24 citations · 44 across the 13 of their papers we have counts for
13 papers
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice
Zachary I Espinosa, Nathaniel Cresswell-Clay, William Yik +6
While AI has shown remarkable promise in atmospheric and meteorological forecasting, accurately simulating other components of the Earth system with AI remains an active frontier.…
ShardTensor: Domain Parallelism for Scientific Machine Learning
Corey Adams, Peter Harrington, Akshay Subramaniam +4
Scientific Machine Learning (SciML) faces unique challenges for extreme-resolution data, with mitigations that often fail to scale or degrade the accuracy of trained models. While…
Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting
Jean Kossaifi, Nikola Kovachki, Morteza Mardani +15
The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fun…
Learning Accurate Storm-Scale Evolution from Observations
Jaideep Pathak, Mohammad Shoaib Abbas, Peter Harrington +10
Accurate short-term prediction of clouds and precipitation is critical for severe weather warnings, aviation safety, and renewable energy operations. Forecasts at this timescale ar…
MoWE : A Mixture of Weather Experts
Dibyajyoti Chakraborty, Romit Maulik, Peter Harrington +3
Data-driven weather models have recently achieved state-of-the-art performance, yet progress has plateaued in recent years. This paper introduces a Mixture of Experts (MoWE) approa…
Hierarchical Conditional Multi-Task Learning for Streamflow Modeling
Shaoming Xu, Arvind Renganathan, Ankush Khandelwal +9
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning m…
Comprehensive Performance Modeling and System Design Insights for Foundation Models
Shashank Subramanian, Ermal Rrapaj, Peter Harrington +6
Generative AI, in particular large transformer models, are increasingly driving HPC system design in science and industry. We analyze performance characteristics of such transforme…
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…
Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction
Jared D. Willard, Peter Harrington, Shashank Subramanian +3
The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior ski…
A Practical Probabilistic Benchmark for AI Weather Models
Noah D. Brenowitz, Yair Cohen, Jaideep Pathak +6
Since the weather is chaotic, forecasts aim to predict the distribution of future states rather than make a single prediction. Recently, multiple data driven weather models have em…