The explosion on the Deepwater Horizon (DWH) tragically killed 11 people, and resulted in one of the largest marine oil spills in history. One of the first questions when there is such a tragedy is: where will the oil go?
In order the help answer that question one can use Near real time currents from the HF-Radar sites near the incident.
First let's start with the HF-Radar DAC, where one can browser the all available data interactively. Below we show an IFrame with the area near DWH for the 27 of July of 2017.
In this notebook we will demonstrate how to obtain such data programmatically.
(For more information on the DWH see http://response.restoration.noaa.gov/oil-and-chemical-spills/significant-incidents/deepwater-horizon-oil-spill.)
In [1]:
from IPython.display import HTML
url = (
"https://cordc.ucsd.edu/projects/mapping/maps/fullpage.php?"
"ll=29.061888,-87.373643&"
"zm=7&"
"mt=&"
"rng=0.00,50.00&"
"us=1&"
"cs=4&"
"res=6km_h&"
"ol=3&"
"cp=1"
)
iframe = (
'<iframe src="{src}" width="750" height="450" style="border:none;"></iframe>'.format
)
HTML(iframe(src=url))
Out[1]:
The interactive interface is handy for exploration but we usually need to download "mechanically" in order to use them in our analysis, plots, or for downloading time-series.
One way to achieve that is to use an OPeNDAP client, here Python's xarray, and explore the endpoint directly.
(We'll use the same 6 km resolution from the IFrame above.)
In [2]:
import xarray as xr
url = (
"http://hfrnet-tds.ucsd.edu/thredds/dodsC/HFR/USEGC/6km/hourly/RTV/"
"HFRADAR_US_East_and_Gulf_Coast_6km_Resolution_Hourly_RTV_best.ncd"
)
ds = xr.open_dataset(url)
ds
Out[2]:
How about extracting a week time-series from the dataset averaged around the area of interest?
In [3]:
dx = dy = 2.25 # Area around the point of interest.
center = -87.373643, 29.061888 # Point of interest.
dsw = ds.sel(time=slice("2017-07-20", "2017-07-27"))
In [4]:
dsw = dsw.sel(
lon=(dsw.lon < center[0] + dx) & (dsw.lon > center[0] - dx),
lat=(dsw.lat < center[1] + dy) & (dsw.lat > center[1] - dy),
)
With xarray we can average hourly (resample) the whole dataset with one method call.
In [5]:
resampled = dsw.resample(indexer={"time": "1H"})
avg = resampled.mean(dim="time")
Now all we have to do is mask the missing data with NaNs and average over the area.
In [6]:
import numpy.ma as ma
v = avg["v"].data
u = avg["u"].data
time = avg["time"].to_index().to_pydatetime()
u = ma.masked_invalid(u)
v = ma.masked_invalid(v)
In [7]:
i, j, k = u.shape
u = u.reshape(i, j * k).mean(axis=1)
v = v.reshape(i, j * k).mean(axis=1)
In [8]:
%matplotlib inline
import matplotlib.pyplot as plt
from oceans.plotting import stick_plot
fig, ax = plt.subplots(figsize=(11, 2.75))
q = stick_plot(time, u, v, ax=ax)
ref = 0.5
qk = plt.quiverkey(
q,
0.1,
0.85,
ref,
"{} {}".format(ref, ds["u"].units),
labelpos="N",
coordinates="axes",
)
_ = plt.xticks(rotation=70)
To close this post let's us reproduce the HF radar DAC image from above but using yesterday's data.
In [9]:
from datetime import date, timedelta
yesterday = date.today() - timedelta(days=1)
dsy = ds.sel(time=yesterday)
Now that we singled out the date and and time we want the data, we trigger the download by accessing the data with xarray's .data property.
In [10]:
u = dsy["u"].data
v = dsy["v"].data
lon = dsy.coords["lon"].data
lat = dsy.coords["lat"].data
time = dsy.coords["time"].data
The cell below computes the speed from the velocity. We can use the speed computation to color code the vectors. Note that we re-create the vector velocity preserving the direction but using intensity of 1. (The same visualization technique used in the HF radar DAC.)
In [11]:
import numpy as np
from oceans.ocfis import spdir2uv, uv2spdir
angle, speed = uv2spdir(u, v)
us, vs = spdir2uv(np.ones_like(speed), angle, deg=True)
Now we can create a matplotlib figure displaying the data.
In [12]:
import cartopy.crs as ccrs
from cartopy import feature
from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
LAND = feature.NaturalEarthFeature(
"physical", "land", "10m", edgecolor="face", facecolor="lightgray"
)
sub = 2
bbox = lon.min(), lon.max(), lat.min(), lat.max()
fig, ax = plt.subplots(figsize=(9, 9), subplot_kw=dict(projection=ccrs.PlateCarree()))
ax.set_extent([center[0] - dx - dx, center[0] + dx, center[1] - dy, center[1] + dy])
vmin, vmax = np.nanmin(speed[::sub, ::sub]), np.nanmax(speed[::sub, ::sub])
speed_clipped = np.clip(speed[::sub, ::sub], 0, 0.65)
ax.quiver(
lon[::sub], lat[::sub], us[::sub, ::sub], vs[::sub, ::sub], speed_clipped, scale=30,
)
# Deepwater Horizon site.
ax.plot(-88.365997, 28.736628, marker="o", color="crimson")
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
feature = ax.add_feature(LAND, zorder=0, edgecolor="black")