jaxparrow implements a novel approach based on a variational formulation to compute the inversion of the cyclogeostrophic balance.
It leverages the power of JAX, to efficiently solve the inversion as an optimization problem. Given the Sea Surface Height (SSH) field of an ocean system, jaxparrow estimates the velocity field that best satisfies the cyclogeostrophic balance.
The package is Pip-installable:
pip install jaxparrowHowever, users with access to GPUs or TPUs should first install JAX separately in order to fully benefit from its high-performance computing capacities.
See JAX instructions.
By default, jaxparrow will install a CPU-only version of JAX if no other version is already present in the Python environment.
Two functions are directly available from jaxparrow:
geostrophycomputes the geostrophic velocity field (returns two2darray) from:- a SSH field (a
2darray), - the latitude and longitude at the T points (two
2darray), - an optional mask grid (one
2darray).
- a SSH field (a
cyclogeostrophycomputes the cyclogeostrophic velocity field (returns two2darray) from:- a SSH field (a
2darray), - the latitude and longitude at the T points (two
2darray), - an optional mask grid (one
2darray).
- a SSH field (a
Because jaxparrow uses C-grids the velocity fields are represented on two grids (U and V), and the SSH on one grid (T).
In a Python script, assuming that the input grids have already been initialised / imported, it would resort to:
from jaxparrow import cyclogeostrophy, geostrophy
u_geos, v_geos = geostrophy(ssh_t=ssh,
lat_t=lat, lon_t=lon,
mask=mask)
u_cyclo, v_cyclo = cyclogeostrophy(ssh_t=ssh,
lat_t=lat, lon_t=lon,
mask=mask)To vectorise the application of the geostrophy and cyclogeostrophy functions across an added time dimension, one aims to utilize vmap.
However, this necessitates avoiding the use of np.ma.masked_array.
Hence, our functions accommodate mask array as parameter to effectively consider masked regions.
By default, the cyclogeostrophy function relies on our variational method.
Its method argument provides the ability to use an iterative method instead, either the one described by Penven et al., or the one by Ioannou et al..
Additional arguments also give a finer control over the three approaches hyperparameters.
See jaxparrow API documentation for more details.
Notebooks are available as step-by-step examples.
jaxparrow is also available from the command line:
jaxparrow --conf_path conf.ymlThe YAML configuration file conf.yml instruct where input netCDF files are locally stored, and how to retrieve variables and coordinates from them.
It also provides the path of the output netCDF file. Optionally, it can specify which cyclogeostrophic approach should be applied and its hyperparameters.
An example configuration file detailing all the required and optional entries can be found here.
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