m2lines

L96_demo

Lorenz 1996 two time-scale model for learning machine learning

pyqg_parameterization_benchmarks

Public-facing git repository for JAMES submission, "Benchmarking of machine learning ocean parameterizations in an idealized model"

pyqg_generative

Generative models (GAN, VAE) for parameterization of mesoscale eddies in pyqg ocean model.

gz21_ocean_momentum

Stochastic-Deep Learning Parameterization of Ocean Momentum Forcing

convection-parameterization-in-CAM

Deployment of the convection parameterization scheme (previously from within SAM) deployed into the CAM model

m2lines.github.io

m2lines.github.io Hugo website source

hub

Implementation-ZB20

Contains source code and Figure plotting functions of ZB20 parameterization implemented to the MOM6 ocean model

Neural_nework_parameterization

Code used in:"Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision"

ocean_emulators

seaice_DA-ML

momentum_parameterization

Code and associated data with neural network parameterization of momentum

subgrid

subgrid_parameterization

swe_stochastic_param

Shallow Water Equations on a square basin with stochastic deep learning parameterization of subgrid momentum forcing

CAM-ML

Community Atmosphere Model

data-gallery

EquationDisco

M²LInES equation discovery package.

MLwithQG

Jupyter book tutorial on Ross 2022 machine learning subgrid parametrizations

pyqg_hackathon_examples

Examples for the M2LiNES hackathon!