stormevents 1.3.2.2

Python interfaces for observational data surrounding named storm events

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;

1. retrieve data for any arbitrary time interval / region, or
2. 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
...              ...   ...   ...   ...     ...       ...                 ...                 ...
EP922021      INVEST    DB  2021    EP      92  METWATCH 2021-06-05 06:00:00                 NaT
AL952021      INVEST    DB  2021    AL      95  METWATCH 2021-10-28 12:00:00                 NaT
AL962021      INVEST    EX  2021    AL      96  METWATCH 2021-11-07 12:00:00                 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

[2729 rows x 8 columns]

retrieve storm track by NHC code

from stormevents.nhc import VortexTrack

track = VortexTrack('AL112017')
track.data

    basin storm_number record_type            datetime  ...   direction     speed    name                    geometry
0      AL           11        BEST 2017-08-30 00:00:00  ...    0.000000  0.000000  INVEST  POINT (-26.90000 16.10000)
1      AL           11        BEST 2017-08-30 06:00:00  ...  274.421188  6.951105  INVEST  POINT (-28.30000 16.20000)
2      AL           11        BEST 2017-08-30 12:00:00  ...  274.424523  6.947623    IRMA  POINT (-29.70000 16.30000)
3      AL           11        BEST 2017-08-30 18:00:00  ...  270.154371  5.442611    IRMA  POINT (-30.80000 16.30000)
4      AL           11        BEST 2017-08-30 18:00:00  ...  270.154371  5.442611    IRMA  POINT (-30.80000 16.30000)
..    ...          ...         ...                 ...  ...         ...       ...     ...                         ...
168    AL           11        BEST 2017-09-12 12:00:00  ...  309.875306  7.262151    IRMA  POINT (-86.90000 33.80000)
169    AL           11        BEST 2017-09-12 18:00:00  ...  315.455084  7.247674    IRMA  POINT (-88.10000 34.80000)
170    AL           11        BEST 2017-09-13 00:00:00  ...  320.849994  5.315966    IRMA  POINT (-88.90000 35.60000)
171    AL           11        BEST 2017-09-13 06:00:00  ...  321.042910  3.973414    IRMA  POINT (-89.50000 36.20000)
172    AL           11        BEST 2017-09-13 12:00:00  ...  321.262133  3.961652    IRMA  POINT (-90.10000 36.80000)

[173 rows x 22 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_Mode.historical: 'ARCHIVE'>, '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 record_type            datetime  ...   direction      speed    name                    geometry
0        AL           11        CARQ 2017-08-27 06:00:00  ...    0.000000   0.000000  INVEST  POINT (-17.40000 11.70000)
1        AL           11        CARQ 2017-08-27 12:00:00  ...  281.524268   2.574642  INVEST  POINT (-17.90000 11.80000)
2        AL           11        CARQ 2017-08-27 12:00:00  ...  281.524268   2.574642  INVEST  POINT (-13.30000 11.50000)
3        AL           11        CARQ 2017-08-27 18:00:00  ...  281.528821   2.573747  INVEST  POINT (-18.40000 11.90000)
4        AL           11        CARQ 2017-08-27 18:00:00  ...  281.528821   2.573747  INVEST  POINT (-16.00000 11.50000)
...     ...          ...         ...                 ...  ...         ...        ...     ...                         ...
10739    AL           11        HMON 2017-09-16 09:00:00  ...   52.414833  11.903071          POINT (-84.30000 43.00000)
10740    AL           11        HMON 2017-09-16 12:00:00  ...    7.196515   6.218772          POINT (-84.30000 41.00000)
10741    AL           11        HMON 2017-09-16 12:00:00  ...    7.196515   6.218772          POINT (-82.00000 39.50000)
10742    AL           11        HMON 2017-09-16 12:00:00  ...    7.196515   6.218772          POINT (-84.30000 44.00000)
10743    AL           11        HMON 2017-09-16 15:00:00  ...  122.402907  22.540200          POINT (-81.90000 39.80000)

[10744 rows x 22 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_from_atcf/atcf.trk')

VortexTrack('BT02008', Timestamp('2008-10-16 17:06:00'), Timestamp('2008-10-20 20:06:00'), <ATCF_FileDeck.BEST: 'b'>, <ATCF_Mode.historical: 'ARCHIVE'>, 'BEST', 'tests/data/input/test_from_atcf/atcf.trk')

from stormevents.nhc import VortexTrack

VortexTrack.from_fort22('tests/data/input/test_from_fort22/irma2017_fort.22')

VortexTrack('AL112017', Timestamp('2017-09-05 00:00:00'), Timestamp('2017-09-19 00:00:00'), <ATCF_FileDeck.BEST: 'b'>, <ATCF_Mode.historical: 'ARCHIVE'>, 'BEST', 'tests/data/input/test_from_fort22/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  ...          start_date            end_date
usgs_id                                                 ...
7                             FEMA 2013 exercise  2013  ... 2013-05-15 04:00:00 2013-05-23 04:00:00
8                                          Wilma  2005  ... 2005-10-20 00:00:00 2005-10-31 00:00:00
9                            Midwest Floods 2011  2011  ... 2011-02-01 06:00:00 2011-08-30 05:00:00
10                          2013 - June PA Flood  2013  ... 2013-06-23 00:00:00 2013-07-01 00:00:00
11               Colorado 2013 Front Range Flood  2013  ... 2013-09-12 05:00:00 2013-09-24 05:00:00
...                                          ...   ...  ...                 ...                 ...
311                   2021 August Flash Flood TN  2021  ... 2021-08-21 05:00:00 2021-08-22 05:00:00
312                    2021 Tropical Cyclone Ida  2021  ... 2021-08-27 05:00:00 2021-09-03 05:00:00
313                Chesapeake Bay - October 2021  2021  ... 2021-10-28 04:00:00                 NaT
314      2021 November Flooding Washington State  2021  ... 2021-11-08 06:00:00 2021-11-19 06:00:00
315          Washington Coastal Winter 2021-2022  2021  ... 2021-11-01 05:00:00 2022-06-30 05:00:00

[292 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  ...                                          hwm_notes siteZone                    geometry
hwm_id                                             ...                                                                                        
22602   31.170642 -81.428402  Irma September 2017  ...                                                NaN      NaN  POINT (-81.42840 31.17064)
22605   31.453850 -81.362853  Irma September 2017  ...                                                NaN      NaN  POINT (-81.36285 31.45385)
22612   30.720000 -81.549440  Irma September 2017  ...  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  ...  Trimble R8 used to establish TBM. Levels ran f...      NaN  POINT (-81.23827 32.00773)
22653   31.531078 -81.358894  Irma September 2017  ...                                                NaN      NaN  POINT (-81.35889 31.53108)
...           ...        ...                  ...  ...                                                ...      ...                         ...
26171   18.470402 -66.246631  Irma September 2017  ...                                                NaN      NaN  POINT (-66.24663 18.47040)
26173   18.470300 -66.449900  Irma September 2017  ...                                levels from GNSS BM      NaN  POINT (-66.44990 18.47030)
26175   18.463954 -66.140869  Irma September 2017  ...                                levels from GNSS BM      NaN  POINT (-66.14087 18.46395)
26177   18.488720 -66.392160  Irma September 2017  ...                                levels from GNSS BM      NaN  POINT (-66.39216 18.48872)
26179   18.005607 -65.871768  Irma September 2017  ...                                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  ...                                          hwm_notes siteZone                    geometry
hwm_id                                             ...                                                                                        
22602   31.170642 -81.428402  Irma September 2017  ...                                                NaN      NaN  POINT (-81.42840 31.17064)
22605   31.453850 -81.362853  Irma September 2017  ...                                                NaN      NaN  POINT (-81.36285 31.45385)
22612   30.720000 -81.549440  Irma September 2017  ...  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  ...  Trimble R8 used to establish TBM. Levels ran f...      NaN  POINT (-81.23827 32.00773)
22653   31.531078 -81.358894  Irma September 2017  ...                                                NaN      NaN  POINT (-81.35889 31.53108)
...           ...        ...                  ...  ...                                                ...      ...                         ...
26171   18.470402 -66.246631  Irma September 2017  ...                                                NaN      NaN  POINT (-66.24663 18.47040)
26173   18.470300 -66.449900  Irma September 2017  ...                                levels from GNSS BM      NaN  POINT (-66.44990 18.47030)
26175   18.463954 -66.140869  Irma September 2017  ...                                levels from GNSS BM      NaN  POINT (-66.14087 18.46395)
26177   18.488720 -66.392160  Irma September 2017  ...                                levels from GNSS BM      NaN  POINT (-66.39216 18.48872)
26179   18.005607 -65.871768  Irma September 2017  ...                                levels from GNSS BM      NaN  POINT (-65.87177 18.00561)

[506 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)
8735180  DILA1                    Dauphin Island    AL        active  2019-07-18 10:00:00,2018-07-30 16:40:00,2017-0...   POINT (-88.06250 30.25000)
8557380  LWSD1                             Lewes    DE        active  2019-08-01 00:00:00,2018-06-18 00:00:00,2017-0...   POINT (-75.12500 38.78125)
8465705  NWHC3                         New Haven    CT        active  2019-08-18 14:55:00,2019-08-18 14:54:00,2018-0...   POINT (-72.93750 41.28125)
9439099  WAUO3                             Wauna    OR        active  2019-08-19 22:59:00,2014-06-20 21:30:00,2013-0...  POINT (-123.43750 46.15625)
...        ...                               ...   ...           ...                                                ...                          ...
8448725  MSHM3               Menemsha Harbor, MA    MA  discontinued  2013-09-26 23:59:00,2013-09-26 00:00:00,2012-0...   POINT (-70.75000 41.34375)
8538886  TPBN4             Tacony-Palmyra Bridge    NJ  discontinued  2013-11-11 00:01:00,2013-11-11 00:00:00,2012-0...   POINT (-75.06250 40.00000)
9439011  HMDO3                           Hammond    OR  discontinued  2014-08-13 00:00:00,2011-04-12 23:59:00,2011-0...  POINT (-123.93750 46.18750)
8762372  LABL1  East Bank 1, Norco, B. LaBranche    LA  discontinued  2012-11-05 10:38:00,2012-11-05 10:37:00,2012-1...   POINT (-90.37500 30.04688)
8530528  CARN4       CARLSTADT, HACKENSACK RIVER    NJ  discontinued            1994-11-12 23:59:00,1994-11-12 00:00:00   POINT (-74.06250 40.81250)

[433 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:

1. 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_level One 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.
2. a region within which to retrieve the data product
3. 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('FLORENCE', 2018)

or from a storm NHC code,

from stormevents import StormEvent

StormEvent.from_nhc_code('EP172016')

StormEvent('PAINE', 2016)

or from a USGS flood event ID.

from stormevents import StormEvent

StormEvent.from_usgs_id(310)

StormEvent('HENRI', 2021, end_date='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('PAINE', 2016, start_date='2016-09-19 00:00:00', end_date='2016-09-19 12:00:00')

from stormevents import StormEvent
from datetime import datetime

StormEvent('paine', 2016, end_date=datetime(2016, 9, 19, 12))

StormEvent('PAINE', 2016, end_date='2016-09-19 12:00:00')

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('FLORENCE', 2018, start_date='2018-09-01 06:00:00')

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('HENRI', 2021, start_date='2021-08-21 12:00:00', end_date='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('IDA', 2021, end_date='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'>, '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'>, None, 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/searvey
• pyStorms - https://github.com/brey/pyStorms
• tropycal - https://tropycal.github.io/tropycal/index.html
• pyoos - https://github.com/ioos/pyoos
• csdllib - https://github.com/noaa-ocs-modeling/csdllib
• pyPoseidon - https://github.com/ec-jrc/pyPoseidon
• Thalassa - https://github.com/ec-jrc/Thalassa
• adcircpy - 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.