Serving Gulf Stream datasets directly from R.
Use the remotes package to install directly from github.
remotes::install("BigelowLab/gstream)
The data for this package is manually curated - and we’ll update it as needed.
The data used for this package is maintained here. Manually download the data, uncompress it and place it in your favorite data storage site.
Next we need to allow this package to securely know where you have
saved the data. We do this by placing a hidden text file in your
home directory. If you are sharing this data with others (say on a
network drive) then each user of the package will need to set up this
file. So, into this file named ~/.gstream place this content.
Obviously, you will want to replace the paths with ones appropriate for
your platform.
path: /mnt/s1/projects/ecocast/coredata/gstream
usn:
datapath: /mnt/s1/projects/ecocast/coredata/gstream/usn
rawpath: /mnt/s1/projects/ecocast/coredata/gstream/usn/raw
dailyuri: https://ocean.weather.gov/gulf_stream_latest.txt
ftpuri: https://ftp.opc.ncep.noaa.gov/grids/experimental/GStream
Now you can test if the package can find the path yout specified.
suppressPackageStartupMessages({
library(gstream)
library(sf)
library(dplyr)
library(rnaturalearth)
})
path = gstream_path()
path## [1] "/mnt/s1/projects/ecocast/coredata/gstream"
The package contains a number of data sets compiled with the purpose of aiding Gulf Stream and AMOC analyses. Beyond access and simple plotting utilities, no effort has been made to include sophisticated analyses.
The Gulf Stream Index provides a positional index. Data are provides via the ecodata R package. If the package is installed, then this package serves the data it provides with a convneient plotting routine. If the package is not installed, then it is an error to try to read the GSI index with this package.
x = read_gsi() |>
dplyr::glimpse()## Rows: 1,676
## Columns: 5
## $ date <date> 1954-01-01, 1954-01-01, 1954-02-01, 1954-02-01, 1954-03-01, 195…
## $ Time <dbl> 1954.01, 1954.01, 1954.02, 1954.02, 1954.03, 1954.03, 1954.04, 1…
## $ Var <chr> "gulf stream index", "western gulf stream index", "gulf stream i…
## $ Value <dbl> 1.6811664, 0.6118636, 1.8233541, 0.6203325, 1.5028627, 0.7795198…
## $ EPU <chr> "All", "All", "All", "All", "All", "All", "All", "All", "All", "…
plot(x)
We can also plot from monthly and annual perspectives.
plot(x, by = "month")
plot(x, by = 'year')
Parfitt, Kwon, and Andres, 2022 proposed a Gulf Stream Gradient Index. Data is served for 2004-2019 here.
Parfitt, R., Y.-O. Kwon, and M. Andres, 2022: A monthly index for the large-scale sea surface temperature gradient across the separated Gulf Stream. Geophys. Res. Lett., 49, e2022GL100914. https://doi.org/10.1029/2022GL100914.
x = read_gsgi() |>
dplyr::glimpse()## Rows: 324
## Columns: 4
## $ date <date> 1993-01-01, 1993-02-01, 1993-03-01, 1993-04-01, 1993-0…
## $ SST.N.deseason <dbl> -0.73139479, 0.37673571, 0.55352506, 0.89345027, 0.2047…
## $ SST.S.deseason <dbl> -0.29115456, -0.34953310, -0.35755056, -0.29019442, -0.…
## $ dSST.deseason <dbl> -0.44024022, 0.72626881, 0.91107561, 1.18364469, 0.5361…
plot(x)
We also can plot from a climatology perspective.
plot(x, by = "month")
plot(x, by = "year")
Data from RAPID
Data from the RAPID AMOC monitoring project is funded by the Natural Environment Research Council and are freely available from www.rapid.ac.uk/rapidmoc.
Reference for Version v2020.2 >Moat B.I.; Frajka-Williams E., Smeed D.A.; Rayner D.; Johns W.E.; Baringer M.O.; Volkov, D.; Collins, J. (2022). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2020 (v2020.2), British Oceanographic Data Centre - Natural Environment Research Council, UK. doi:10.5285/e91b10af-6f0a-7fa7-e053-6c86abc05a09
x = read_moc_transports() |>
dplyr::glimpse()## Rows: 13,057
## Columns: 10
## $ date <date> 2004-01-02, 2004-01-02, 2004-01-03, 2004-01-03, 2004-01-…
## $ t_therm10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -16.71886, -16.94…
## $ t_aiw10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.8313935, 0.7999…
## $ t_ud10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -9.957221, -9.954…
## $ t_ld10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -3.133432, -3.305…
## $ t_bw10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.4894769, 1.4455…
## $ t_gs10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28.60981, 28.4917…
## $ t_ek10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -1.1396932, -0.55…
## $ t_umo10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -15.85125, -16.10…
## $ moc_mar_hc10 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 11.63748, 11.8528…
plot(x)## Warning: Removed 180 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_line()`).
RAPID-MOCHA provides a heat transport timeseries.
x = read_rapid_mocha() |>
dplyr::glimpse()## Rows: 12,202
## Columns: 19
## $ time <dttm> 2004-04-02 00:00:00, 2004-04-02 12:00:00, 2004-04-03 00:00…
## $ Q_eddy <dbl> 5.689694e+13, 5.508646e+13, 5.332703e+13, 5.167863e+13, 5.0…
## $ Q_ek <dbl> -1.455417e+14, -1.617927e+14, -1.778214e+14, -1.919228e+14,…
## $ Q_fc <dbl> 2.153860e+15, 2.179862e+15, 2.203375e+15, 2.223300e+15, 2.2…
## $ Q_gyre <dbl> 1.354565e+14, 1.345823e+14, 1.338101e+14, 1.331889e+14, 1.3…
## $ Q_int <dbl> -1.668320e+15, -1.665047e+15, -1.662643e+15, -1.662057e+15,…
## $ Q_mo <dbl> -1.414269e+15, -1.407616e+15, -1.400726e+15, -1.394223e+15,…
## $ Q_ot <dbl> 4.585924e+14, 4.758707e+14, 4.910170e+14, 5.039655e+14, 5.1…
## $ Q_sum <dbl> 5.940489e+14, 6.104530e+14, 6.248270e+14, 6.371544e+14, 6.4…
## $ Q_wedge <dbl> 1.971534e+14, 2.023443e+14, 2.085901e+14, 2.161555e+14, 2.2…
## $ T_fc_fwt <dbl> 19.11297, 19.11348, 19.11400, 19.11454, 19.11511, 19.11573,…
## $ trans_ek <dbl> -1.6293229, -1.8095506, -1.9872548, -2.1434514, -2.2586002,…
## $ trans_fc <dbl> 27.56607, 27.89806, 28.19822, 28.45247, 28.65009, 28.78458,…
## $ maxmoc <dbl> 9.153097, 9.584503, 9.978900, 10.325347, 10.617973, 10.8569…
## $ julian_day <dbl> 2453098, 2453098, 2453099, 2453100, 2453100, 2453100, 24531…
## $ year <dbl> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004,…
## $ month <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,…
## $ day <dbl> 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11,…
## $ hour <dbl> 0, 12, 0, 12, 0, 12, 0, 12, 0, 12, 0, 12, 0, 12, 0, 12, 0, …
plot(x)
plot(x, by = 'day')
plot(x, by = 'year')
We defined two bounding boxes in the North Atlantic - one for the persistent “cold blob” centered south of Iceland and another for the “warm spot” south of New England and Martime Canada. We extracted monthly ERSST data and computed monthly OISST sea surface temperature statistics for each.
# read the boxes but exclude the northern hemisphere record
bb = read_patch_bbs() |>
dplyr::filter(name != "nh")
bb## Simple feature collection with 2 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -74 ymin: 36 xmax: -15 ymax: 60
## Geodetic CRS: WGS 84
## # A tibble: 2 × 2
## name geom
## * <chr> <POLYGON [°]>
## 1 cold_blob ((-30 42, -15 42, -15 60, -30 60, -30 42))
## 2 warm_spot ((-74 36, -58 36, -58 42, -74 42, -74 36))
x = read_patch_month() |>
dplyr::glimpse()## Rows: 5,046
## Columns: 9
## $ date <date> 1854-01-01, 1854-02-01, 1854-03-01, 1854-04-01, 1854-05-01, 18…
## $ region <chr> "cold_blob", "cold_blob", "cold_blob", "cold_blob", "cold_blob"…
## $ source <chr> "ersst", "ersst", "ersst", "ersst", "ersst", "ersst", "ersst", …
## $ min <dbl> 8.886023, 9.316138, 8.727843, 8.101231, 8.113076, 7.979538, 8.8…
## $ q25 <dbl> 10.321997, 10.784625, 10.092959, 9.832815, 10.034244, 10.801755…
## $ median <dbl> 11.16081, 11.27052, 10.82493, 11.25506, 11.79660, 13.18124, 15.…
## $ mean <dbl> 11.39416, 11.53059, 11.10309, 11.34614, 11.86744, 13.19618, 15.…
## $ q75 <dbl> 12.43899, 12.25503, 11.89755, 12.79622, 13.57947, 15.63272, 18.…
## $ max <dbl> 14.99075, 14.55934, 14.96488, 15.87475, 17.07551, 19.63632, 22.…
plot_patch_location(bb)
plot(x)
NOAA’s Ocean Prediction Center provides a
FTP server](https://ftp.opc.ncep.noaa.gov/grids/experimental/GStream)
for downloads by year. We have downloaded these and repackaged into
spatial format files - these are included with the gstream package.
They also provide daily
updates.
x = read_usn(what = "orig") |>
dplyr::glimpse()## Rows: 3,905
## Columns: 3
## $ date <date> 2010-01-22, 2010-01-22, 2010-01-25, 2010-01-25, 2010-01-27, 2010…
## $ wall <chr> "north", "south", "north", "south", "north", "south", "north", "s…
## $ geom <MULTIPOINT [°]> MULTIPOINT ((-80.2 25), (-8..., MULTIPOINT ((-77.5 31.…
This reads in all of the data stored with the package. We can then do a simple plot of all of the locations.
bb = sf::st_bbox(x)
coast = rnaturalearth::ne_coastline(scale = "medium", returnclass = "sf")
plot(x['wall'], pch = ".", axes = TRUE, reset = FALSE)
plot(sf::st_geometry(coast), add = TRUE)
Note that you don’t need to create the configuration file if you are not downloading data.
The daily data is hosted by by NOAA’s Ocean Prediction
Center In particular they post the US
Navy’s daily Gulf Stream point
data for the north
and south walls. These can be downloaded. We provide a mechanism for
storing the URL of the daily data, the path to where you want to store
the downloads and a simple script for downloading. The configuration can
be stored anywhere, but by default we look for it isn ~/.gstream.
cfg = read_configuration()
cfg## $path
## [1] "/mnt/s1/projects/ecocast/coredata/gstream"
##
## $usn
## $usn$datapath
## [1] "/mnt/s1/projects/ecocast/coredata/gstream/usn"
##
## $usn$rawpath
## [1] "/mnt/s1/projects/ecocast/coredata/gstream/usn/raw"
##
## $usn$dailyuri
## [1] "https://ocean.weather.gov/gulf_stream_latest.txt"
##
## $usn$ftpuri
## [1] "https://ftp.opc.ncep.noaa.gov/grids/experimental/GStream"
Obviously, you will want to modify the rawpath to suit your own needs.
We then set up a cron job to make the daily download at local 6pm.
# gstream data
0 18 * * * /usr/local/bin/Rscript /Users/ben/Library/CloudStorage/Dropbox/code/projects/gsi/inst/scripts/usn_daily_download.R >> /dev/null 2>&1
The USN data is not ordered, that is the points for a given day are not following a polyline.
d = dplyr::filter(x, date == as.Date("2020-12-19"), wall == "north")
plot(sf::st_geometry(d), type = "l", axes = TRUE)
With thanks to Dewey
Dunnington
we can reorder them into a single LINESTRING.
d = dplyr::filter(x, date == as.Date("2020-01-03"), wall == "north")
do = order_usn(d)
plot(sf::st_geometry(d), type = "l", axes = TRUE, reset= FALSE)
plot(sf::st_geometry(do), type = "l", add = TRUE, col = "orange")