pyqg support parameterizations, which are functions that take a pyqg.Model and return an additional term to add to its potential vorticity tendency every timestep (or two terms to add to each velocity tendency, in which case we apply them to PV after taking their curl). Typically, parameterizations are used to account for the contribution of phenomena occuring at subgrid scales. This approach can be a computationally efficient way to improve the physical realism of simulations without needing to increase their spatial resolution (which can be very expensive).

Using predefined parameterizations

pyqg implements a number of predefined parameterizations (see Parameterizations for a full list). You can use these in a pyqg.Model as follows:

param = pyqg.BackscatterBiharmonic(smag_constant=0.1, back_constant=0.95)
model = pyqg.QGModel(parameterization=param)

Note that parameterizations either target the tendencies of potential vorticity \(q\) or the velocities \(u\) and \(v\). If you have two parameterizations with the same target, you can add them together, even as a weighted sum. If they have different targets, you can still use both, but they must be passed in as separate q_parameterization and uv_parameterization arguments:

param1 = pyqg.Smagorinsky() # targets uv
param2 = pyqg.ZannaBolton2020() # also targets uv
good_model = pyqg.QGModel(parameterization=param1 + 0.25*param2) # this works!

param3 = pyqg.BackscatterBiharmonic() # targets q
bad_model = pyqg.QGModel(parameterization=param1 + param3) # this will error!

# do this instead to combine parameterizations of different types
good_model2 = pyqg.QGModel(uv_parameterization=param1, q_parameterization=param3)

Defining new parameterizations

To define a new parameterization, you have two options. The first is just to define a Python function which takes a single argument (the model) and returns either a single real array of size (nz, ny, nz) if it targets \(q\) or an iterable of two such arrays if it targets \(u\) and \(v\). This can then be passed to the model using the type-specific arguments:

# These parameterizations just add random noise, but with the right shape
noisy_q_param = lambda model: np.random.normal(size=model.q.shape)
noisy_uv_param = lambda model: np.random.normal(size=(2, *model.u.shape))

model1 = pyqg.QGModel(q_parameterization=noisy_q_param)
model2 = pyqg.QGModel(uv_parameterization=noisy_uv_param)

The second (and usually better) option is to define a subclass of pyqg.UVParameterization or pyqg.QParameterization with a new definition of __call__:

class NoisyQParam(pyqg.QParameterization):
    def __init__(self, scale):
        self.scale = scale

    def __call__(self, model):
        return np.random.normal(size=model.q.shape) * self.scale

If you would like to make your parameterization available for others to test, please consider Contributing your parameterization to pyqg.

Parameterization diagnostics

Parameterizations of potential vorticity affect how energy is redistributed across scales according to the following formula:

\[\left(\frac{\partial E(k, l)}{\partial t}\right)^{\text{param}} = -\frac{1}{H}\sum_{n=1}^N H_n\mathbb{R}\left[\hat{\psi}_n^*\hat{\dot{q}}^{\text{param}}_n\right],\]

The contribution of velocity parameterizations is analogous, except with \(\hat{\dot{q}}^{\text{param}}\) replaced by the curl of the velocity tendency terms in spectral space. This term is made available in the diagnostics under paramspec.

In the case of a quasi-geostrophic model, the paramspec can be decomposed into two terms which represent its contribution to the kinetic and available potential energy tendencies:

\[\begin{split}\begin{align} \left(\frac{\partial \mathrm{KE}(k, l)}{\partial t}\right)^{\text{param}} &= \frac{1}{H}\sum_{n=1}^N H_n\mathbb{R}\left[\kappa^2\hat{\psi}_n^* \left(\mathbf{A}\hat{\dot{\mathbf{q}}}^{\text{param}}\right)_n\right] \\ \left(\frac{\partial \mathrm{APE}(k, l)}{\partial t}\right)^{\text{param}} &= -\frac{1}{H}\sum_{n=1}^N H_n\mathbb{R}\left[\hat{\psi}_n^* \left(\mathbf{SA}\hat{\dot{\mathbf{q}}}^{\text{param}}\right)_n\right] \end{align}\end{split}\]

where \(\mathbf{A}(\mathbf{k}) = (\mathbf{S} - \kappa^2\mathbf{I})^{-1}\) and \(\mathbf{S}\) is the model’s stretching matrix (more details here).

We make these terms available in the diagnostics under paramspec_KEflux and paramspec_APEflux, respectively. When comparing the KE and APE fluxes of parameterized and unparameterized models, it may make sense to do so after adding these terms to the raw KEflux and APEflux values.

Evaluating subgrid parameterizations

As many parameterizations attempt to account for missing physics due to low resolution, we provide several helper methods for evaluating them.

Assume we have run a high-resolution model and both parameterized and unparameterized low-resolution models. We provide helper methods to compare the root mean squared difference in their resulting diagnostics (properly adding, e.g., KEflux and paramspec_KEflux), and even compute similarity metrics describing how much closer each of the parameterized model’s diagnostics are to those of the high-resolution model as compared to those of the low-resolution model:

from pyqg.diagnostic tools import diagnostic_differences, diagnostic_similarities

m_highres = pyqg.QGModel(nx=256)
m_lowres = pyqg.QGModel(nx=64)
m_param = pyqg.QGModel(nx=64, parameterization=pyqg.BackscatterBiharmonic())
[ for m in [m_highres, m_lowres, m_param]]

highres_lowres_diffs = diagnostic_differences(m_highres, m_lowres)
highres_param_diffs = diagnostic_differences(m_highres, m_param)

param_similarity = diagnostic_similarities(m_param,

The target does not need to be a high-resolution model, but regardless, similarity scores near 1 indicate that the parameterization’s diagnostics are much closer to the target than the baseline, while scores below 0 indicate they are further from the target than the baseline.

Contributing your parameterization to pyqg

We encourage contributions of parameterizations to pyqg for others to test. To add yours, please:

  1. Define it as a subclass of pyqg.UVParameterization or pyqg.QParameterization as described above.

  2. Add the code either to pyqg/ or a new file imported in pyqg/

  3. Write a test ensuring it can be evaluated for the appropriate model classes.

  4. Create or update a notebook in docs/examples to illustrate its effects or compare it to other parameterizations (optional but encouraged).

  5. Create a pull request following the normal development workflow.