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

storm interface

You can instantiate a new StormEvent object from the NHC storm name and year (i.e. FLORENCE 2018),

from stormevents import StormEvent

florence2018 = StormEvent('florence', 2018)

StormEvent('FLORENCE', 2018)

or from the NHC storm code (i.e. AL062018),

from stormevents import StormEvent

paine2016 = StormEvent.from_nhc_code('EP172016')

StormEvent('PAINE', 2016)

or from the USGS flood event ID (i.e. 283).

from stormevents import StormEvent

henri2021 = StormEvent.from_usgs_id(310)

StormEvent('HENRI', 2021)

For this storm, you can then retrieve track data from NHC,

from stormevents import StormEvent

florence2018 = StormEvent('florence', 2018)

track = florence2018.track()

VortexTrack('AL062018', Timestamp('2018-08-29 06:00:00'), Timestamp('2018-09-22 18:00:00'), 
            ATCF_FileDeck.a, ATCF_Mode.historical, None, None)

high-water mark data from USGS,

from stormevents import StormEvent

florence2018 = StormEvent('florence', 2018)

high_water_marks = florence2018.high_water_marks()

         latitude  ...  siteZone
hwm_id             ...          
33496   37.298440  ...       NaN
33502   35.342089  ...       NaN
33503   35.378963  ...       NaN
33505   35.216282  ...       NaN
33508   35.199859  ...       NaN
  ...         ...  ...       ...
34191   33.724722  ...       NaN
34235   34.936308  ...          
34840   34.145930  ...       NaN
34871   35.424707  ...       NaN
34876   35.301135  ...       NaN

[509 rows x 51 columns]

and water level products from CO-OPS.

from stormevents import StormEvent

florence2018 = StormEvent('florence', 2018)

water_levels = florence2018.tidal_data_within_isotach(isotach=34, start_date='20180913230000', end_date='20180914')

Dimensions:  (t: 11, nos_id: 10)
Coordinates:
  * t        (t) datetime64[ns] 2018-09-13T23:00:00 ... 2018-09-14
  * nos_id   (nos_id) int64 8639348 8651370 8652587 ... 8661070 8662245 8665530
    nws_id   (nos_id) <U5 'MNPV2' 'DUKN7' 'ORIN7' ... 'MROS1' 'NITS1' 'CHTS1'
    x        (nos_id) float64 -76.31 -75.75 -75.56 ... -78.94 -79.19 -79.94
    y        (nos_id) float64 36.78 36.19 35.78 35.22 ... 33.66 33.34 32.78
Data variables:
    v        (nos_id, t) float32 7.271 7.274 7.27 7.27 ... 1.549 1.587 1.624
    s        (nos_id, t) float32 0.005 0.004 0.005 0.004 ... 0.005 0.007 0.006
    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'

By default, these functions operate within the time interval defined by the NHC best track.

storm data from the National Hurricane Center (NHC)

list storm events defined by the NHC

from stormevents.nhc import nhc_storms

nhc_storms = nhc_storms()

                name class  ...          start_date            end_date
nhc_code                    ...                                        
AL021851     UNNAMED    HU  ... 1851-07-05 12:00:00 1851-07-05 12:00:00
AL031851     UNNAMED    TS  ... 1851-07-10 12:00:00 1851-07-10 12:00:00
AL041851     UNNAMED    HU  ... 1851-08-16 00:00:00 1851-08-27 18:00:00
AL051851     UNNAMED    TS  ... 1851-09-13 00:00:00 1851-09-16 18:00:00
AL061851     UNNAMED    TS  ... 1851-10-16 00:00:00 1851-10-19 18:00:00
     ...         ...   ...  ...                 ...                 ...
EP922021      INVEST    DB  ... 2021-06-05 06:00:00                 NaT
AL952021      INVEST    DB  ... 2021-10-28 12:00:00                 NaT
AL962021      INVEST    EX  ... 2021-11-07 12:00:00                 NaT
EP712022  GENESIS001    DB  ... 2022-01-20 12:00:00                 NaT
EP902022      INVEST    LO  ... 2022-01-20 12:00:00                 NaT

[2729 rows x 8 columns]

retrieve storm tracks provided by the NHC

from stormevents.nhc import VortexTrack
from stormevents.nhc.atcf import ATCF_FileDeck

# retrieve vortex data from the Internet from its ID
vortex = VortexTrack('AL112017')

# you can specify the file deck with `file_deck`
vortex = VortexTrack('AL112017', file_deck=ATCF_FileDeck.b)

# you can also use the storm name and year in the lookup
vortex = VortexTrack('irma2017')

# write to a file in the ADCIRC `fort.22` format
vortex.write('fort.22')

# read vortex data from an existing ATCF track file (`*.trk`)
vortex = VortexTrack.from_atcf_file('atcf.trk')

# read vortex data from an existing file in the ADCIRC `fort.22` format
vortex = VortexTrack.from_fort22('fort.22')

high water mark (HWM) surveys from the United States Geological Survey (USGS)

list storm flood events that have HWM surveys

from stormevents.usgs import usgs_highwatermark_storms

hwm_storms = usgs_highwatermark_storms()

         year                         usgs_name  nhc_name  nhc_code
usgs_id                                                            
7        2013                FEMA 2013 exercise      None      None
8        2013                             Wilma      None      None
18       2012                    Isaac Aug 2012     ISAAC  AL092012
19       2005                              Rita      RITA  AL182005
23       2011                             Irene     IRENE  AL092011
...       ...                               ...       ...       ...
303      2020  2020 TS Marco - Hurricane  Laura     MARCO  AL142020
304      2020              2020 Hurricane Sally     SALLY  AL192020
305      2020              2020 Hurricane Delta     DELTA  AL262020
310      2021       2021 Tropical Cyclone Henri     HENRI  AL082021
312      2021         2021 Tropical Cyclone Ida       IDA  AL092021

[24 rows x 4 columns]

retrieve HWM data for a specific storm

from stormevents.usgs import StormHighWaterMarks

hwm_florence2018 = StormHighWaterMarks('florence', 2018)

hwm_florence2018.data
hwm_florence2018.data.columns

         latitude  ...  siteZone
hwm_id             ...          
33496   37.298440  ...       NaN
33502   35.342089  ...       NaN
33503   35.378963  ...       NaN
33505   35.216282  ...       NaN
33508   35.199859  ...       NaN
...           ...  ...       ...
34191   33.724722  ...       NaN
34235   34.936308  ...          
34840   34.145930  ...       NaN
34871   35.424707  ...       NaN
34876   35.301135  ...       NaN

[509 rows x 51 columns]

Index(['latitude', 'longitude', 'eventName', 'hwmTypeName', 'hwmQualityName',
       'verticalDatumName', 'verticalMethodName', 'approvalMember',
       'markerName', 'horizontalMethodName', 'horizontalDatumName',
       'flagMemberName', 'surveyMemberName', 'site_no', 'siteDescription',
       'sitePriorityName', 'networkNames', 'stateName', 'countyName',
       'sitePermHousing', 'site_latitude', 'site_longitude', 'waterbody',
       'site_id', 'event_id', 'hwm_type_id', 'hwm_quality_id',
       'hwm_locationdescription', 'latitude_dd', 'longitude_dd', 'survey_date',
       'elev_ft', 'vdatum_id', 'vcollect_method_id', 'bank', 'marker_id',
       'hcollect_method_id', 'hwm_environment', 'flag_date', 'stillwater',
       'hdatum_id', 'flag_member_id', 'survey_member_id', 'uncertainty',
       'hwm_uncertainty', 'hwm_label', 'files', 'approval_id',
       'height_above_gnd', 'hwm_notes', 'siteZone'],
      dtype='object')

tidal station data from the Center for Operational Oceanographic Products and Services (CO-OPS)

list CO-OPS tidal stations

from stormevents.coops import coops_stations

stations = coops_stations()

        nws_id         x          y                          name state removed
nos_id                                                                         
1600012  46125  122.6250  37.750000                     QREB buoy           NaT
1611400  NWWH1 -159.3750  21.953125                    Nawiliwili    HI     NaT
1612340  OOUH1 -157.8750  21.312500                      Honolulu    HI     NaT
1612480  MOKH1 -157.7500  21.437500                      Mokuoloe    HI     NaT
1615680  KLIH1 -156.5000  20.890625       Kahului, Kahului Harbor    HI     NaT
        ...       ...        ...                           ...   ...     ...
9759394  MGZP4  -67.1875  18.218750                      Mayaguez    PR     NaT
9759938  MISP4  -67.9375  18.093750                   Mona Island           NaT
9761115  BARA9  -61.8125  17.593750                       Barbuda           NaT
9999530  FRCB6  -64.6875  32.375000  Bermuda, Ferry Reach Channel           NaT
9999531         -93.3125  29.765625        Calcasieu Test Station    LA     NaT

[363 rows x 6 columns]

list CO-OPS tidal stations within a region

from shapely.geometry import Polygon
from stormevents.coops import coops_stations_within_region

polygon = Polygon(...)

stations = coops_stations_within_region(region=polygon)

retrieve CO-OPS tidal data within a region

from datetime import datetime, timedelta

from shapely.geometry import MultiPolygon
from stormevents.coops import coops_data_within_region

polygon = MultiPolygon(...)

coops_data_within_region(region=polygon, start_date=datetime.now() - timedelta(days=2), end_date=datetime.now())

Related Projects

• searvey - https://github.com/pmav99/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/zacharyburnettNOAA/adcircpy

Acknowledgements

This code was initially written by @jreniel for adcircpy. Additionally, methodology for retrieving USGS high water marks data and CO-OPS tidal station data came from @moghimis and @Sergey.Vinogradov