bmi-wavewatch3 0.1.0b3.dev0

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.

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()

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)