1k citations · 1.4k across the 12 of their papers we have counts for
12 papers
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…
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…
Neural Compression of Atmospheric States
Piotr Mirowski, David Warde-Farley, Mihaela Rosca +7
Atmospheric states derived from reanalysis comprise a substantial portion of weather and climate simulation outputs. Many stakeholders -- such as researchers, policy makers, and in…
Machine learning emulation of precipitation from km-scale UK regional climate simulations using a diffusion model
Henry Addison, Elizabeth Kendon, Suman Ravuri +2
High-resolution climate simulations are valuable for understanding climate change impacts. This has motivated use of regional convection-permitting climate models (CPMs), but these…
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Suman Ravuri, Mélanie Rey, Shakir Mohamed +1
Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge. It is especially difficult for certain model cl…
GraphCast: Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson +15
Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute reso…
Machine learning emulation of a local-scale UK climate model
Henry Addison, Elizabeth Kendon, Suman Ravuri +2
Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e…
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
Suman Ravuri, Karel Lenc, Matthew Willson +17
Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socio-economic needs of many sectors reliant on weather-de…
A review of radar-based nowcasting of precipitation and applicable machine learning techniques
Rachel Prudden, Samantha Adams, Dmitry Kangin +4
A 'nowcast' is a type of weather forecast which makes predictions in the very short term, typically less than two hours - a period in which traditional numerical weather prediction…
Classification Accuracy Score for Conditional Generative Models
Suman Ravuri, Oriol Vinyals
Deep generative models (DGMs) of images are now sufficiently mature that they produce nearly photorealistic samples and obtain scores similar to the data distribution on heuristics…
Learning Implicit Generative Models with the Method of Learned Moments
Suman Ravuri, Shakir Mohamed, Mihaela Rosca +1
We propose a method of moments (MoM) algorithm for training large-scale implicit generative models. Moment estimation in this setting encounters two problems: it is often difficult…