Dataset for paper Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda "Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model" submitted to JAMES.

The dataset consists of the directory tree of .zarr archives. See Github repository for the description of the dataset. The directory tree is: <pre><code>├── eddy │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires ├── jet │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires</code></pre> Every individual dataset is a <code>.zarr</code> archive <code>eddy/jet</code> - configuration of the pyqg; eddy is default; See Ross2022 for description <code>hires.zarr</code> - high-resolution simulation at 256x256 grid <code>48/64/96</code> - resolution of the coarse models <code>lores.zarr</code> - low-resolution simulation <code>gauss.zarr</code>, <code>sharp.zarr</code> - training datasets for prediction of subgrid forcing obtained with Gaussian or Sharp filters <code>hires-gauss.zarr</code>, <code>hires-sharp.zarr</code> - high-resolution simulation projected onto coarse grid with Gaussian or Sharp filters The directory tree is split into small tar.gz files each representing a separate .zarr archive. Download any required parts of the dataset and unpack with: <strong>tar -xf *.tar.gz </strong> <strong>The directory tree will be restored automatically!</strong>