Here is how we set up my data path.
my_data_path = "/Users/ben/Library/CloudStorage/Dropbox/data/noaa/nbs"
ok = dir.create(my_data_path, showWarnings = FALSE, recursive = TRUE)
nbs::set_root_path(my_data_path)
That’s it. When you later download data for some region, say for the
Mediterranean, you can specify a subfolder, perhaps something like
med, and store regional data there.
Blended Sea Winds are organized by product and interval. The two primary
product types are wind velocities and wind stresses. Each product type
is organized further by aggregation interval [6h, daily, monthly]. In
addition, NOAA/NCEI serves up both “near real time” (NRT) and
science-digest versions (SCI). These are served as “aggregate” or
“per-file” OPeNDAP resources on the THREDDS server. We find that
accessing the “per-file” resources is more responsive than the otherwise
more convenient “aggregate” resources.
Here we tally the the 10 resources available to us. Caveat - we don’t use the near real time (NRT) resources thus we haven’t tested them.
nbs_tally()## [1] "Global Six-Hour UVComp" "Global Six-Hour Stress" "Global Daily UVComp"
## [4] "Global Daily Stress" "Global Monthly UVComp" "Global Monthly Stress"
## [7] "Global Six-Hour UVComp" "Global Six-Hour Stress" "Global Daily UVComp"
## [10] "Global Daily Stress"
Use nbs_query() to search for product URLs.
uris = query_nbs(period = "monthly",
dates = as.Date(c("1995-01-01", "1995-12-31")),
product = "uvcomp") |>
dplyr::glimpse()## Rows: 12
## Columns: 3
## $ date <date> 1995-01-01, 1995-02-01, 1995-03-01, 1995-04-01, 1995-05-01, 19…
## $ dateid <chr> "199501", "199502", "199503", "199504", "199505", "199506", "19…
## $ url <chr> "https://www.star.nesdis.noaa.gov/thredds/dodsC/uvcompNCEIBlend…
When data are fetched they are automatically stored in GeoTIFF format in a database-like directory structure in the path of your own choosing. Each GeoTIFF file contains one parameter, at one depth and one time.
We’ll download just a portion of the western South Atlantic. First we
need to make the destination path for the data, and then define the
bounding box. Then we simply pass the table of URLs and the bounding box
to fetch_nbs(). We also provide the bounding box and path.
param = "all" is shorthand for fetching
c("u_wind", "v_wind", "windspeed") from the uvcomp product.
In return we’ll receive a small table (aka database) listing the downloaded files.
path = nbs_path("wsa")
ok = dir.create(path, recursive = TRUE, showWarnings = FALSE)
bb = c(xmin = 290, ymin = -60, xmax = 360, ymax = 10)
db = fetch_nbs(uris, params = 'all', bb = bb, path = path, verbose = FALSE)The database contains identifying information about the stored files except for the path to the storage location. This adds a little extra work for the end user (keeping track of the data path), but that extra effort is offset by improved portability and ease of use.
db## # A tibble: 36 × 7
## source date hour param per nrt time
## <chr> <date> <chr> <chr> <chr> <lgl> <dttm>
## 1 uvcomp_SCIMonthlyGlob… 1995-01-15 0000… u_wi… month FALSE 1995-01-15 00:00:00
## 2 uvcomp_SCIMonthlyGlob… 1995-01-15 0000… v_wi… month FALSE 1995-01-15 00:00:00
## 3 uvcomp_SCIMonthlyGlob… 1995-01-15 0000… wind… month FALSE 1995-01-15 00:00:00
## 4 uvcomp_SCIMonthlyGlob… 1995-02-15 0000… u_wi… month FALSE 1995-02-15 00:00:00
## 5 uvcomp_SCIMonthlyGlob… 1995-02-15 0000… v_wi… month FALSE 1995-02-15 00:00:00
## 6 uvcomp_SCIMonthlyGlob… 1995-02-15 0000… wind… month FALSE 1995-02-15 00:00:00
## 7 uvcomp_SCIMonthlyGlob… 1995-03-15 0000… u_wi… month FALSE 1995-03-15 00:00:00
## 8 uvcomp_SCIMonthlyGlob… 1995-03-15 0000… v_wi… month FALSE 1995-03-15 00:00:00
## 9 uvcomp_SCIMonthlyGlob… 1995-03-15 0000… wind… month FALSE 1995-03-15 00:00:00
## 10 uvcomp_SCIMonthlyGlob… 1995-04-15 0000… u_wi… month FALSE 1995-04-15 00:00:00
## # ℹ 26 more rows
We provide functions, write_database() and read_database(), to
simplify the input-output.
write_database(db, path)We can filter the database using standard tools, and then read in the data as rasters.
db2 = dplyr::filter(db, param %in% c("u_wind", "v_wind"),
date > as.Date("1995-10-01"),
date <= as.Date("1995-12-31"))
x = read_nbs(db2, path)
x## stars object with 3 dimensions and 2 attributes
## attribute(s):
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## u_wind -10.904867 -4.461770 0.05806307 0.1607201 4.617272 11.819275 61099
## v_wind -6.077292 -1.241886 -0.10956497 0.2898438 1.383320 8.025926 61099
## dimension(s):
## from to offset delta refsys point
## x 1 281 289.6 0.25 WGS 84 FALSE
## y 1 282 10.12 -0.25 WGS 84 FALSE
## time 1 3 NA NA POSIXct NA
## values x/y
## x NULL [x]
## y NULL [y]
## time 1995-10-15 UTC, 1995-11-15 UTC, 1995-12-15 UTC
Note that the object has two attributes (“u_wind” and “v_wind”) and three layers (October, November and December).
plot(x['u_wind'])
plot(x['v_wind'])