The National Oceanic and Atmospheric Association's National Data Buoy Center maintains marine monitoring and observation stations around the world^1. These stations report atmospheric, oceanographic, and other meterological data at regular intervals to the NDBC. Measurements are made available over HTTP through the NDBC's data service.

The ndbc-api is a python library that makes this data more widely accessible.

The ndbc-api is primarily built to parse whitespace-delimited oceanographic and atmospheric data distributed as text files for available time ranges, on a station-by-station basis^2. Measurements are typically distributed as utf-8 encoded, station-by-station, fixed-period text files. More information on the measurements and methodology are available on the NDBC website^3.

Please see the included example notebook for a more detailed walkthrough of the API's capabilities.

Installation

The ndbc-api can be installed via PIP:

pip install ndbc-api

Conda users can install the ndbc-api via the conda-forge channel:

conda install -c conda-forge ndbc-api

Finally, to install the ndbc-api from source, clone the repository and run the following command:

python setup.py install

Requirements

The ndbc-api has been tested on Python 3.6, 3.7, 3.8, 3.9, and 3.10. Python 2 support is not currently planned, but could be implemented based on the needs of the atmospheric research community.

The API uses synchronous HTTP requests to compile data matching the user-supplied parameters. The ndbc-api package depends on:

• requests>=2.10.0
• pandas
• bs4
• html5lib>=1.1
• xarray
• scipy

Development

If you would like to contribute to the growth and maintenance of the ndbc-api, please feel free to open a PR with tests covering your changes. The tests leverage pytest and depend on the above requirements, as well as:

• coveralls
• httpretty
• pytest
• pytest-cov
• pyyaml
• pyarrow

Breaking changes will be considered, especially in the current alpha state of the package on PyPi. As the API further matures, breaking changes will only be considered with new major versions (e.g. N.0.0).

Example

The ndbc-api exposes public methods through the NdbcApi class.

from ndbc_api import NdbcApi

api = NdbcApi()

The NdbcApi provides a unified access point for NDBC data. All methods for obtaining data, metadata, and locating stations are available using the api object. The get_data method is the primary method for accessing NDBC data, and is used to retrieve measurements from a given station over a specified time range. This method can request data from the NDBC HTTP Data Service or the THREDDS data service, and return the data as a pandas.DataFrame, xarray.Dataset or python dict object.

Data made available by the NDBC falls into two broad categories.

1. Station metadata
2. Station measurements

The api supports a range of public methods for accessing data from the above categories.

Station metadata

The api has five key public methods for accessing NDBC metadata.

1. The stations method, which returns all NDBC stations.
2. The nearest_station method, which returns the station ID of the nearest station.
3. The station method, which returns station metadata from a given station ID.
4. The available_realtime method, which returns hyperlinks and measurement names for realtime measurements captured by a given station.
5. The available_historical method, which returns hyperlinks and measurement names for historical measurements captured by a given station.

stations

# get all stations and some metadata as a Pandas DataFrame
stations_df = api.stations()
# parse the response as a dictionary
stations_dict = api.stations(as_df=False)

nearest_station

# specify desired latitude and longitude
lat = '38.88N'
lon = '76.43W'

# find the station ID of the nearest NDBC station
nearest = api.nearest_station(lat=lat, lon=lon)
print(nearest_station)

'tplm2'

radial_search

# specify desired latitude, longitude, radius, and units
lat = '38.88N'
lon = '76.43W'
radius = 100
units = 'km'

# find the station IDs of all NDBC stations within the radius
nearby_stations_df = api.radial_search(lat=lat, lon=lon, radius=radius, units=units)

'tplm2'

station

# get station metadata
tplm2_meta = api.station(station_id='tplm2')
# parse the response as a Pandas DataFrame
tplm2_df = api.station(station_id='tplm2', as_df=True)

available_realtime

# get all available realtime measurements, periods, and hyperlinks
tplm2_realtime = api.available_realtime(station_id='tplm2')
# parse the response as a Pandas DataFrame
tplm2_realtime_df = api.available_realtime(station_id='tplm2', as_df=True)

available_historical

# get all available historical measurements, periods, and hyperlinks
tplm2_historical = api.available_historical(station_id='tplm2')
# parse the response as a Pandas DataFrame
tplm2_historical_df = api.available_historical(station_id='tplm2', as_df=True)

Station measurements

The api has two public methods which support accessing supported NDBC station measurements.

1. The get_modes method, which returns a list of supported modes, corresponding to the data formats provided by the NDBC data service. For example, the adcp mode represents "Acoustic Doppler Current Profiler" measurements, providing information about ocean currents at different depths, while cwind represents "Continuous winds" data, offering high-frequency wind speed and direction measurements.

Note that not all stations provide the same set of measurements. The available_realtime and available_historical methods can be called on a station-by station basis to ensure a station has the desired data available, before building and executing requests with get_data.

2. The get_data method, which returns measurements of a given type for a given station.

get_modes

# get the list of supported meterological measurement modes
modes = api.get_modes()
print(modes)

[
    'adcp',
    'cwind',
    'ocean',
    'spec',
    'stdmet',
    'supl',
    'swden',
    'swdir',
    'swdir2',
    'swr1',
    'swr2'
]

The mode values above map directly to the identifiers used buy the NDBC. Desriptions for each mode are presented below:

• adcp: Acoustic Doppler Current Profiler measurements, providing information about ocean currents at different depths.
• cwind: Continuous winds data, offering high-frequency wind speed and direction measurements.
• ocean: Oceanographic data, including water temperature, salinity, and wave measurements.
• spec: Spectral wave data, providing detailed information about wave energy and direction.
• stdmet: Standard meteorological data, including air temperature, pressure, wind speed, and visibility.
• supl: Supplemental measurements, which can vary depending on the specific buoy and its sensors.
• swden: Spectral wave density data, providing information about the distribution of wave energy across different frequencies.
• swdir: Spectral wave direction data, indicating the primary direction of wave energy.
• swdir2: Secondary spectral wave direction data, capturing additional wave direction information.
• swr1: First-order spectral wave data, providing basic wave height and period information.
• swr2: Second-order spectral wave data, offering more detailed wave measurements.

get_data

# get all continuous wind (`cwind`) measurements for station tplm2
cwind_df = api.get_data(
    station_id='tplm2',
    mode='cwind',
    start_time='2020-01-01',
    end_time='2022-09-15',
)
# return data as a dictionary
cwind_dict = api.get_data(
    station_id='tplm2',
    mode='cwind',
    start_time='2020-01-01',
    end_time='2022-09-15',
    as_df=False
)
# get only the wind speed measurements
wspd_df = api.get_data(
    station_id='tplm2',
    mode='cwind',
    start_time='2020-01-01',
    end_time='2022-09-15',
    as_df=True,
    cols=['WSPD']
)
# get all standard meterological (`stdmet`) measurements for stations tplm2 and apam2
stdmet_df = api.get_data(
    station_ids=['tplm2', 'apam2'],
    mode='stdmet',
    start_time='2022-01-01',
    end_time='2023-01-01',
)
# get all (available) continuous wind and standard meterological measurements for stations tplm2 and apam2
# for station apam2, this is unavailable and will log an error but not affect the rest of the results.
stdmet_df = api.get_data(
    station_ids=['tplm2', 'apam2'],
    modes=['stdmet', 'cwind'],
    start_time='2022-01-01',
    end_time='2023-01-01',
)

More Information

Please see the included example notebook for a more detailed walkthrough of the API's capabilities.

Questions

If you have questions regarding the library please post them into
the GitHub discussion forum.