This notebook aims to demonstrate how to create a simple interactive GIS map with the National Hurricane Center predictions [1] and the observed sea surface height from CO-OPS [2].
See https://tidesandcurrents.noaa.gov/quicklook/view.html?name=IRMA for the latest information on Irma.
First we have to download the National Hurricane Center (NHC) GIS 5 day predictions data for Irma.
NHC codes storms are coded with 8 letter names:
al → Atlantic11 is Irma2017Browse http://www.nhc.noaa.gov/gis/archive_wsurge.php?year=2017 to find other hurricanes code.
In [1]:
code = "al112017"
hurricane = "{}_5day".format(code)
In [2]:
import os
import sys
import lxml.html
try:
from urllib.request import urlopen, urlretrieve
except Exception:
from urllib import urlopen, urlretrieve
def url_lister(url):
urls = []
connection = urlopen(url)
dom = lxml.html.fromstring(connection.read())
for link in dom.xpath("//a/@href"):
urls.append(link)
return urls
def download(url, path):
sys.stdout.write(fname + "\n")
if not os.path.isfile(path):
urlretrieve(url, filename=path, reporthook=progress_hook(sys.stdout))
sys.stdout.write("\n")
sys.stdout.flush()
def progress_hook(out):
"""
Return a progress hook function, suitable for passing to
urllib.retrieve, that writes to the file object *out*.
"""
def it(n, bs, ts):
got = n * bs
if ts < 0:
outof = ""
else:
# On the last block n*bs can exceed ts, so we clamp it
# to avoid awkward questions.
got = min(got, ts)
outof = "/%d [%d%%]" % (ts, 100 * got // ts)
out.write("\r %d%s" % (got, outof))
out.flush()
return it
In [3]:
nhc = "http://www.nhc.noaa.gov/gis/forecast/archive/"
# We don't need the latest file b/c that is redundant to the latest number.
fnames = [
fname
for fname in url_lister(nhc)
if fname.startswith(hurricane) and "latest" not in fname
]
In [4]:
base = os.path.abspath(os.path.join(os.path.curdir, "data", hurricane))
if not os.path.exists(base):
os.makedirs(base)
for fname in fnames:
url = "{}/{}".format(nhc, fname)
path = os.path.join(base, fname)
download(url, path)
al112017_5day_001.zip
al112017_5day_002.zip
al112017_5day_003.zip
al112017_5day_004.zip
al112017_5day_005.zip
al112017_5day_006.zip
al112017_5day_007.zip
al112017_5day_008.zip
al112017_5day_009.zip
al112017_5day_010.zip
al112017_5day_011.zip
al112017_5day_012.zip
al112017_5day_013.zip
al112017_5day_014.zip
al112017_5day_015.zip
al112017_5day_016.zip
al112017_5day_017.zip
al112017_5day_018.zip
al112017_5day_018A.zip
al112017_5day_019.zip
al112017_5day_019A.zip
al112017_5day_020.zip
al112017_5day_020A.zip
al112017_5day_021.zip
al112017_5day_021A.zip
al112017_5day_022.zip
al112017_5day_022A.zip
al112017_5day_023.zip
al112017_5day_023A.zip
al112017_5day_024.zip
al112017_5day_025.zip
al112017_5day_026.zip
al112017_5day_026A.zip
al112017_5day_027.zip
al112017_5day_027A.zip
al112017_5day_028.zip
al112017_5day_028A.zip
al112017_5day_029.zip
al112017_5day_029A.zip
al112017_5day_030.zip
al112017_5day_030A.zip
al112017_5day_031.zip
al112017_5day_031A.zip
al112017_5day_032.zip
al112017_5day_032A.zip
al112017_5day_033.zip
al112017_5day_033A.zip
al112017_5day_034.zip
al112017_5day_034A.zip
al112017_5day_035.zip
al112017_5day_035A.zip
al112017_5day_036.zip
al112017_5day_036A.zip
al112017_5day_037.zip
al112017_5day_037A.zip
al112017_5day_038.zip
al112017_5day_038A.zip
al112017_5day_039.zip
al112017_5day_039A.zip
al112017_5day_040.zip
al112017_5day_040A.zip
al112017_5day_041.zip
al112017_5day_041A.zip
al112017_5day_042.zip
al112017_5day_042A.zip
al112017_5day_043.zip
al112017_5day_043A.zip
al112017_5day_044.zip
al112017_5day_044A.zip
al112017_5day_045.zip
al112017_5day_045A.zip
al112017_5day_046.zip
al112017_5day_046A.zip
al112017_5day_047.zip
al112017_5day_047A.zip
al112017_5day_048.zip
al112017_5day_048A.zip
al112017_5day_049.zip
al112017_5day_049A.zip
al112017_5day_050.zip
al112017_5day_050A.zip
al112017_5day_051.zip
al112017_5day_051A.zip
al112017_5day_052.zip
In the cells below we use geopandas to load the data we just downloaded. We will use only the prediction cone (png) and the track points (pts), but feel free to explore this data further, there is plenty more there.
In [5]:
import os
os.environ["CPL_ZIP_ENCODING"] = "UTF-8"
os.environ["TZ"] = "GMT0"
In [6]:
from glob import glob
import geopandas
cones, points = [], []
for fname in sorted(glob(os.path.join(base, "{}_*.zip".format(hurricane)))):
number = os.path.splitext(os.path.split(fname)[-1])[0].split("_")[-1]
pgn = geopandas.read_file(
"/{}-{}_5day_pgn.shp".format(code, number), vfs="zip://{}".format(fname)
)
cones.append(pgn)
pts = geopandas.read_file(
"/{}-{}_5day_pts.shp".format(code, number), vfs="zip://{}".format(fname)
)
# Only the first "obsevartion."
points.append(pts.iloc[0])
Let's create a color code for the point track.
In [7]:
colors = {
"Subtropical Depression": "yellow",
"Tropical Depression": "yellow",
"Tropical Storm": "orange",
"Subtropical Storm": "orange",
"Hurricane": "red",
"Major Hurricane": "crimson",
}
Now we can get all the information we need from those GIS files. Let's start with the event dates.
In [8]:
import dateutil
start = points[0]["FLDATELBL"].strip(" AST")
end = points[-1]["FLDATELBL"].strip(" EDT")
start = dateutil.parser.parse(start)
end = dateutil.parser.parse(end)
And the bounding box to search the data.
In [9]:
from shapely.geometry import LineString
from shapely.ops import cascaded_union
last_cone = cones[-1]["geometry"].iloc[0]
track = LineString([point["geometry"] for point in points])
polygon = cascaded_union([last_cone, track])
# Add a buffer to find the stations along the track.
bbox = polygon.buffer(2).bounds
Note that the bounding box is derived from the track and the latest prediction cone.
In [10]:
strbbox = ", ".join(format(v, ".2f") for v in bbox)
print("bbox: {}\nstart: {}\n end: {}".format(strbbox, start, end))
bbox: -91.91, 14.40, -28.30, 39.45
start: 2017-08-30 08:00:00
end: 2017-09-11 20:00:00
Now we need to build a filter with those parameters to find the observations along the Hurricane path. We still need to specify:
Next, we can use pyoos to assemble a collector to download the data into a pandas DataFrame.
In [11]:
import cf_units
import pandas as pd
from ioos_tools.ioos import collector2table
from pyoos.collectors.coops.coops_sos import CoopsSos
from retrying import retry
# We need to retry in case of failure b/c the server cannot handle
# the high traffic during events like Irma.
@retry(stop_max_attempt_number=5, wait_fixed=3000)
def get_coops(start, end, sos_name, units, bbox, verbose=False):
collector = CoopsSos()
collector.set_bbox(bbox)
collector.end_time = end
collector.start_time = start
collector.variables = [sos_name]
ofrs = collector.server.offerings
title = collector.server.identification.title
config = dict(units=units, sos_name=sos_name,)
data = collector2table(
collector=collector,
config=config,
col="{} ({})".format(sos_name, units.format(cf_units.UT_ISO_8859_1)),
)
# Clean the table.
table = dict(
station_name=[s._metadata.get("station_name") for s in data],
station_code=[s._metadata.get("station_code") for s in data],
sensor=[s._metadata.get("sensor") for s in data],
lon=[s._metadata.get("lon") for s in data],
lat=[s._metadata.get("lat") for s in data],
depth=[s._metadata.get("depth", "NA") for s in data],
)
table = pd.DataFrame(table).set_index("station_name")
if verbose:
print("Collector offerings")
print("{}: {} offerings".format(title, len(ofrs)))
return data, table
In [12]:
ssh, ssh_table = get_coops(
start=start,
end=end,
sos_name="water_surface_height_above_reference_datum",
units=cf_units.Unit("meters"),
bbox=bbox,
)
ssh_table
Out[12]:
station_code
sensor
lon
lat
depth
station_name
Bermuda Biological Station
2695535
urn:ioos:sensor:NOAA.NOS.CO-OPS:2695535:N1
-64.6950
32.3700
None
Bermuda, St. Georges Island, Bermuda
2695540
urn:ioos:sensor:NOAA.NOS.CO-OPS:2695540:Y1
-64.7033
32.3733
None
Atlantic City, NJ
8534720
urn:ioos:sensor:NOAA.NOS.CO-OPS:8534720:A1
-74.4181
39.3567
None
Cape May, NJ
8536110
urn:ioos:sensor:NOAA.NOS.CO-OPS:8536110:A1
-74.9597
38.9678
None
Ship John Shoal, NJ
8537121
urn:ioos:sensor:NOAA.NOS.CO-OPS:8537121:A1
-75.3750
39.3050
None
Brandywine Shoal Light, DE
8555889
urn:ioos:sensor:NOAA.NOS.CO-OPS:8555889:Y1
-75.1130
38.9870
None
Lewes, DE
8557380
urn:ioos:sensor:NOAA.NOS.CO-OPS:8557380:A1
-75.1192
38.7828
None
Ocean City Inlet, MD
8570283
urn:ioos:sensor:NOAA.NOS.CO-OPS:8570283:A1
-75.0911
38.3283
None
Bishops Head, MD
8571421
urn:ioos:sensor:NOAA.NOS.CO-OPS:8571421:A1
-76.0387
38.2204
None
Cambridge, MD
8571892
urn:ioos:sensor:NOAA.NOS.CO-OPS:8571892:Y1
-76.0617
38.5725
None
Tolchester Beach, MD
8573364
urn:ioos:sensor:NOAA.NOS.CO-OPS:8573364:A1
-76.2450
39.2133
None
Baltimore, Fort McHenry, Patapsco River, MD
8574680
urn:ioos:sensor:NOAA.NOS.CO-OPS:8574680:A1
-76.5794
39.2669
None
Annapolis, MD
8575512
urn:ioos:sensor:NOAA.NOS.CO-OPS:8575512:A1
-76.4816
38.9833
None
Solomons Island, MD
8577330
urn:ioos:sensor:NOAA.NOS.CO-OPS:8577330:Y1
-76.4508
38.3172
None
Washington, DC
8594900
urn:ioos:sensor:NOAA.NOS.CO-OPS:8594900:A1
-77.0217
38.8730
None
Wachapreague, VA
8631044
urn:ioos:sensor:NOAA.NOS.CO-OPS:8631044:Y1
-75.6858
37.6078
None
Kiptopeke, VA
8632200
urn:ioos:sensor:NOAA.NOS.CO-OPS:8632200:A1
-75.9884
37.1652
None
Dahlgren, VA
8635027
urn:ioos:sensor:NOAA.NOS.CO-OPS:8635027:Y1
-77.0366
38.3197
None
Lewisetta, VA
8635750
urn:ioos:sensor:NOAA.NOS.CO-OPS:8635750:A1
-76.4647
37.9953
None
Windmill Point, VA
8636580
urn:ioos:sensor:NOAA.NOS.CO-OPS:8636580:Y1
-76.2900
37.6161
None
Yorktown USCG Training Center, VA
8637689
urn:ioos:sensor:NOAA.NOS.CO-OPS:8637689:A1
-76.4788
37.2265
None
Sewells Point, VA
8638610
urn:ioos:sensor:NOAA.NOS.CO-OPS:8638610:A1
-76.3300
36.9467
None
CBBT, Chesapeake Channel, VA
8638901
urn:ioos:sensor:NOAA.NOS.CO-OPS:8638901:Y1
-76.0833
37.0329
None
Money Point, VA
8639348
urn:ioos:sensor:NOAA.NOS.CO-OPS:8639348:Y1
-76.3017
36.7783
None
Duck, NC
8651370
urn:ioos:sensor:NOAA.NOS.CO-OPS:8651370:A1
-75.7467
36.1833
None
Oregon Inlet Marina, NC
8652587
urn:ioos:sensor:NOAA.NOS.CO-OPS:8652587:A1
-75.5481
35.7950
None
USCG Station Hatteras, NC
8654467
urn:ioos:sensor:NOAA.NOS.CO-OPS:8654467:A1
-75.7042
35.2086
None
Beaufort, Duke Marine Lab, NC
8656483
urn:ioos:sensor:NOAA.NOS.CO-OPS:8656483:Y1
-76.6700
34.7200
None
Wilmington, NC
8658120
urn:ioos:sensor:NOAA.NOS.CO-OPS:8658120:A1
-77.9536
34.2275
None
Wrightsville Beach, NC
8658163
urn:ioos:sensor:NOAA.NOS.CO-OPS:8658163:A1
-77.7867
34.2133
None
...
...
...
...
...
...
West Fowl River Bridge, AL
8738043
urn:ioos:sensor:NOAA.NOS.CO-OPS:8738043:Y1
-88.1586
30.3766
None
Bayou La Batre Bridge, AL
8739803
urn:ioos:sensor:NOAA.NOS.CO-OPS:8739803:Y1
-88.2478
30.4062
None
Pascagoula NOAA Lab, MS
8741533
urn:ioos:sensor:NOAA.NOS.CO-OPS:8741533:A1
-88.5631
30.3678
None
Bay Waveland Yacht Club, MS
8747437
urn:ioos:sensor:NOAA.NOS.CO-OPS:8747437:A1
-89.3250
30.3250
None
Pilottown, LA
8760721
urn:ioos:sensor:NOAA.NOS.CO-OPS:8760721:A1
-89.2583
29.1783
None
Pilots Station East, S.W. Pass, LA
8760922
urn:ioos:sensor:NOAA.NOS.CO-OPS:8760922:Y1
-89.4075
28.9322
None
Shell Beach, LA
8761305
urn:ioos:sensor:NOAA.NOS.CO-OPS:8761305:A1
-89.6730
29.8683
None
Grand Isle, LA
8761724
urn:ioos:sensor:NOAA.NOS.CO-OPS:8761724:A1
-89.9567
29.2633
None
New Canal Station, LA
8761927
urn:ioos:sensor:NOAA.NOS.CO-OPS:8761927:A1
-90.1133
30.0272
None
Carrollton, LA
8761955
urn:ioos:sensor:NOAA.NOS.CO-OPS:8761955:A1
-90.1355
29.9329
None
Port Fourchon, Belle Pass, LA
8762075
urn:ioos:sensor:NOAA.NOS.CO-OPS:8762075:A1
-90.1993
29.1142
None
West Bank 1, Bayou Gauche, LA
8762482
urn:ioos:sensor:NOAA.NOS.CO-OPS:8762482:Y1
-90.4203
29.7886
None
I-10 Bonnet Carre Floodway, LA
8762483
urn:ioos:sensor:NOAA.NOS.CO-OPS:8762483:Y1
-90.3900
30.0679
None
Berwick, Atchafalaya River, LA
8764044
urn:ioos:sensor:NOAA.NOS.CO-OPS:8764044:A1
-91.2376
29.6675
None
LAWMA, Amerada Pass, LA
8764227
urn:ioos:sensor:NOAA.NOS.CO-OPS:8764227:Y1
-91.3381
29.4496
None
Eugene Island, North of , Gulf of Mexico, LA
8764314
urn:ioos:sensor:NOAA.NOS.CO-OPS:8764314:Y1
-91.3839
29.3675
None
Christiansted Harbor, St Croix, VI
9751364
urn:ioos:sensor:NOAA.NOS.CO-OPS:9751364:A1
-64.6984
17.7477
None
Lameshur Bay, St John, VI
9751381
urn:ioos:sensor:NOAA.NOS.CO-OPS:9751381:A1
-64.7242
18.3182
None
Lime Tree Bay, VI
9751401
urn:ioos:sensor:NOAA.NOS.CO-OPS:9751401:Y1
-64.7538
17.6947
None
Charlotte Amalie, VI
9751639
urn:ioos:sensor:NOAA.NOS.CO-OPS:9751639:Y1
-64.9258
18.3472
None
Culebra, PR
9752235
urn:ioos:sensor:NOAA.NOS.CO-OPS:9752235:NT
-65.3025
18.3009
None
Isabel Segunda, Vieques Island, PR
9752619
urn:ioos:sensor:NOAA.NOS.CO-OPS:9752619:A1
-65.4439
18.1525
None
Esperanza, Vieques Island, PR
9752695
urn:ioos:sensor:NOAA.NOS.CO-OPS:9752695:A1
-65.4714
18.0939
None
Yabucoa Harbor, PR
9754228
urn:ioos:sensor:NOAA.NOS.CO-OPS:9754228:A1
-65.8330
18.0551
None
San Juan, La Puntilla, San Juan Bay, PR
9755371
urn:ioos:sensor:NOAA.NOS.CO-OPS:9755371:Y1
-66.1164
18.4592
None
Arecibo, PR
9757809
urn:ioos:sensor:NOAA.NOS.CO-OPS:9757809:A1
-66.7024
18.4805
None
Magueyes Island, PR
9759110
urn:ioos:sensor:NOAA.NOS.CO-OPS:9759110:A1
-67.0464
17.9701
None
Mayaguez, PR
9759394
urn:ioos:sensor:NOAA.NOS.CO-OPS:9759394:Y1
-67.1625
18.2190
None
Mona Island, PR
9759938
urn:ioos:sensor:NOAA.NOS.CO-OPS:9759938:N1
-67.9385
18.0899
None
Barbuda, Antigua and Barbuda
9761115
urn:ioos:sensor:NOAA.NOS.CO-OPS:9761115:N1
-61.8206
17.5908
None
93 rows × 5 columns
In [13]:
wind_speed, wind_speed_table = get_coops(
start=start, end=end, sos_name="wind_speed", units=cf_units.Unit("m/s"), bbox=bbox,
)
wind_speed_table
Out[13]:
station_code
sensor
lon
lat
depth
station_name
Bermuda, St. Georges Island, Bermuda
2695540
urn:ioos:sensor:NOAA.NOS.CO-OPS:2695540:C1
-64.7033
32.3734
None
Cape May, NJ
8536110
urn:ioos:sensor:NOAA.NOS.CO-OPS:8536110:C1
-74.9597
38.9678
None
Ship John Shoal, NJ
8537121
urn:ioos:sensor:NOAA.NOS.CO-OPS:8537121:C1
-75.3750
39.3050
None
Brandywine Shoal Light, DE
8555889
urn:ioos:sensor:NOAA.NOS.CO-OPS:8555889:C1
-75.1133
38.9867
None
Lewes, DE
8557380
urn:ioos:sensor:NOAA.NOS.CO-OPS:8557380:C1
-75.1192
38.7828
None
Ocean City Inlet, MD
8570283
urn:ioos:sensor:NOAA.NOS.CO-OPS:8570283:C1
-75.0917
38.3283
None
Bishops Head, MD
8571421
urn:ioos:sensor:NOAA.NOS.CO-OPS:8571421:C1
-76.0387
38.2204
None
Tolchester Beach, MD
8573364
urn:ioos:sensor:NOAA.NOS.CO-OPS:8573364:C1
-76.2450
39.2133
None
Baltimore, Fort McHenry, Patapsco River, MD
8574680
urn:ioos:sensor:NOAA.NOS.CO-OPS:8574680:C1
-76.5783
39.2667
None
Francis Scott Key Bridge, MD
8574728
urn:ioos:sensor:NOAA.NOS.CO-OPS:8574728:C1
-76.5283
39.2200
None
Cove Point LNG Pier, MD
8577018
urn:ioos:sensor:NOAA.NOS.CO-OPS:8577018:C1
-76.3855
38.4044
None
Solomons Island, MD
8577330
urn:ioos:sensor:NOAA.NOS.CO-OPS:8577330:C1
-76.4508
38.3172
None
Piney Point, MD
8578240
urn:ioos:sensor:NOAA.NOS.CO-OPS:8578240:C1
-76.5333
38.1333
None
Washington, DC
8594900
urn:ioos:sensor:NOAA.NOS.CO-OPS:8594900:C1
-77.0217
38.8733
None
Wachapreague, VA
8631044
urn:ioos:sensor:NOAA.NOS.CO-OPS:8631044:C1
-75.6858
37.6078
None
Kiptopeke, VA
8632200
urn:ioos:sensor:NOAA.NOS.CO-OPS:8632200:C1
-75.9884
37.1652
None
Rappahannock Light, VA
8632837
urn:ioos:sensor:NOAA.NOS.CO-OPS:8632837:C1
-76.0150
37.5383
None
Dahlgren, VA
8635027
urn:ioos:sensor:NOAA.NOS.CO-OPS:8635027:C1
-77.0366
38.3197
None
Lewisetta, VA
8635750
urn:ioos:sensor:NOAA.NOS.CO-OPS:8635750:C1
-76.4646
37.9954
None
York River East Rear Range Light, VA
8637611
urn:ioos:sensor:NOAA.NOS.CO-OPS:8637611:C1
-76.3333
37.2500
None
Yorktown USCG Training Center, VA
8637689
urn:ioos:sensor:NOAA.NOS.CO-OPS:8637689:C1
-76.4788
37.2265
None
Dominion Terminal Associates, VA
8638511
urn:ioos:sensor:NOAA.NOS.CO-OPS:8638511:C1
-76.4242
36.9623
None
South Craney Island, VA
8638595
urn:ioos:sensor:NOAA.NOS.CO-OPS:8638595:C1
-76.3383
36.8883
None
Willoughby Degaussing Station, VA
8638614
urn:ioos:sensor:NOAA.NOS.CO-OPS:8638614:C1
-76.3217
36.9817
None
urn:ioos:station:NOAA.NOS.CO-OPS:8638863
8638863
urn:ioos:sensor:NOAA.NOS.CO-OPS:8638863:C1
-76.1133
36.9667
None
CBBT, Chesapeake Channel, VA
8638901
urn:ioos:sensor:NOAA.NOS.CO-OPS:8638901:C1
-76.0833
37.0329
None
Cape Henry, VA
8638999
urn:ioos:sensor:NOAA.NOS.CO-OPS:8638999:C1
-76.0067
36.9300
None
Money Point, VA
8639348
urn:ioos:sensor:NOAA.NOS.CO-OPS:8639348:C1
-76.3017
36.7783
None
Duck, NC
8651370
urn:ioos:sensor:NOAA.NOS.CO-OPS:8651370:C1
-75.7467
36.1833
None
Oregon Inlet Marina, NC
8652587
urn:ioos:sensor:NOAA.NOS.CO-OPS:8652587:C1
-75.5481
35.7950
None
...
...
...
...
...
...
Panama City, FL
8729108
urn:ioos:sensor:NOAA.NOS.CO-OPS:8729108:C1
-85.6669
30.1523
None
Panama City Beach, FL
8729210
urn:ioos:sensor:NOAA.NOS.CO-OPS:8729210:C1
-85.8783
30.2133
None
Fort Morgan, AL
8734673
urn:ioos:sensor:NOAA.NOS.CO-OPS:8734673:C1
-88.0250
30.2283
None
Dauphin Island, AL
8735180
urn:ioos:sensor:NOAA.NOS.CO-OPS:8735180:C1
-88.0750
30.2500
None
Coast Guard Sector Mobile, AL
8736897
urn:ioos:sensor:NOAA.NOS.CO-OPS:8736897:C1
-88.0583
30.6483
None
Petit Bois Island, Port of Pascagoula, MS
8741003
urn:ioos:sensor:NOAA.NOS.CO-OPS:8741003:C1
-88.5000
30.2133
None
urn:ioos:station:NOAA.NOS.CO-OPS:8741094
8741094
urn:ioos:sensor:NOAA.NOS.CO-OPS:8741094:C1
-88.5117
30.3433
None
urn:ioos:station:NOAA.NOS.CO-OPS:8741501
8741501
urn:ioos:sensor:NOAA.NOS.CO-OPS:8741501:C1
-88.5667
30.3550
None
Bay Waveland Yacht Club, MS
8747437
urn:ioos:sensor:NOAA.NOS.CO-OPS:8747437:C1
-89.3250
30.3250
None
Pilottown, LA
8760721
urn:ioos:sensor:NOAA.NOS.CO-OPS:8760721:C1
-89.2583
29.1783
None
Pilots Station East, S.W. Pass, LA
8760922
urn:ioos:sensor:NOAA.NOS.CO-OPS:8760922:C1
-89.4075
28.9322
None
Shell Beach, LA
8761305
urn:ioos:sensor:NOAA.NOS.CO-OPS:8761305:C1
-89.6732
29.8681
None
Grand Isle, LA
8761724
urn:ioos:sensor:NOAA.NOS.CO-OPS:8761724:C1
-89.9567
29.2633
None
New Canal Station, LA
8761927
urn:ioos:sensor:NOAA.NOS.CO-OPS:8761927:C1
-90.1133
30.0272
None
West Bank 1, Bayou Gauche, LA
8762482
urn:ioos:sensor:NOAA.NOS.CO-OPS:8762482:C1
-90.4203
29.7886
None
Berwick, Atchafalaya River, LA
8764044
urn:ioos:sensor:NOAA.NOS.CO-OPS:8764044:C1
-91.2376
29.6675
None
LAWMA, Amerada Pass, LA
8764227
urn:ioos:sensor:NOAA.NOS.CO-OPS:8764227:C1
-91.3381
29.4496
None
Eugene Island, North of , Gulf of Mexico, LA
8764314
urn:ioos:sensor:NOAA.NOS.CO-OPS:8764314:C1
-91.3839
29.3675
None
Christiansted Harbor, St Croix, VI
9751364
urn:ioos:sensor:NOAA.NOS.CO-OPS:9751364:C1
-64.7050
17.7500
None
Lime Tree Bay, VI
9751401
urn:ioos:sensor:NOAA.NOS.CO-OPS:9751401:C1
-64.7538
17.6947
None
Charlotte Amalie, VI
9751639
urn:ioos:sensor:NOAA.NOS.CO-OPS:9751639:C1
-64.9200
18.3358
None
Isabel Segunda, Vieques Island, PR
9752619
urn:ioos:sensor:NOAA.NOS.CO-OPS:9752619:C1
-65.4438
18.1525
None
Esperanza, Vieques Island, PR
9752695
urn:ioos:sensor:NOAA.NOS.CO-OPS:9752695:C1
-65.4714
18.0939
None
Fajardo, PR
9753216
urn:ioos:sensor:NOAA.NOS.CO-OPS:9753216:C1
-65.6311
18.3352
None
Yabucoa Harbor, PR
9754228
urn:ioos:sensor:NOAA.NOS.CO-OPS:9754228:C1
-65.8330
18.0551
None
San Juan, La Puntilla, San Juan Bay, PR
9755371
urn:ioos:sensor:NOAA.NOS.CO-OPS:9755371:C1
-66.1164
18.4592
None
Arecibo, PR
9757809
urn:ioos:sensor:NOAA.NOS.CO-OPS:9757809:C1
-66.7024
18.4805
None
Magueyes Island, PR
9759110
urn:ioos:sensor:NOAA.NOS.CO-OPS:9759110:C1
-67.0464
17.9701
None
Mayaguez, PR
9759394
urn:ioos:sensor:NOAA.NOS.CO-OPS:9759394:C1
-67.1625
18.2189
None
Barbuda, Antigua and Barbuda
9761115
urn:ioos:sensor:NOAA.NOS.CO-OPS:9761115:C1
-61.8206
17.5908
None
85 rows × 5 columns
For simplicity we will use only the stations that have both wind speed and sea surface height and reject those that have only one or the other.
In [14]:
common = set(ssh_table["station_code"]).intersection(wind_speed_table["station_code"])
In [15]:
ssh_obs, win_obs = [], []
for station in common:
ssh_obs.extend([obs for obs in ssh if obs._metadata["station_code"] == station])
win_obs.extend(
[obs for obs in wind_speed if obs._metadata["station_code"] == station]
)
In [16]:
index = pd.date_range(start=start, end=end, freq="15min")
# Re-index and rename series.
ssh_observations = []
for series in ssh_obs:
_metadata = series._metadata
series.index = series.index.tz_localize(None)
obs = series.reindex(index=index, limit=1, method="nearest")
obs._metadata = _metadata
obs.name = _metadata["station_name"]
ssh_observations.append(obs)
winds_observations = []
for series in win_obs:
_metadata = series._metadata
series.index = series.index.tz_localize(None)
obs = series.reindex(index=index, limit=1, method="nearest")
obs._metadata = _metadata
obs.name = _metadata["station_name"]
winds_observations.append(obs)
Let's take a look at some stations to see if the data is OK. Below we have a station in Naples, FL along the Gulf of Mexico.
In [17]:
%matplotlib inline
import matplotlib.pyplot as plt
try:
station = "8725110"
w = [obs for obs in winds_observations if obs._metadata["station_code"] == station][
0
]
s = [obs for obs in ssh_observations if obs._metadata["station_code"] == station][0]
fig, ax = plt.subplots(figsize=(17, 3.75))
s["2017-9-10":].plot(ax=ax, label="Sea surface height (m)", color="#0000ff")
ax1 = w["2017-9-10":].plot(
ax=ax, label="Wind speed (m/s)", color="#9900cc", secondary_y=True
)
ax.set_title(w._metadata["station_name"])
lines = ax.get_lines() + ax.right_ax.get_lines()
ax.legend(lines, [l.get_label() for l in lines], loc="upper left")
ax.axhline(0, color="black")
ax.set_ylabel("Sea surface height (m)", color="#0000ff")
ax.right_ax.set_ylabel("Wind speed (m/s)", color="#9900cc")
ax1.annotate(
"Eye of the hurricane",
xy=(w["2017-9-10":].argmin().to_pydatetime(), w["2017-9-10":].min()),
xytext=(5, 10),
textcoords="offset points",
arrowprops=dict(arrowstyle="simple", facecolor="crimson"),
)
ax.grid(True)
except Exception:
print("Cannot find station {}".format(station))
/home/filipe/miniconda3/envs/IOOS/lib/python3.7/site-packages/ipykernel_launcher.py:23: FutureWarning:
The current behaviour of 'Series.argmin' is deprecated, use 'idxmin'
instead.
The behavior of 'argmin' will be corrected to return the positional
minimum in the future. For now, use 'series.values.argmin' or
'np.argmin(np.array(values))' to get the position of the minimum
row.
We can observe the sea level retreating around 10-Sep 9:00 and then a significant surge after 19:00. The lower winds at beginning of the surge is probably the eye of the hurricane.
For our interactive map we will use bokeh HTML plots instead of the usual raster matplotlib ones to enhance the user experience when exploring the graphs.
In [18]:
from bokeh.embed import file_html
from bokeh.models import HoverTool, LinearAxis, Range1d
from bokeh.plotting import figure
from bokeh.resources import CDN
from folium import IFrame
# Plot defaults.
tools = "pan,box_zoom,reset"
width, height = 750, 250
def make_plot(ssh, wind):
p = figure(
toolbar_location="above",
x_axis_type="datetime",
width=width,
height=height,
tools=tools,
title=ssh.name,
)
p.yaxis.axis_label = "wind speed (m/s)"
l0 = p.line(
x=wind.index,
y=wind.values,
line_width=5,
line_cap="round",
line_join="round",
legend="wind speed (m/s)",
color="#9900cc",
alpha=0.5,
)
p.extra_y_ranges = {}
p.extra_y_ranges["y2"] = Range1d(start=-1, end=3.5)
p.add_layout(LinearAxis(y_range_name="y2", axis_label="ssh (m)"), "right")
l1 = p.line(
x=ssh.index,
y=ssh.values,
line_width=5,
line_cap="round",
line_join="round",
legend="ssh (m)",
color="#0000ff",
alpha=0.5,
y_range_name="y2",
)
p.legend.location = "top_left"
p.add_tools(
HoverTool(tooltips=[("wind speed (m/s)", "@y"),], renderers=[l0],),
HoverTool(tooltips=[("ssh (m)", "@y"),], renderers=[l1],),
)
return p
def make_marker(p, location, fname):
html = file_html(p, CDN, fname)
iframe = IFrame(html, width=width + 45, height=height + 80)
popup = folium.Popup(iframe, max_width=2650)
icon = folium.Icon(color="green", icon="stats")
marker = folium.Marker(location=location, popup=popup, icon=icon)
return marker
Here is the final result. Explore the map by clicking on the map features plotted!
In [19]:
import folium
from folium.plugins import Fullscreen, MarkerCluster
from ioos_tools.ioos import get_coordinates
lon = track.centroid.x
lat = track.centroid.y
m = folium.Map(location=[lat, lon], tiles="OpenStreetMap", zoom_start=4)
Fullscreen(position="topright", force_separate_button=True).add_to(m)
marker_cluster0 = MarkerCluster(name="Observations")
marker_cluster1 = MarkerCluster(name="Past predictions")
marker_cluster0.add_to(m)
marker_cluster1.add_to(m)
url = "http://oos.soest.hawaii.edu/thredds/wms/hioos/satellite/dhw_5km"
w0 = folium.WmsTileLayer(
url,
name="Sea Surface Temperature",
fmt="image/png",
layers="CRW_SST",
attr="PacIOOS TDS",
overlay=True,
transparent=True,
)
w0.add_to(m)
url = "http://hfrnet.ucsd.edu/thredds/wms/HFRNet/USEGC/6km/hourly/RTV"
w1 = folium.WmsTileLayer(
url,
name="HF Radar",
fmt="image/png",
layers="surface_sea_water_velocity",
attr="HFRNet",
overlay=True,
transparent=True,
)
w1.add_to(m)
def style_function(feature):
return {
"fillOpacity": 0,
"color": "black",
"stroke": 1,
"weight": 0.5,
"opacity": 0.2,
}
# Latest cone prediction.
latest = cones[-1]
folium.GeoJson(
data=latest.__geo_interface__,
name="Cone prediction as of {}".format(latest["ADVDATE"].values[0]),
).add_to(m)
# Past cone predictions.
for cone in cones[:-1]:
folium.GeoJson(data=cone.__geo_interface__, style_function=style_function,).add_to(
marker_cluster1
)
# Latest points prediction.
for k, row in pts.iterrows():
date = row["FLDATELBL"]
hclass = row["TCDVLP"]
location = row["LAT"], row["LON"]
popup = "{}<br>{}".format(date, hclass)
folium.CircleMarker(
location=location, radius=10, fill=True, color=colors[hclass], popup=popup,
).add_to(m)
# All the points along the track.
for point in points:
date = point["FLDATELBL"]
hclass = point["TCDVLP"]
location = point["LAT"], point["LON"]
popup = "{}<br>{}".format(date, hclass)
folium.CircleMarker(
location=location, radius=5, fill=True, color=colors[hclass], popup=popup,
).add_to(m)
# Observations.
for ssh, wind in zip(ssh_observations, winds_observations):
fname = ssh._metadata["station_code"]
location = ssh._metadata["lat"], ssh._metadata["lon"]
p = make_plot(ssh, wind)
marker = make_marker(p, location=location, fname=fname)
marker.add_to(marker_cluster0)
folium.LayerControl().add_to(m)
p = folium.PolyLine(get_coordinates(bbox), color="#009933", weight=1, opacity=0.2)
p.add_to(m)
In [20]:
def embed_map(m):
from IPython.display import HTML
m.save("index.html")
with open("index.html") as f:
html = f.read()
iframe = '<iframe srcdoc="{srcdoc}" style="width: 100%; height: 750px; border: none"></iframe>'
srcdoc = html.replace('"', """)
return HTML(iframe.format(srcdoc=srcdoc))
embed_map(m)
/home/filipe/miniconda3/envs/IOOS/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning: Consider using IPython.display.IFrame instead
warnings.warn("Consider using IPython.display.IFrame instead")
Out[20]:
Tropical Depression`)[0];
popup_8f1646228e5f41e488db772ce943539a.setContent(html_5730f9da81444cd8aabd97931469f7c2);
circle_marker_c747563d5ae841eebf13657ccf62f0da.bindPopup(popup_8f1646228e5f41e488db772ce943539a)
;
var circle_marker_4e0e7e1662e24f5f92d0eb7667c164f6 = L.circleMarker(
[33.8, -86.4],
{"bubblingMouseEvents": true, "color": "yellow", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "yellow", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_cd9f68afa99f43ba860d68a5c8afdda0 = L.popup({"maxWidth": "100%"});
var html_ae6bbc0aedb4488eaf291d6106add089 = $(`2017-09-12 8:00 AM Tue EDT
Tropical Depression`)[0];
popup_cd9f68afa99f43ba860d68a5c8afdda0.setContent(html_ae6bbc0aedb4488eaf291d6106add089);
circle_marker_4e0e7e1662e24f5f92d0eb7667c164f6.bindPopup(popup_cd9f68afa99f43ba860d68a5c8afdda0)
;
var circle_marker_5a2a3b9c70e44ce2a85a84662d7388dc = L.circleMarker(
[35.2, -88.2],
{"bubblingMouseEvents": true, "color": "yellow", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "yellow", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_5f6a32749f874341829274bcbb09426f = L.popup({"maxWidth": "100%"});
var html_e8e1f5a11aab4c27a1d5517bbee5adbb = $(`2017-09-12 8:00 PM Tue EDT
Subtropical Depression`)[0];
popup_5f6a32749f874341829274bcbb09426f.setContent(html_e8e1f5a11aab4c27a1d5517bbee5adbb);
circle_marker_5a2a3b9c70e44ce2a85a84662d7388dc.bindPopup(popup_5f6a32749f874341829274bcbb09426f)
;
var circle_marker_42ca1eb163ca4d8d82bf6b5bbaa82f8e = L.circleMarker(
[36.4, -88.6],
{"bubblingMouseEvents": true, "color": "yellow", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "yellow", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_514b3f4b11a54980a1306632a82e3723 = L.popup({"maxWidth": "100%"});
var html_ad2e405558c4488a8dac558122ab3b89 = $(`2017-09-13 8:00 AM Wed EDT
Subtropical Depression`)[0];
popup_514b3f4b11a54980a1306632a82e3723.setContent(html_ad2e405558c4488a8dac558122ab3b89);
circle_marker_42ca1eb163ca4d8d82bf6b5bbaa82f8e.bindPopup(popup_514b3f4b11a54980a1306632a82e3723)
;
var circle_marker_f9449d2eced243f0b50771c8b1ab4f7a = L.circleMarker(
[16.4, -30.3],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_bb5685b96c1840258d9f810e28f35357 = L.popup({"maxWidth": "100%"});
var html_fe38125a081f4fbc97950da4036d3a9b = $(`2017-08-30 8:00 AM Wed AST
Tropical Storm`)[0];
popup_bb5685b96c1840258d9f810e28f35357.setContent(html_fe38125a081f4fbc97950da4036d3a9b);
circle_marker_f9449d2eced243f0b50771c8b1ab4f7a.bindPopup(popup_bb5685b96c1840258d9f810e28f35357)
;
var circle_marker_69b5a419b71142099210bd5914519db7 = L.circleMarker(
[16.4, -31.2],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_cb7d4aba1d9245609c8d57091bc8580e = L.popup({"maxWidth": "100%"});
var html_803b50e64e7d4544920d9b18b0d84ac0 = $(`2017-08-30 2:00 PM Wed AST
Tropical Storm`)[0];
popup_cb7d4aba1d9245609c8d57091bc8580e.setContent(html_803b50e64e7d4544920d9b18b0d84ac0);
circle_marker_69b5a419b71142099210bd5914519db7.bindPopup(popup_cb7d4aba1d9245609c8d57091bc8580e)
;
var circle_marker_cdb2c153b23b48ce95d948f697ba4da8 = L.circleMarker(
[16.4, -32.2],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_f970aad7f683451298c8753fe8e693cd = L.popup({"maxWidth": "100%"});
var html_8564b01cf59e4b6bb1475b94d7bf840b = $(`2017-08-30 8:00 PM Wed AST
Tropical Storm`)[0];
popup_f970aad7f683451298c8753fe8e693cd.setContent(html_8564b01cf59e4b6bb1475b94d7bf840b);
circle_marker_cdb2c153b23b48ce95d948f697ba4da8.bindPopup(popup_f970aad7f683451298c8753fe8e693cd)
;
var circle_marker_2ffbe0e95814452995014bb310e724ef = L.circleMarker(
[16.5, -32.9],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_7a7c94efcf424ac18b06a7013c643ab0 = L.popup({"maxWidth": "100%"});
var html_356b3615bb184eedadca4259360d7eeb = $(`2017-08-31 2:00 AM Thu AST
Tropical Storm`)[0];
popup_7a7c94efcf424ac18b06a7013c643ab0.setContent(html_356b3615bb184eedadca4259360d7eeb);
circle_marker_2ffbe0e95814452995014bb310e724ef.bindPopup(popup_7a7c94efcf424ac18b06a7013c643ab0)
;
var circle_marker_bfe0780049dd4141b01afed387766940 = L.circleMarker(
[16.9, -33.8],
{"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_baba2f1afe5d49018e32758046279fdc = L.popup({"maxWidth": "100%"});
var html_7b37ac29381847d9bc65bcef25efcab4 = $(`2017-08-31 8:00 AM Thu AST
Hurricane`)[0];
popup_baba2f1afe5d49018e32758046279fdc.setContent(html_7b37ac29381847d9bc65bcef25efcab4);
circle_marker_bfe0780049dd4141b01afed387766940.bindPopup(popup_baba2f1afe5d49018e32758046279fdc)
;
var circle_marker_3e14fb3289bc4c3e9f95e6418e8da463 = L.circleMarker(
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).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_1e87009229284a70a0d84f2bf516b23d = L.popup({"maxWidth": "100%"});
var html_f3fc2e22caff482098d47ce59ae571ac = $(`2017-08-31 2:00 PM Thu AST
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popup_1e87009229284a70a0d84f2bf516b23d.setContent(html_f3fc2e22caff482098d47ce59ae571ac);
circle_marker_3e14fb3289bc4c3e9f95e6418e8da463.bindPopup(popup_1e87009229284a70a0d84f2bf516b23d)
;
var circle_marker_7c0156a77ff24f06b3517c196b511699 = L.circleMarker(
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).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_ee27cad6513b471d97d111106c1dde68 = L.popup({"maxWidth": "100%"});
var html_db36b9ddd2804b88b4abe6d32e510ff9 = $(`2017-08-31 8:00 PM Thu AST
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popup_d0fdc5082a0e4c979817b51701c5c46c.setContent(html_b5419bd403b54dd49dddb64de81df38a);
circle_marker_79ca51b542504b3bb6ed20a3a231bd3b.bindPopup(popup_d0fdc5082a0e4c979817b51701c5c46c)
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).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_6e12e39879014ab78712a66f885aae03 = L.popup({"maxWidth": "100%"});
var html_ac9b8bc51fc542179058a7b0d146ed99 = $(`2017-09-10 5:00 PM Sun EDT
Hurricane`)[0];
popup_6e12e39879014ab78712a66f885aae03.setContent(html_ac9b8bc51fc542179058a7b0d146ed99);
circle_marker_10b79c1ef5b2466e956f39f8d74a0238.bindPopup(popup_6e12e39879014ab78712a66f885aae03)
;
var circle_marker_5747e1ddd1604e6c8eb7f9c2df11ea16 = L.circleMarker(
[27.5, -81.9],
{"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_5831097fd011492e96c8f7933060da64 = L.popup({"maxWidth": "100%"});
var html_7de956ec988d49b385c3424aa9a51867 = $(`2017-09-10 8:00 PM Sun EDT
Hurricane`)[0];
popup_5831097fd011492e96c8f7933060da64.setContent(html_7de956ec988d49b385c3424aa9a51867);
circle_marker_5747e1ddd1604e6c8eb7f9c2df11ea16.bindPopup(popup_5831097fd011492e96c8f7933060da64)
;
var circle_marker_007de6b97fe042c484eecc216acf833d = L.circleMarker(
[28.2, -82.2],
{"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_4b1bcfc0de2147c98bc46e0d49d1b6c3 = L.popup({"maxWidth": "100%"});
var html_e109174af1694fab8a1e5523a74baac3 = $(`2017-09-10 11:00 PM Sun EDT
Hurricane`)[0];
popup_4b1bcfc0de2147c98bc46e0d49d1b6c3.setContent(html_e109174af1694fab8a1e5523a74baac3);
circle_marker_007de6b97fe042c484eecc216acf833d.bindPopup(popup_4b1bcfc0de2147c98bc46e0d49d1b6c3)
;
var circle_marker_89e1133f6c6049be94f34714bace7275 = L.circleMarker(
[28.9, -82.6],
{"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_b1b988e9f7a742218b36b4c523787f30 = L.popup({"maxWidth": "100%"});
var html_7e654d2587a2454cad8886323b6e4455 = $(`2017-09-11 2:00 AM Mon EDT
Hurricane`)[0];
popup_b1b988e9f7a742218b36b4c523787f30.setContent(html_7e654d2587a2454cad8886323b6e4455);
circle_marker_89e1133f6c6049be94f34714bace7275.bindPopup(popup_b1b988e9f7a742218b36b4c523787f30)
;
var circle_marker_7b9b0e148a25467cab75ae15c23698ac = L.circleMarker(
[29.5, -82.9],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_065a602bbe6d4b22954efa2cfa0b0086 = L.popup({"maxWidth": "100%"});
var html_53f057b5627345ff9a1e0032178572fd = $(`2017-09-11 5:00 AM Mon EDT
Tropical Storm`)[0];
popup_065a602bbe6d4b22954efa2cfa0b0086.setContent(html_53f057b5627345ff9a1e0032178572fd);
circle_marker_7b9b0e148a25467cab75ae15c23698ac.bindPopup(popup_065a602bbe6d4b22954efa2cfa0b0086)
;
var circle_marker_cf1f916677d04e7c87f1c4114c879bee = L.circleMarker(
[30.3, -83.1],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_f159a4df75364db898ea2022af4f50a4 = L.popup({"maxWidth": "100%"});
var html_bff19a272b154798a9c05ba9b3014db1 = $(`2017-09-11 8:00 AM Mon EDT
Tropical Storm`)[0];
popup_f159a4df75364db898ea2022af4f50a4.setContent(html_bff19a272b154798a9c05ba9b3014db1);
circle_marker_cf1f916677d04e7c87f1c4114c879bee.bindPopup(popup_f159a4df75364db898ea2022af4f50a4)
;
var circle_marker_6ca46e4a3a2c4a7687c17ca7982c0253 = L.circleMarker(
[30.8, -83.6],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_341a4f21ae674ecfbf63b0a8c9315411 = L.popup({"maxWidth": "100%"});
var html_82189291623d471385e53701b3d68cd2 = $(`2017-09-11 11:00 AM Mon EDT
Tropical Storm`)[0];
popup_341a4f21ae674ecfbf63b0a8c9315411.setContent(html_82189291623d471385e53701b3d68cd2);
circle_marker_6ca46e4a3a2c4a7687c17ca7982c0253.bindPopup(popup_341a4f21ae674ecfbf63b0a8c9315411)
;
var circle_marker_28417bf15f5142dca8876573cc0fd336 = L.circleMarker(
[31.5, -84.0],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_4a36d93012e24f24aff542e33413f5a3 = L.popup({"maxWidth": "100%"});
var html_9a842b3f0b4a4487b6859361c35a4fd3 = $(`2017-09-11 2:00 PM Mon EDT
Tropical Storm`)[0];
popup_4a36d93012e24f24aff542e33413f5a3.setContent(html_9a842b3f0b4a4487b6859361c35a4fd3);
circle_marker_28417bf15f5142dca8876573cc0fd336.bindPopup(popup_4a36d93012e24f24aff542e33413f5a3)
;
var circle_marker_e3d3ca86a34e419482867534dfa1dbd6 = L.circleMarker(
[31.9, -84.4],
{"bubblingMouseEvents": true, "color": "orange", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "orange", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_1013b8bc893a4a898f375e72c0c6de2a = L.popup({"maxWidth": "100%"});
var html_78549cc5272d4098be06b28b37decb8e = $(`2017-09-11 5:00 PM Mon EDT
Tropical Storm`)[0];
popup_1013b8bc893a4a898f375e72c0c6de2a.setContent(html_78549cc5272d4098be06b28b37decb8e);
circle_marker_e3d3ca86a34e419482867534dfa1dbd6.bindPopup(popup_1013b8bc893a4a898f375e72c0c6de2a)
;
var circle_marker_149fc7560f334614bc3a1bd297cc38b3 = L.circleMarker(
[32.4, -84.9],
{"bubblingMouseEvents": true, "color": "yellow", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "yellow", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var popup_191c65ec2f1142c398057f186dcc5f83 = L.popup({"maxWidth": "100%"});
var html_e5aa9b67d177468799de86badee5e33b = $(`2017-09-11 8:00 PM Mon EDT
Tropical Depression`)[0];
popup_191c65ec2f1142c398057f186dcc5f83.setContent(html_e5aa9b67d177468799de86badee5e33b);
circle_marker_149fc7560f334614bc3a1bd297cc38b3.bindPopup(popup_191c65ec2f1142c398057f186dcc5f83)
;
var layer_control_9e7d1bba37c84bf894faefcd0363013b = {
base_layers : {
"openstreetmap" : tile_layer_f5a68aca46034a2a8d03c9d93c1fbea9,
},
overlays : {
"Observations" : marker_cluster_624cbc20221448a3a44838711f76c24c,
"Past predictions" : marker_cluster_583b0018b2cb4fab8f6a9268961c28b1,
"Sea Surface Temperature" : macro_element_d905a95ac2644d268c8ba663ca7a5915,
"HF Radar" : macro_element_c3c5c0d52eb54418853349ee862c34a2,
"Cone prediction as of 1100 PM EDT Mon Sep 11 2017" : geo_json_0bad691d11884d8a8f0b9e927b9f9fa2,
},
};
L.control.layers(
layer_control_9e7d1bba37c84bf894faefcd0363013b.base_layers,
layer_control_9e7d1bba37c84bf894faefcd0363013b.overlays,
{"autoZIndex": true, "collapsed": true, "position": "topright"}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
var poly_line_978f7ecf1f904e6aba8345623b53331f = L.polyline(
[[14.399999999999999, -91.90643899589291], [14.399999999999999, -28.3], [39.45358, -28.3], [39.45358, -91.90643899589291], [14.399999999999999, -91.90643899589291]],
{"bubblingMouseEvents": true, "color": "#009933", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "#009933", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "noClip": false, "opacity": 0.2, "smoothFactor": 1.0, "stroke": true, "weight": 1}
).addTo(map_de6666017af64adeb0ad9ae619186d5a);
" style="width: 100%; height: 750px; border: none">
Content source: ioos/notebooks_demos
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