bmi-wavewatch3 0.1.0b3.dev0

Fetch WaveWatch3 data.

WAVEWATCH III data in Python

About

The bmi_wavewatch3 Python package provides both a command line interface and a programming interface for downloading and working with WAVEWATCH III data.

bmi_wavewatch3 provides access to the following raster data sources,

• 30 year wave hindcast Phase 1

• 30 year wave hindcast Phase 2

• Production hindcast Singlegrid

• Production hindcast Multigrid

All data sources provide both global and regional grids.

Installation

bmi_wavewatch3 can be installed by running pip install bmi-wavewatch3. It requires Python >= 3.8 to run.

If you simply can’t wait for the latest release, you can install bmi_wavewatch3 directly from GitHub,

$ pip install git+https://github.com/csdms/bmi-wavewatch3

bmi_wavewatch3 is also available through conda, conda install bmi-wavewatch3 -c conda-forge.

Usage

To get started, you can download WAVEWATCH III data by date with the ww3 command (use ww3 –help to print a brief message),

$ ww3 fetch "2010-05-22"

You can also do this through Python,

>>> from bmi_wavewatch3 import WaveWatch3
>>> WaveWatch3.fetch("2010-05-22")

The bmi_wavewatch3 package provides the WaveWatch3 class for downloading data and presenting it as an xarray Dataset.

>>> from bmi_wavewatch3 import WaveWatch3
>>> ww3 = WaveWatch3("2010-05-22")
>>> ww3.data
<xarray.Dataset>
...

Use the inc method to advance in time month-by-month,

>>> ww3.date
'2010-05-22'
>>> ww3.inc()
'2010-06-22'
>>> ww3.data.time
<xarray.DataArray 'time' ()>
array('2010-06-01T00:00:00.000000000', dtype='datetime64[ns]')
...

This will download new datasets as necessary and load the new data into the data attribute.

Note

If the new data are not cached on you computer, you will notice a delay while the new data are download. If the lazy flag is set, the download will only occur once you try to access the data (i.e. ww3.data), otherwise the data are downloaded as soon as the date is set.

Example

>>> from bmi_wavewatch3 import WaveWatch3
>>> ww3 = WaveWatch3("2009-11-08")

The data can be accessed as an xarray Dataset through the data attribute.

>>> ww3.data
<xarray.Dataset>
Dimensions:     (step: 241, latitude: 311, longitude: 720)
Coordinates:
    time        datetime64[ns] 2009-11-01
  * step        (step) timedelta64[ns] 0 days 00:00:00 ... 30 days 00:00:00
    surface     float64 1.0
  * latitude    (latitude) float64 77.5 77.0 76.5 76.0 ... -76.5 -77.0 -77.5
  * longitude   (longitude) float64 0.0 0.5 1.0 1.5 ... 358.0 358.5 359.0 359.5
    valid_time  (step) datetime64[ns] dask.array<chunksize=(241,), meta=np.ndarray>
Data variables:
    dirpw       (step, latitude, longitude) float32 dask.array<chunksize=(241, 311, 720), meta=np.ndarray>
    perpw       (step, latitude, longitude) float32 dask.array<chunksize=(241, 311, 720), meta=np.ndarray>
    swh         (step, latitude, longitude) float32 dask.array<chunksize=(241, 311, 720), meta=np.ndarray>
    u           (step, latitude, longitude) float32 dask.array<chunksize=(241, 311, 720), meta=np.ndarray>
    v           (step, latitude, longitude) float32 dask.array<chunksize=(241, 311, 720), meta=np.ndarray>
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP
    history:                 2022-06-08T16:08 GRIB to CDM+CF via cfgrib-0.9.1...

The step attribute points to the current time slice into the data (i.e number of three hour increments since the start of the month),

>>> ww3.step
56
>>> ww3.data.swh[ww3.step, :, :].plot()

Credits

Development Lead

• Eric Hutton (@mcflugen)

Contributors

None yet. Why not be the first?

0.1.0b2 (unreleased)

• Nothing changed yet.

0.1.0b1 (2022-06-09)