Explanipedia

Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model Open

Gan Zhang, M. S. V. RAO, Janni Yuval, Ming Zhao · 2025

Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using neural general c…

Singularity image for ClimSim-Online Open

Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta , et al. · 2025

This is a singularity image for climsim-online: https://github.com/leap-stc/climsim-online It can be used to launch E3SM-MMF climate simulations or the hybrid physics-machine-learning variants (by replacing the MMF cloud-resolving calculat…

Advancing Seasonal Prediction of Tropical Cyclone Activity with a Hybrid AI-Physics Climate Model Open

Gan Zhang, M. S. V. RAO, Janni Yuval, Ming Zhao · 2025

Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using Neural General C…

Solar Geoengineering Strategies Based on Reinforcement Learning Open

Heng Quan, Daniel D. B. Koll, Nicholas J. Lutsko, Janni Yuval · 2025

Data Driven Equation Discovery Reveals Non-linear Reinforcement Learning in Humans Open

Amit Goldenberg, Kyle J. LaFollette, Janni Yuval, Roey Schurr, David Melnikoff · 2025

Computational models of reinforcement learning (RL), have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, …

Data Driven Equation Discovery Reveals Non-linear Reinforcement Learning in Humans Open

Amit Goldenberg, Kyle J. LaFollette, Janni Yuval, Roey Schurr, David Melnikoff · 2025

Computational models of reinforcement learning (RL), have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, …

Learning Machine Learning with Lorenz-96 Open

Dhruv Balwada, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Berner , et al. · 2024

NeuralGCM precipitation checkpoints Open

Janni Yuval, Ian Langmore, Dmitrii Kochkov, Stephan Hoyer · 2024

Model checkpoints for NeuralGCM. See the NeuralGCM documentation for usage instructions.

Neural general circulation models for weather and climate Open

Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Nørgaard, Jamie Smith , et al. · 2024

General circulation models (GCMs) are the foundation of weather and climate prediction 1,2 . GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes su…

Atmospheric circulation to constrain subtropical precipitation projections Open

Rei Chemke, Janni Yuval · 2024

Accurately assessing future changes in precipitation presents one of the greatest challenges of climate change. A leading source of uncertainty in precipitation predictions stems from potential future changes in atmospheric circulation. Sp…

Climate-invariant machine learning Open

Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng , et al. · 2024

Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller…

Learning Machine Learning with Lorenz-96 Open

Dhruv Balwada, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Brener , et al. · 2023

Atmospheric circulation to constrain subtropical precipitation projections Open

Rei Chemke, Janni Yuval · 2023

Accurately assessing future precipitation changes is one of the largest challenges of climate change. A leading source of uncertainty in precipitation predictions stems from potential fu10 ture changes in atmospheric circulation ^{1, 2<…}

Neural General Circulation Models for Weather and Climate Open

Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Nørgaard, Jamie Smith , et al. · 2023

General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such a…

Data Driven Equation Discovery Reveals Non-linear Reinforcement Learning in Humans Open

Kyle J. LaFollette, Janni Yuval, Roey Schurr, David Melnikoff, Amit Goldenberg · 2023

Computational models of reinforcement learning (RL), have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, …

Data Driven Equation Discovery Reveals Non-linear Reinforcement Learning in Humans Open

Kyle J. LaFollette, Janni Yuval, Roey Schurr, David Melnikoff, Amit Goldenberg · 2023

Computational models of reinforcement learning (RL), have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, …

Climate-Invariant Machine Learning Open

Tom Beucler, Michael S. Pritchard, Liran Peng, Janni Yuval · 2023

The "Climate-Invariant Machine Learning" manuscript's accompanying data is organized into two folders: "CIML_Fig_Data_v2.zip" contains the data necessary to reproduce all the manuscript's figures by running the Jupyter notebook at this lin…

Climate-Invariant Machine Learning Open

Tom Beucler, Michael S. Pritchard, Liran Peng, Janni Yuval · 2023

The "Climate-Invariant Machine Learning" manuscript's accompanying data is organized into two folders: "CIML_Fig_Data_v2.zip" contains the data necessary to reproduce all the manuscript's figures by running the Jupyter notebook at this lin…

Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere Open

Janni Yuval, Paul A. O’Gorman · 2023

Attempts to use machine learning to develop atmospheric parameterizations have mainly focused on subgrid effects on temperature and moisture, but subgrid momentum transport is also important in simulations of the atmospheric circulation. H…

Non‐Local Parameterization of Atmospheric Subgrid Processes With Neural Networks Open

Peidong Wang, Janni Yuval, Paul A. O’Gorman · 2022

Subgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that machine‐learning (ML) parameterizations based …

The intensification of winter mid-latitude storm tracks in the Southern Hemisphere Open

Rei Chemke, Yi Ming, Janni Yuval · 2022

Improving Cardiovascular Disease Prediction Using Automated Coronary Artery Calcium Scoring from Existing Chest CTs Open

Noam Barda, Noa Dagan, Amos Stemmer, Janni Yuval, Eitan Bachmat , et al. · 2022

Non-local parameterization of atmospheric subgrid processes with neural networks Open

Peidong Wang, Janni Yuval, Paul A. O’Gorman · 2022

Subgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that machine-learning (ML) parameterizations based …

Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES) Open

Peidong Wang, Janni Yuval, Paul A. O’Gorman · 2021

Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES). Detailed description of the files in README.txt.

Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES) Open

Peidong Wang, Janni Yuval, Paul A. O’Gorman · 2021

Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES). Detailed description of the files in README.txt.

Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2021 submit to JAMES) Open

Peidong Wang, Janni Yuval, Paul A. O’Gorman · 2021

Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2021 submit to JAMES). Detailed description of the files in README.txt.

Climate-Invariant Machine Learning Open

Tom Beucler, Michael S. Pritchard, Janni Yuval, Ankitesh Gupta, Liran Peng , et al. · 2021

Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller…

Climate-Invariant Machine Learning Open

Tom Beucler, Michael S. Pritchard, Liran Peng, Janni Yuval · 2021

The "Climate-Invariant Machine Learning" manuscript's accompanying data is organized in three folders: "CIML_Brief_Guide_Data" contains the data necessary to run the Jupyter notebook at this link. "CIML_Fig_Data" contains the data necessar…

Call for Papers on Machine Learning and Earth System Modeling Open

Janni Yuval, Mike Pritchard, Pierre Gentine, Laure Zanna, Jiwen Fan · 2021

Contributions are invited to a new journal special collection on the use of new machine learning methodologies and applications of machine learning to Earth system modeling.

Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution Open

Paul A. O’Gorman, Ziwei Li, William R. Boos, Janni Yuval · 2021

Projections of precipitation extremes in simulations with global climate models are very uncertain in the tropics, in part because of the use of parameterizations of deep convection and model deficiencies in simulating convective organizat…

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