pyshqg 1.0.1

QG Model in spherical harmonics

pyshqg

pyshqg is a python solver for the Marshal and Molteni (1993) quasi-geostrophic (QG) model. QG models express the conservation of potential vorticity over time and are meant to describe the large-scale circulation of the atmosphere under specific hypotheses. This QG model is very special, because it is expressed in spherical harmonics and because it encapsulates complex physical processes.

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Installation

Install using pip:

$ pip install pyshqg

More details can be found on this page.

Usage

Here is a sneak peak at how to use the package. First import all the machinery.

>>> import numpy as np
>>>
>>> from pyshqg.backend.numpy_backend import NumpyBackend as Backend
>>> from pyshqg.preprocessing.reference_data import load_test_data
>>> from pyshqg.core.constructors import construct_model, construct_integrator

Load the data and create the model and integrator.

>>> ds_test = load_test_data(internal_truncature=21, grid_truncature=31)
>>> backend = Backend(floatx='float64')
>>> model = construct_model(backend, ds_test.config)
>>> rk4 = construct_integrator(ds_test.config['rk4_integration'], model)

Perform one integration step and test against the test data.

>>> state = model.model_state(ds_test.spec_q.to_numpy())
>>> fwd_state = rk4.forward(state)
>>>
>>> def rms(x):
...     return np.sqrt(np.mean(np.square(x)))
>>>
>>> def rmse(x, y):
...     return rms(x-y)
>>>
>>> rms_ref = rms(ds_test.spec_rk4.to_numpy())
>>> rms_diff = rmse(
...     backend.to_numpy(fwd_state['spec_q']),
...     ds_test.spec_rk4.to_numpy(),
... )
>>> assert rms_diff < 1e-6 * rms_ref

Run the model for a full trajectory and convert the output to an xarray.Dataset.

>>> state = model.model_state(ds_test.spec_q.to_numpy())
>>> trajectory = rk4.run(
...     state,
...     t_start=0,
...     num_snapshots=8,
...     num_steps_per_snapshot=10,
...     variables=('q', 'psi'),
... )
>>> trajectory
<xarray.Dataset> Size: 14MB
Dimensions:  (time: 9, batch: 16, level: 3, lat: 32, lon: 64)
Coordinates:
  * time     (time) int64 72B 0 18000 36000 54000 ... 90000 108000 126000 144000
  * lat      (lat) float64 256B -85.76 -80.27 -74.74 ... 74.74 80.27 85.76
  * lon      (lon) float64 512B 180.0 185.6 191.2 196.9 ... 163.1 168.8 174.4
Dimensions without coordinates: batch, level
Data variables:
    q        (time, batch, level, lat, lon) float64 7MB -0.000298 ... -0.0001269
    psi      (time, batch, level, lat, lon) float64 7MB 1.445e+08 ... -1.38e+07

More details can be found on this page.

Aknowledgements

This python package is based on an original implementation of the model in Fortran written at LMD by XXX.

Todo-list

• make higher resolution gif animation
• make more example notebooks (see list below)
• fill in the documentation sections
• fill in docstrings, in particular with the convention used in the docs
• publish first version on pypi and conda-forge
• write pytorch/jax backends
• include unit tests in github CI
• apply linting and include it in github CI

Potential example notebooks:

• one notebook for each term of the QG model
• how to compute the forecast skill T15 vs T31 (as illustration)
• how to use tf backend to compute the grad
• gradient test for tf backend
• adjoint test for tf backend
• how to define a NN-based model error correction for the T21 and train it with dummy data