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¶
Plot data from the command line¶
Running the following from the command line will plot the variable significant wave height
from the WAVEWATCH III at_4m grid. Note that the time of day (in this case, 15:00) is
separated from the date with a T (i.e. times can be given as YYYY-MM-DDTHH)
$ ww3 plot --grid=at_4m --data-var=swh "2010-09-15T15"
Plot data from Python¶
This example is similar to the previous but uses the bmi_wavewatch3 Python interface.
>>> 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()
