455 citations · 930 across the 30 of their papers we have counts for
32 papers
reGRAF: a global MPAS reforecast data set with convection-allowing refinements over the US and Europe
Thomas M Hamill, Akshay Subramaniam, Montgomery Flora +8
NVIDIA and The Weather Company (TWC) have generated a data set of reforecasts from TWC's GRAF (Global high-Resolution Atmospheric Forecasting) model, a version of the National Cent…
Anomalous Diffusion of Tropical Cyclones Observed in Huge Ensembles of Hindcasts
Abdoul R. Zeba, William D. Collins, Ankur Mahesh +5
We examine whether tropical cyclones (TCs) obey ordinary Brownian or anomalous diffusion using a huge ensemble (HENS) of hindcasts for summer 2023. Anomalous diffusion has been inf…
Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet
Zeyuan Hu, Akshay Subramaniam, Noel Keen +9
Kilometer-scale convection shapes precipitation extremes, tropical organization, and cloud feedbacks, but most global atmospheric models approximate these processes at 25-100 km re…
Towards accurate extreme event likelihoods from diffusion model climate emulators
Peter Manshausen, Noah Brenowitz, Julius Berner +2
ML climate model emulators are useful for scenario planning and adaptation, allowing for cost-efficient experimentation. Recently, the diffusion model Climate in a Bottle (cBottle)…
Surface temperature extremes produced by huge machine learning hindcasts of summer 2023
Mark Risser, Ankur Mahesh, William D. Collins +6
The summer of 2023 brought record-breaking heat extremes across the globe. In this work, we investigate how much worse these extremes might have become, given identical large-scale…
Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Ankur Mahesh, William D. Collins, Travis A. O'Brien +10
The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typic…
HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts
Aayush Gupta, Akshay Subramaniam, Michael S. Pritchard +6
AI weather models now rival leading numerical weather prediction (NWP) systems in medium-range skill. However, almost all still rely on NWP data assimilation (DA) to provide initia…
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh +7
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed…
Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere
Noah D. Brenowitz, Tao Ge, Akshay Subramaniam +7
Climate modeling is reaching unprecedented resolution, producing petabytes of data. AI climate model emulators offer a path to computationally cheap analysis, enabling new scientif…
Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling
Jaideep Pathak, Yair Cohen, Piyush Garg +8
Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme w…
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
Ankur Mahesh, William Collins, Boris Bonev +13
Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems pr…
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
Ankur Mahesh, William Collins, Boris Bonev +12
In Part I, we created an ensemble based on Spherical Fourier Neural Operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multipl…
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Peter Manshausen, Yair Cohen, Peter Harrington +7
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation…
ACE: A fast, skillful learned global atmospheric model for climate prediction
Oliver Watt-Meyer, Gideon Dresdner, Jeremy McGibbon +9
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a…
Earth Virtualization Engines -- A Technical Perspective
Torsten Hoefler, Bjorn Stevens, Andreas F. Prein +17
Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide intera…
Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling
Morteza Mardani, Noah Brenowitz, Yair Cohen +10
The state of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a g…
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Boris Bonev, Thorsten Kurth, Christian Hundt +4
Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scie…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization
Adam Rupe, Karthik Kashinath, Nalini Kumar +1
Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal represent…
DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting
Tao Ge, Jaideep Pathak, Akshay Subramaniam +1
Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however, is based on supervision on…
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Thorsten Kurth, Shashank Subramanian, Peter Harrington +6
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits…
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Jaideep Pathak, Shashank Subramanian, Peter Harrington +10
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.2…
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh +2
There is growing interest in data-driven weather prediction (DDWP), for example using convolutional neural networks such as U-NETs that are trained on data from models or reanalysi…
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath +3
Simulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields…