Accelerating High-Resolution Weather Models with Deep-Learning Hardware. - Sam Hatfield; Matthew Chantry; Peter D. Düben; Tim N. Palmer (PASC, 2019)

RainBench: Towards Global Precipitation Forecasting from Satellite Imagery. - Christian Schröder de Witt; Catherine Tong; Valentina Zantedeschi; Daniele De Martini; Freddie Kalaitzis; Matthew Chantry; Duncan Watson-Parris; Piotr Bilinski (CoRR, 2020)

RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery. - Christian Schröder de Witt; Catherine Tong; Valentina Zantedeschi; Daniele De Martini; Alfredo Kalaitzis; Matthew Chantry; Duncan Watson-Parris; Piotr Bilinski (AAAI, 2021)

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts. - Lucy Harris; Andrew T. T. McRae; Matthew Chantry; Peter D. Düben; Tim N. Palmer (CoRR, 2022)

WeatherBench 2: A benchmark for the next generation of data-driven global weather models. - Stephan Rasp 001; Stephan Hoyer; Alexander Merose; Ian Langmore; Peter W. Battaglia; Tyler Russell; Alvaro Sanchez-Gonzalez; Vivian Yang; Rob Carver; Shreya Agrawal; Matthew Chantry; Zied Ben Bouallegue; et al. (CoRR, 2023)

Data driven weather forecasts trained and initialised directly from observations. - Anthony McNally; Christian Lessig; Peter Lean; Eulalie Boucher; Mihai Alexe; Ewan Pinnington; Matthew Chantry; Simon Lang; Chris Burrows 002; Marcin Chrust; Florian Pinault; Ethel Villeneuve; et al. (CoRR, 2024)

Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators. - Marieke Wesselkamp; Matthew Chantry; Ewan Pinnington; Margarita Choulga; Souhail Boussetta; Maria Kalweit; Joschka Boedecker; Carsten F. Dormann; Florian Pappenberger; Gianpaolo Balsamo (CoRR, 2024)

Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications. - Tom Beucler; Arthur Grundner; Sara Shamekh; Peter Ukkonen; Matthew Chantry; Ryan Lagerquist (CoRR, 2024)

Regional data-driven weather modeling with a global stretched-grid. - Thomas Nils Nipen; Håvard Homleid Haugen; Magnus Sikora Ingstad; Even Marius Nordhagen; Aram Farhad Shafiq Salihi; Paulina Tedesco; Ivar Ambjørn Seierstad; Jørn Kristiansen; Simon Lang; Mihai Alexe; Jesper Sören Dramsch; Baudouin Raoult; et al. (CoRR, 2024)

Robustness of AI-based weather forecasts in a changing climate. - Thomas Rackow; Nikolay Koldunov; Christian Lessig; Irina Sandu; Mihai Alexe; Matthew Chantry; Mariana Clare; Jesper Sören Dramsch; Florian Pappenberger; Xabier Pedruzo-Bagazgoitia; Steffen Tietsche; Thomas Jung (CoRR, 2024)

Hydra-LSTM: A semi-shared Machine Learning architecture for prediction across Watersheds. - Karan Ruparell; Robert J. Marks; Andy Wood; Kieran M. R. Hunt; Hannah L. Cloke; Christel Prudhomme; Florian Pappenberger; Matthew Chantry (CoRR, 2024)

GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations. - Mihai Alexe; Eulalie Boucher; Peter Lean; Ewan Pinnington; Patrick Laloyaux; Anthony McNally; Simon Lang; Matthew Chantry; Chris Burrows 002; Marcin Chrust; Florian Pinault; Ethel Villeneuve; et al. (CoRR, 2024)

End-to-end data-driven weather prediction. - Anna Allen; Stratis Markou; Will Tebbutt; James Requeima; Wessel P. Bruinsma; Tom R. Andersson; Michael Herzog; Nicholas D. Lane; Matthew Chantry; J. Scott Hosking; Richard E. Turner (Nat., 2025)

RainShift: A Benchmark for Precipitation Downscaling Across Geographies. - Paula Harder; Luca Schmidt; Francis Pelletier; Nicole Ludwig; Matthew Chantry; Christian Lessig; Alex Hernández-García; David Rolnick (CoRR, 2025)