Python interfaces for observational data surrounding named storm events
Reason this release was yanked:
incorrect version tag
Project description
StormEvents
stormevents provides Python interfaces for observational data surrounding named storm events.
pip install stormevents
Full documentation can be found at https://stormevents.readthedocs.io
Usage
There are two ways to retrieve observational data via stormevents;
- retrieve data for any arbitrary time interval / region, or
- retrieve data surrounding a specific storm.
retrieve data for any arbitrary time interval / region
stormevents currently implements retrieval for
- storm tracks from the National Hurricane Center (NHC),
- high-water mark (HWM) surveys provided by the United States Geological Survey (USGS), and
- data products from the Center for Operational Oceanographic Products and Services (CO-OPS).
storm tracks from the National Hurricane Center (NHC)
The National Hurricane Center (NHC) tracks and tropical cyclones dating back to 1851.
The nhc_storms() function provides a list of NHC storms from their online archive:
from stormevents.nhc import nhc_storms
nhc_storms()
name class year basin number source start_date end_date
nhc_code
AL021851 UNNAMED HU 1851 AL 2 ARCHIVE 1851-07-05 12:00:00 1851-07-05 12:00:00
AL031851 UNNAMED TS 1851 AL 3 ARCHIVE 1851-07-10 12:00:00 1851-07-10 12:00:00
AL041851 UNNAMED HU 1851 AL 4 ARCHIVE 1851-08-16 00:00:00 1851-08-27 18:00:00
AL051851 UNNAMED TS 1851 AL 5 ARCHIVE 1851-09-13 00:00:00 1851-09-16 18:00:00
AL061851 UNNAMED TS 1851 AL 6 ARCHIVE 1851-10-16 00:00:00 1851-10-19 18:00:00
... ... ... ... ... ... ... ... ...
CP902021 INVEST LO 2021 CP 90 METWATCH 2021-07-24 12:00:00 NaT
CP912021 INVEST DB 2021 CP 91 METWATCH 2021-08-07 18:00:00 NaT
EP922021 INVEST DB 2021 EP 92 METWATCH 2021-06-05 06:00:00 NaT
EP712022 GENESIS001 DB 2022 EP 71 GENESIS 2022-01-20 12:00:00 NaT
EP902022 INVEST LO 2022 EP 90 METWATCH 2022-01-20 12:00:00 NaT
[2714 rows x 8 columns]
retrieve storm track by NHC code
from stormevents.nhc import VortexTrack
track = VortexTrack('AL112017')
track.data
basin storm_number datetime advisory_number ... isowave_radius_for_SWQ extra_values geometry track_start_time
0 AL 11 2017-08-30 00:00:00 ... NaN <NA> POINT (-26.90000 16.10000) 2017-08-30
1 AL 11 2017-08-30 06:00:00 ... NaN <NA> POINT (-28.30000 16.20000) 2017-08-30
2 AL 11 2017-08-30 12:00:00 ... NaN <NA> POINT (-29.70000 16.30000) 2017-08-30
3 AL 11 2017-08-30 18:00:00 ... NaN <NA> POINT (-30.80000 16.30000) 2017-08-30
4 AL 11 2017-08-30 18:00:00 ... NaN <NA> POINT (-30.80000 16.30000) 2017-08-30
.. ... ... ... ... ... ... ... ... ...
168 AL 11 2017-09-12 12:00:00 ... NaN <NA> POINT (-86.90000 33.80000) 2017-08-30
169 AL 11 2017-09-12 18:00:00 ... NaN <NA> POINT (-88.10000 34.80000) 2017-08-30
170 AL 11 2017-09-13 00:00:00 ... NaN <NA> POINT (-88.90000 35.60000) 2017-08-30
171 AL 11 2017-09-13 06:00:00 ... NaN <NA> POINT (-89.50000 36.20000) 2017-08-30
172 AL 11 2017-09-13 12:00:00 ... NaN <NA> POINT (-90.10000 36.80000) 2017-08-30
[173 rows x 38 columns]
retrieve storm track by name and year
If you do not know the storm code, you can input the storm name and year:
from stormevents.nhc import VortexTrack
VortexTrack.from_storm_name('irma', 2017)
VortexTrack('AL112017', Timestamp('2017-08-30 00:00:00'), Timestamp('2017-09-13 12:00:00'), <ATCF_FileDeck.BEST: 'b'>, [<ATCF_Advisory.BEST: 'BEST'>], None)
specify storm track file deck
By default, VortexTrack retrieves data from the BEST track file deck (b). You can specify that you want
the ADVISORY (a) or FIXED (f) file decks with the file_deck parameter.
from stormevents.nhc import VortexTrack
track = VortexTrack('AL112017', file_deck='a')
track.data
basin storm_number datetime advisory_number ... isowave_radius_for_SWQ extra_values geometry track_start_time
0 AL 11 2017-08-27 06:00:00 01 ... NaN <NA> POINT (-17.40000 11.70000) 2017-08-28 06:00:00
1 AL 11 2017-08-27 12:00:00 01 ... NaN <NA> POINT (-17.90000 11.80000) 2017-08-28 06:00:00
2 AL 11 2017-08-27 18:00:00 01 ... NaN <NA> POINT (-18.40000 11.90000) 2017-08-28 06:00:00
3 AL 11 2017-08-28 00:00:00 01 ... NaN <NA> POINT (-19.00000 12.00000) 2017-08-28 06:00:00
4 AL 11 2017-08-28 06:00:00 01 ... NaN <NA> POINT (-19.50000 12.00000) 2017-08-28 06:00:00
... ... ... ... ... ... ... ... ... ...
10739 AL 11 2017-09-12 00:00:00 03 ... NaN <NA> POINT (-84.40000 31.90000) 2017-09-12 00:00:00
10740 AL 11 2017-09-12 03:00:00 03 ... NaN <NA> POINT (-84.90000 32.40000) 2017-09-12 00:00:00
10741 AL 11 2017-09-12 12:00:00 03 ... NaN <NA> POINT (-86.40000 33.80000) 2017-09-12 00:00:00
10742 AL 11 2017-09-13 00:00:00 03 ... NaN <NA> POINT (-88.20000 35.20000) 2017-09-12 00:00:00
10743 AL 11 2017-09-13 12:00:00 03 ... NaN <NA> POINT (-88.60000 36.40000) 2017-09-12 00:00:00
[10434 rows x 38 columns]
read storm track from file
If you have an ATCF or fort.22 file, use the corresponding methods:
from stormevents.nhc import VortexTrack
VortexTrack.from_file('tests/data/input/test_vortex_track_from_file/AL062018.dat')
VortexTrack('AL062018', Timestamp('2018-08-30 06:00:00'), Timestamp('2018-09-18 12:00:00'), None, <ATCF_Mode.HISTORICAL: 'ARCHIVE'>, ['BEST', 'OFCL', 'OFCP', 'HMON', 'CARQ', 'HWRF'], PosixPath('/home/zrb/Projects/StormEvents/tests/data/input/test_vortex_track_from_file/AL062018.dat'))
from stormevents.nhc import VortexTrack
VortexTrack.from_file('tests/data/input/test_vortex_track_from_file/irma2017_fort.22')
VortexTrack('AL112017', Timestamp('2017-09-05 00:00:00'), Timestamp('2017-09-12 00:00:00'), None, <ATCF_Mode.HISTORICAL: 'ARCHIVE'>, ['BEST', 'OFCL', 'OFCP', 'HMON', 'CARQ', 'HWRF'], PosixPath('/home/zrb/Projects/StormEvents/tests/data/input/test_vortex_track_from_file/irma2017_fort.22'))
write storm track to fort.22 file
from stormevents.nhc import VortexTrack
track = VortexTrack.from_storm_name('florence', 2018)
track.to_file('fort.22')
high-water mark (HWM) surveys provided by the United States Geological Survey (USGS)
The United States Geological Survey (USGS) conducts surveys of flooded areas following flood events to determine the highest level of water elevation, and provides the results of these surveys via their API.
list flood events defined by the USGS that have HWM surveys
from stormevents.usgs import usgs_flood_events
usgs_flood_events()
name year description ... last_updated_by start_date end_date
usgs_id ...
7 FEMA 2013 exercise 2013 Ardent/Sentry 2013 FEMA Exercise ... NaN 2013-05-15 04:00:00 2013-05-23 04:00:00
8 Wilma 2005 Category 3 in west FL. \nHurricane Wilma was t... ... NaN 2005-10-20 00:00:00 2005-10-31 00:00:00
9 Midwest Floods 2011 2011 Spring and summer 2011 flooding of the Mississ... ... 35.0 2011-02-01 06:00:00 2011-08-30 05:00:00
10 2013 - June PA Flood 2013 Localized summer rain, small scale event ... NaN 2013-06-23 00:00:00 2013-07-01 00:00:00
11 Colorado 2013 Front Range Flood 2013 A large prolonged precipitation event resulted... ... 35.0 2013-09-12 05:00:00 2013-09-24 05:00:00
... ... ... ... ... ... ... ...
312 2021 Tropical Cyclone Ida 2021 NaN ... 864.0 2021-08-27 05:00:00 2021-09-03 05:00:00
313 Chesapeake Bay - October 2021 2021 Coastal-flooding event in the Chesapeake Bay. ... 406.0 2021-10-28 04:00:00 NaT
314 2021 November Flooding Washington State 2021 Atmospheric River Flooding ... 864.0 2021-11-08 06:00:00 2021-11-19 06:00:00
315 Washington Coastal Winter 2021-2022 2021 NaN ... 864.0 2021-11-01 05:00:00 2022-06-30 05:00:00
317 2022 Hunga Tonga-Hunga Haapai tsunami 2022 ... 1.0 2022-01-14 05:00:00 2022-01-18 05:00:00
[293 rows x 11 columns]
retrieve HWM survey data for any flood event
from stormevents.usgs import USGS_Event
flood = USGS_Event(182)
flood.high_water_marks()
latitude longitude eventName hwmTypeName ... hwm_uncertainty hwm_notes siteZone geometry
hwm_id ...
22602 31.170642 -81.428402 Irma September 2017 Debris ... NaN NaN NaN POINT (-81.42840 31.17064)
22605 31.453850 -81.362853 Irma September 2017 Seed line ... 0.1 NaN NaN POINT (-81.36285 31.45385)
22612 30.720000 -81.549440 Irma September 2017 Seed line ... NaN There is a secondary peak around 5.5 ft, so th... NaN POINT (-81.54944 30.72000)
22636 32.007730 -81.238270 Irma September 2017 Seed line ... 0.1 Trimble R8 used to establish TBM. Levels ran f... NaN POINT (-81.23827 32.00773)
22653 31.531078 -81.358894 Irma September 2017 Seed line ... NaN NaN NaN POINT (-81.35889 31.53108)
... ... ... ... ... ... ... ... ... ...
26171 18.470402 -66.246631 Irma September 2017 Debris ... 0.5 NaN NaN POINT (-66.24663 18.47040)
26173 18.470300 -66.449900 Irma September 2017 Debris ... 0.5 levels from GNSS BM NaN POINT (-66.44990 18.47030)
26175 18.463954 -66.140869 Irma September 2017 Debris ... 0.5 levels from GNSS BM NaN POINT (-66.14087 18.46395)
26177 18.488720 -66.392160 Irma September 2017 Debris ... 0.5 levels from GNSS BM NaN POINT (-66.39216 18.48872)
26179 18.005607 -65.871768 Irma September 2017 Debris ... 0.5 levels from GNSS BM NaN POINT (-65.87177 18.00561)
[506 rows x 53 columns]
from stormevents.usgs import USGS_Event
flood = USGS_Event(182)
flood.high_water_marks(quality=['EXCELLENT', 'GOOD'])
latitude longitude eventName hwmTypeName ... hwm_notes peak_summary_id siteZone geometry
hwm_id ...
22605 31.453850 -81.362853 Irma September 2017 Seed line ... NaN NaN NaN POINT (-81.36285 31.45385)
22612 30.720000 -81.549440 Irma September 2017 Seed line ... There is a secondary peak around 5.5 ft, so th... NaN NaN POINT (-81.54944 30.72000)
22636 32.007730 -81.238270 Irma September 2017 Seed line ... Trimble R8 used to establish TBM. Levels ran f... NaN NaN POINT (-81.23827 32.00773)
22674 32.030907 -80.900605 Irma September 2017 Seed line ... NaN 5042.0 NaN POINT (-80.90061 32.03091)
22849 30.741940 -81.687780 Irma September 2017 Debris ... NaN 4834.0 NaN POINT (-81.68778 30.74194)
... ... ... ... ... ... ... ... ... ...
25150 30.038222 -81.880928 Irma September 2017 Seed line ... GNSS Level II survey. NaN NaN POINT (-81.88093 30.03822)
25151 30.118110 -81.760220 Irma September 2017 Seed line ... GNSS Level III survey. NaN NaN POINT (-81.76022 30.11811)
25158 29.720560 -81.506110 Irma September 2017 Seed line ... GNSS Level II survey. NaN NaN POINT (-81.50611 29.72056)
25159 30.097514 -81.794375 Irma September 2017 Seed line ... GNSS Level III survey. NaN NaN POINT (-81.79438 30.09751)
25205 29.783890 -81.263060 Irma September 2017 Seed line ... GNSS Level II survey. NaN NaN POINT (-81.26306 29.78389)
[277 rows x 53 columns]
data products from the Center for Operational Oceanographic Products and Services (CO-OPS)
The Center for Operational Oceanographic Products and Services (CO-OPS) maintains and operates a large array of tidal buoys and oceanic weather stations that measure water and atmospheric variables across the coastal United States. CO-OPS provides several data products including hourly water levels, tidal datums and predictions, and trends in sea level over time.
A list of CO-OPS stations can be retrieved with the coops_stations() function.
from stormevents.coops import coops_stations
coops_stations()
nws_id name state status removed geometry
nos_id
1600012 46125 QREB buoy active <NA> POINT (122.62500 37.75000)
1619910 SNDP5 Sand Island, Midway Islands active <NA> POINT (-177.37500 28.21875)
1630000 APRP7 Apra Harbor, Guam active <NA> POINT (144.62500 13.44531)
1631428 PGBP7 Pago Bay, Guam active <NA> POINT (144.75000 13.42969)
1770000 NSTP6 Pago Pago, American Samoa active <NA> POINT (-170.75000 -14.27344)
... ... ... ... ... ... ...
8423898 FTPN3 Fort Point NH discontinued 2020-04-13 00:00:00,2014-08-05 00:00:00,2012-0... POINT (-70.68750 43.06250)
8726667 MCYF1 Mckay Bay Entrance FL discontinued 2020-05-20 00:00:00,2019-03-08 00:00:00,2017-0... POINT (-82.43750 27.90625)
8772447 FCGT2 Freeport TX discontinued 2020-05-24 18:45:00,2018-10-10 21:50:00,2018-1... POINT (-95.31250 28.93750)
9087079 GBWW3 Green Bay WI discontinued 2020-10-28 13:00:00,2007-08-06 23:59:00,2007-0... POINT (-88.00000 44.53125)
8770570 SBPT2 Sabine Pass North TX discontinued 2021-01-18 00:00:00,2020-09-30 15:45:00,2020-0... POINT (-93.87500 29.73438)
[435 rows x 6 columns]
Additionally, you can use a Shapely Polygon or MultiPolygon to constrain the stations query to a specific region:
from shapely.geometry import Polygon
from stormevents.coops import coops_stations_within_region
region = Polygon(...)
coops_stations_within_region(region=region)
nws_id name state removed geometry
nos_id
8651370 DUKN7 Duck NC NaT POINT (-75.75000 36.18750)
8652587 ORIN7 Oregon Inlet Marina NC NaT POINT (-75.56250 35.78125)
8654467 HCGN7 USCG Station Hatteras NC NaT POINT (-75.68750 35.21875)
8656483 BFTN7 Beaufort, Duke Marine Lab NC NaT POINT (-76.68750 34.71875)
8658120 WLON7 Wilmington NC NaT POINT (-77.93750 34.21875)
8658163 JMPN7 Wrightsville Beach NC NaT POINT (-77.81250 34.21875)
8661070 MROS1 Springmaid Pier SC NaT POINT (-78.93750 33.65625)
8662245 NITS1 Oyster Landing (N Inlet Estuary) SC NaT POINT (-79.18750 33.34375)
8665530 CHTS1 Charleston, Cooper River Entrance SC NaT POINT (-79.93750 32.78125)
8670870 FPKG1 Fort Pulaski GA NaT POINT (-80.87500 32.03125)
retrieve CO-OPS data product for a specific station
from datetime import datetime
from stormevents.coops import COOPS_Station
station = COOPS_Station(8632200)
station.product('water_level', start_date=datetime(2018, 9, 13), end_date=datetime(2018, 9, 16, 12))
<xarray.Dataset>
Dimensions: (nos_id: 1, t: 841)
Coordinates:
* nos_id (nos_id) int64 8632200
* t (t) datetime64[ns] 2018-09-13 ... 2018-09-16T12:00:00
nws_id (nos_id) <U5 'KPTV2'
x (nos_id) float64 -76.0
y (nos_id) float64 37.16
Data variables:
v (nos_id, t) float32 1.67 1.694 1.73 1.751 ... 1.597 1.607 1.605
s (nos_id, t) float32 0.026 0.027 0.034 0.03 ... 0.018 0.019 0.021
f (nos_id, t) object '0,0,0,0' '0,0,0,0' ... '0,0,0,0' '0,0,0,0'
q (nos_id, t) object 'v' 'v' 'v' 'v' 'v' 'v' ... 'v' 'v' 'v' 'v' 'v'
retrieve CO-OPS data product from within a region and time interval
To retrieve data, you must provide three things:
- the data product of interest; one of
water_level- Preliminary or verified water levels, depending on availability.air_temperature- Air temperature as measured at the station.water_temperature- Water temperature as measured at the station.wind- Wind speed, direction, and gusts as measured at the station.air_pressure- Barometric pressure as measured at the station.air_gap- Air Gap (distance between a bridge and the water's surface) at the station.conductivity- The water's conductivity as measured at the station.visibility- Visibility from the station's visibility sensor. A measure of atmospheric clarity.humidity- Relative humidity as measured at the station.salinity- Salinity and specific gravity data for the station.hourly_height- Verified hourly height water level data for the station.high_low- Verified high/low water level data for the station.daily_mean- Verified daily mean water level data for the station.monthly_mean- Verified monthly mean water level data for the station.one_minute_water_levelOne minute water level data for the station.predictions- 6 minute predictions water level data for the station.*datums- datums data for the stations.currents- Currents data for currents stations.currents_predictions- Currents predictions data for currents predictions stations.
- a region within which to retrieve the data product
- a time interval within which to retrieve the data product
from datetime import datetime, timedelta
from shapely.geometry import Polygon
from stormevents.coops import coops_product_within_region
polygon = Polygon(...)
coops_product_within_region(
'water_level',
region=polygon,
start_date=datetime.now() - timedelta(hours=1),
)
<xarray.Dataset>
Dimensions: (nos_id: 10, t: 11)
Coordinates:
* nos_id (nos_id) int64 8651370 8652587 8654467 ... 8662245 8665530 8670870
* t (t) datetime64[ns] 2022-03-08T14:48:00 ... 2022-03-08T15:48:00
nws_id (nos_id) <U5 'DUKN7' 'ORIN7' 'HCGN7' ... 'NITS1' 'CHTS1' 'FPKG1'
x (nos_id) float64 -75.75 -75.56 -75.69 ... -79.19 -79.94 -80.88
y (nos_id) float64 36.19 35.78 35.22 34.72 ... 33.34 32.78 32.03
Data variables:
v (nos_id, t) float32 6.256 6.304 6.361 6.375 ... 2.633 2.659 2.686
s (nos_id, t) float32 0.107 0.097 0.127 0.122 ... 0.005 0.004 0.004
f (nos_id, t) object '1,0,0,0' '1,0,0,0' ... '1,0,0,0' '1,0,0,0'
q (nos_id, t) object 'p' 'p' 'p' 'p' 'p' 'p' ... 'p' 'p' 'p' 'p' 'p'
retrieve data surrounding a specific storm
The StormEvent class provides an interface to retrieve data within the time interval and spatial bounds of a specific storm
event.
You can create a new StormEvent object from a storm name and year,
from stormevents import StormEvent
StormEvent('FLORENCE', 2018)
StormEvent(name='FLORENCE', year=2018, start_date=Timestamp('2018-08-30 06:00:00'), end_date=Timestamp('2018-09-18 12:00:00'))
or from a storm NHC code,
from stormevents import StormEvent
StormEvent.from_nhc_code('EP172016')
StormEvent(name='PAINE', year=2016, start_date=Timestamp('2016-09-18 00:00:00'), end_date=Timestamp('2016-09-21 12:00:00'))
or from a USGS flood event ID.
from stormevents import StormEvent
StormEvent.from_usgs_id(310)
StormEvent(name='HENRI', year=2021, start_date=Timestamp('2021-08-20 18:00:00'), end_date=Timestamp('2021-08-24 12:00:00'))
To constrain the time interval, you can provide an absolute time range,
from stormevents import StormEvent
from datetime import datetime
StormEvent('paine', 2016, start_date='2016-09-19', end_date=datetime(2016, 9, 19, 12))
StormEvent(name='PAINE', year=2016, start_date=datetime.datetime(2016, 9, 19, 0, 0), end_date=datetime.datetime(2016, 9, 19, 12, 0))
from stormevents import StormEvent
from datetime import datetime
StormEvent('paine', 2016, end_date=datetime(2016, 9, 19, 12))
StormEvent(name='PAINE', year=2016, start_date=Timestamp('2016-09-18 00:00:00'), end_date=datetime.datetime(2016, 9, 19, 12, 0))
or, alternatively, you can provide relative time deltas, which will be interpreted compared to the absolute time interval provided by the NHC.
from stormevents import StormEvent
from datetime import timedelta
StormEvent('florence', 2018, start_date=timedelta(days=2)) # <- start 2 days after NHC start time
StormEvent(name='FLORENCE', year=2018, start_date=Timestamp('2018-09-01 06:00:00'), end_date=Timestamp('2018-09-18 12:00:00'))
from stormevents import StormEvent
from datetime import timedelta
StormEvent(
'henri',
2021,
start_date=timedelta(days=-3), # <- start 3 days before NHC end time
end_date=timedelta(days=-2), # <- end 2 days before NHC end time
)
StormEvent(name='HENRI', year=2021, start_date=Timestamp('2021-08-21 12:00:00'), end_date=Timestamp('2021-08-22 12:00:00'))
from stormevents import StormEvent
from datetime import timedelta
StormEvent('ida', 2021, end_date=timedelta(days=2)) # <- end 2 days after NHC start time
StormEvent(name='IDA', year=2021, start_date=Timestamp('2021-08-27 18:00:00'), end_date=Timestamp('2021-08-29 18:00:00'))
retrieve data for a storm
The following methods are very similar to the data getter functions detailed above. However, these methods are tied to a specific storm event, and will focus on retrieving data within the spatial region and time interval of their specific storm event.
track data from the National Hurricane Center (NHC)
from stormevents import StormEvent
storm = StormEvent('florence', 2018)
storm.track()
VortexTrack('AL062018', Timestamp('2018-08-30 06:00:00'), Timestamp('2018-09-18 12:00:00'), <ATCF_FileDeck.BEST: 'b'>, <ATCF_Mode.HISTORICAL: 'ARCHIVE'>, [<ATCF_Advisory.BEST: 'BEST'>], None)
from stormevents import StormEvent
storm = StormEvent('florence', 2018)
storm.track(file_deck='a')
VortexTrack('AL062018', Timestamp('2018-08-30 06:00:00'), Timestamp('2018-09-18 12:00:00'), <ATCF_FileDeck.ADVISORY: 'a'>, <ATCF_Mode.HISTORICAL: 'ARCHIVE'>, ['OFCL', 'OFCP', 'HMON', 'CARQ', 'HWRF'], None)
high-water mark (HWM) surveys provided by the United States Geological Survey (USGS)
from stormevents import StormEvent
storm = StormEvent('florence', 2018)
flood = storm.flood_event
flood.high_water_marks()
latitude longitude eventName ... siteZone peak_summary_id geometry
hwm_id ...
33496 37.298440 -80.007750 Florence Sep 2018 ... NaN NaN POINT (-80.00775 37.29844)
33497 33.699720 -78.936940 Florence Sep 2018 ... NaN NaN POINT (-78.93694 33.69972)
33498 33.758610 -78.792780 Florence Sep 2018 ... NaN NaN POINT (-78.79278 33.75861)
33499 33.641389 -78.947778 Florence Sep 2018 ... NaN POINT (-78.94778 33.64139)
33500 33.602500 -78.973889 Florence Sep 2018 ... NaN POINT (-78.97389 33.60250)
... ... ... ... ... ... ... ...
34872 35.534641 -77.038183 Florence Sep 2018 ... NaN NaN POINT (-77.03818 35.53464)
34873 35.125000 -77.050044 Florence Sep 2018 ... NaN NaN POINT (-77.05004 35.12500)
34874 35.917467 -76.254367 Florence Sep 2018 ... NaN NaN POINT (-76.25437 35.91747)
34875 35.111000 -77.037851 Florence Sep 2018 ... NaN NaN POINT (-77.03785 35.11100)
34876 35.301135 -77.264727 Florence Sep 2018 ... NaN NaN POINT (-77.26473 35.30114)
[644 rows x 53 columns]
products from the Center for Operational Oceanographic Products and Services (CO-OPS)
from stormevents import StormEvent
storm = StormEvent('florence', 2018)
storm.coops_product_within_isotach('water_level', wind_speed=34, start_date='2018-09-12 14:03:00', end_date='2018-09-14')
<xarray.Dataset>
Dimensions: (nos_id: 7, t: 340)
Coordinates:
* nos_id (nos_id) int64 8651370 8652587 8654467 ... 8658120 8658163 8661070
* t (t) datetime64[ns] 2018-09-12T14:06:00 ... 2018-09-14
nws_id (nos_id) <U5 'DUKN7' 'ORIN7' 'HCGN7' ... 'WLON7' 'JMPN7' 'MROS1'
x (nos_id) float64 -75.75 -75.56 -75.69 -76.69 -77.94 -77.81 -78.94
y (nos_id) float64 36.19 35.78 35.22 34.72 34.22 34.22 33.66
Data variables:
v (nos_id, t) float32 7.181 7.199 7.144 7.156 ... 9.6 9.634 9.686
s (nos_id, t) float32 0.317 0.36 0.31 0.318 ... 0.049 0.047 0.054
f (nos_id, t) object '0,0,0,0' '0,0,0,0' ... '0,0,0,0' '0,0,0,0'
q (nos_id, t) object 'v' 'v' 'v' 'v' 'v' 'v' ... 'v' 'v' 'v' 'v' 'v'
Related Projects
searvey- https://github.com/oceanmodeling/searveypyStorms- https://github.com/brey/pyStormstropycal- https://tropycal.github.io/tropycal/index.htmlpyoos- https://github.com/ioos/pyooscsdllib- https://github.com/noaa-ocs-modeling/csdllibpyPoseidon- https://github.com/ec-jrc/pyPoseidonThalassa- https://github.com/ec-jrc/Thalassaadcircpy- https://github.com/noaa-ocs-modeling/adcircpy
Acknowledgements
Original methodology for retrieving NHC storm tracks and CO-OPS tidal data was written
by @jreniel for adcircpy.
Original methodology for retrieving USGS high-water mark surveys and CO-OPS tidal station metadata was written
by @moghimis and @grapesh
for csdllib.
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