Matthew Chantry
Cited by
Cited by
Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI
M Chantry, H Christensen, P Dueben, T Palmer
Philosophical Transactions of the Royal Society A 379 (2194), 20200083, 2021
Universal continuous transition to turbulence in a planar shear flow
M Chantry, LS Tuckerman, D Barkley
Journal of Fluid Mechanics 824, R1, 2017
Patterns in wall-bounded shear flows
LS Tuckerman, M Chantry, D Barkley
Annual Review of Fluid Mechanics 52, 343-367, 2020
Machine learning emulation of gravity wave drag in numerical weather forecasting
M Chantry, S Hatfield, P Dueben, I Polichtchouk, T Palmer
Journal of Advances in Modeling Earth Systems 13 (7), e2021MS002477, 2021
A generative deep learning approach to stochastic downscaling of precipitation forecasts
L Harris, ATT McRae, M Chantry, PD Dueben, TN Palmer
Journal of Advances in Modeling Earth Systems 14 (10), e2022MS003120, 2022
Genesis of streamwise-localized solutions from globally periodic traveling waves in pipe flow
M Chantry, AP Willis, RR Kerswell
Physical review letters 112 (16), 164501, 2014
Building tangent‐linear and adjoint models for data assimilation with neural networks
S Hatfield, M Chantry, P Dueben, P Lopez, A Geer, T Palmer
Journal of Advances in Modeling Earth Systems 13 (9), e2021MS002521, 2021
Turbulent–laminar patterns in shear flows without walls
M Chantry, LS Tuckerman, D Barkley
Journal of Fluid Mechanics 791, R8, 2016
Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status, and outlook
PD Dueben, MG Schultz, M Chantry, DJ Gagne, DM Hall, A McGovern
Artificial Intelligence for the Earth Systems 1 (3), e210002, 2022
Scale-selective precision for weather and climate forecasting
M Chantry, T Thornes, T Palmer, P Düben
Monthly Weather Review 147 (2), 645-655, 2019
Choosing the optimal numerical precision for data assimilation in the presence of model error
S Hatfield, P Düben, M Chantry, K Kondo, T Miyoshi, T Palmer
Journal of Advances in Modeling Earth Systems 10 (9), 2177-2191, 2018
The rise of data-driven weather forecasting: A first statistical assessment of machine learning-based weather forecasts in an operational-like context
Z Ben Bouallègue, MCA Clare, L Magnusson, E Gascon, M Maier-Gerber, ...
Bulletin of the American Meteorological Society, 2024
Weatherbench 2: A benchmark for the next generation of data-driven global weather models
S Rasp, S Hoyer, A Merose, I Langmore, P Battaglia, T Russel, ...
arXiv preprint arXiv:2308.15560, 2023
Accelerating high-resolution weather models with deep-learning hardware
S Hatfield, M Chantry, P Düben, T Palmer
Proceedings of the platform for advanced scientific computing conference, 1-11, 2019
Climate modeling in low precision: Effects of both deterministic and stochastic rounding
EA Paxton, M Chantry, M Klöwer, L Saffin, T Palmer
Journal of Climate 35 (4), 1215-1229, 2022
Studying edge geometry in transiently turbulent shear flows
M Chantry, TM Schneider
Journal of fluid mechanics 747, 506-517, 2014
Challenging conventional wisdom: Experimental evidence on heterogeneity and coordination in avoiding a collective catastrophic event
I Waichman, T Requate, M Karde, M Milinski
Journal of Environmental Economics and Management 109, 102502, 2021
Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers
ZB Bouallègue, JA Weyn, MCA Clare, J Dramsch, P Dueben, M Chantry
Artificial Intelligence for the Earth Systems 3 (1), e230027, 2024
A topological perspective on weather regimes
K Strommen, M Chantry, J Dorrington, N Otter
Climate Dynamics 60 (5), 1415-1445, 2023
Deep learning and a changing economy in weather and climate prediction
P Bauer, P Dueben, M Chantry, F Doblas-Reyes, T Hoefler, A McGovern, ...
Nature Reviews Earth & Environment 4 (8), 507-509, 2023
The system can't perform the operation now. Try again later.
Articles 1–20