pyqg Subgrid Forcing Datasets

<strong>pyqg Subgrid Forcing Datasets</strong> This repository contains a set of zarr datasets which (when uncompressed) contain snapshots from different series of <code>pyqg.QGModel</code> simulations run with different sets of parameters (one which produces isotropic eddies, stored in <code>eddy/</code>, and one which produces strong jets, stored in <code>jet/</code>. These parameters are stored as JSON in each dataset's metadata as the <code>pyqg_params</code> attribute. Descriptions of each dataset: <code>low_res.zarr</code> contains snapshots saved every 1000 hours from 5 simulations run for 10 years at <code>nx=64</code> (which is eddy-permitting but not resolving) <code>high_res.zarr</code> contains similar snapshots but for simulations at <code>nx=256</code> (which is fully eddy-resolving) <code>forcing{1,2,3}.zarr</code> contain subgrid forcing variables (described below) and coarsened state variables which can be used to predict them for 275 simulations. Each <code>forcing</code> dataset is computed using a different filtering and coarse-graining operator: <code>forcing1</code> is computed by truncating high-frequency potential vorticity (PV) modes in spectral space, then applying the sharp, scale-selective spectral filter described here <code>forcing2</code> is computed by truncating high-frequency PV modes in spectral space, then applying a Gaussian spectral filter used here <code>forcing3</code> is computed by applying a diffusive filter from GCM-Filters to the PV in real space, then averaging in real space Descriptions and units for the variables in each dataset are stored in metadata which can be viewed when the files are loaded. More details (and links to datasets in different configurations) can be found at https://github.com/m2lines/pyqg_parameterization_benchmarks.