Python interfaces for observational data surrounding named storm events
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
... ... ... ... ... ... ... ... ...
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.write('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 FloodEvent
flood = FloodEvent(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 FloodEvent
flood = FloodEvent(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 removed geometry
nos_id
1600012 46125 QREB buoy NaT POINT (122.62500 37.75000)
1611400 NWWH1 Nawiliwili HI NaT POINT (-159.37500 21.95312)
1612340 OOUH1 Honolulu HI NaT POINT (-157.87500 21.31250)
1612480 MOKH1 Mokuoloe HI NaT POINT (-157.75000 21.43750)
1615680 KLIH1 Kahului, Kahului Harbor HI NaT POINT (-156.50000 20.89062)
... ... ... ... ... ...
9759394 MGZP4 Mayaguez PR NaT POINT (-67.18750 18.21875)
9759938 MISP4 Mona Island NaT POINT (-67.93750 18.09375)
9761115 BARA9 Barbuda NaT POINT (-61.81250 17.59375)
9999530 FRCB6 Bermuda, Ferry Reach Channel NaT POINT (-64.68750 32.37500)
9999531 Calcasieu Test Station LA NaT POINT (-93.31250 29.76562)
[363 rows x 5 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('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/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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