Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Abstract

Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some existing stochastic parameterizations utilize data-driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and sub-grid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate timescales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both timescales, and the networks closely reproduce the spatio-temporal correlations and regimes of the Lorenz '96 system. We also find that in general those models which produce skillful forecasts are also associated with the best climate simulations.

arXiv commentSubmitted to Journal of Advances in Modeling Earth Systems (JAMES)

References in corpus (14)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Auto-Encoding Variational Bayes
• Conditional Generative Adversarial Nets
• Generative Adversarial Networks
• Deep learning to represent sub-grid processes in climate models
• Stochastic Parameterization: Towards a new view of Weather and Climate Models
• Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
• Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
• A discrete approach to stochastic parametrization and dimensional reduction in nonlinear dynamics
• Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction
• Statistical and Dynamical Properties of Covariant Lyapunov Vectors in a Coupled Atmosphere-Ocean Model - Multiscale Effects, Geometric Degeneracy, and Error Dynamics
• Controlling Unpredictability with Observations in the Partially Observed Lorenz '96 Model
• A Large-Scale Study on Regularization and Normalization in GANs

Cited by in corpus (32)

• Will Artificial Intelligence supersede Earth System and Climate Models?
• Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
• Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network
• Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
• On closures for reduced order models $-$ A spectrum of first-principle to machine-learned avenues
• The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences
• Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation
• Deep Learning Based Cloud Cover Parameterization for ICON
• On the modern deep learning approaches for precipitation downscaling
• Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations
• Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning
• Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
• Controlled abstention neural networks for identifying skillful predictions for regression problems
• A topological perspective on weather regimes
• Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation
• A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning
• Combining data assimilation and machine learning to estimate parameters of a convective-scale model
• Data-driven stochastic Lie transport modelling of the 2D Euler equations
• Resampling with neural networks for stochastic parameterization in multiscale systems
• A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization
• Controlled abstention neural networks for identifying skillful predictions for classification problems
• Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model
• CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
• Development of an offline and online hybrid model for the Integrated Forecasting System
• CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
• Subgrid-scale parametrization of unresolved scales in forced Burgers equation using Generative Adversarial Networks (GAN)
• Heterogeneity of the Attractor of the Lorenz '96 Model: Lyapunov Analysis, Unstable Periodic Orbits, and Shadowing Properties
• CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation
• A fast time-stepping strategy for dynamical systems equipped with a surrogate model
• Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets
• Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
• A Short Review on Data Modelling for Vector Fields