27 citations · 28 across the 11 of their papers we have counts for
10 papers · 1 filter
Evaluating Dynamical Fidelity through Predictive Structure in Physical Representations
Oskar Bohn Lassen, Joao Paulo de Souza Boger, Simon Driscoll +4
Machine-learning models for physical systems are currently evaluated primarily through errors between predicted and reference states and, increasingly, through tests of physical co…
Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models
Simon Adamov, Oliver Fuhrer, Reto Knutti +1
Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at far lower inference cost. Many…
Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling
Pritthijit Nath, Sebastian Schemm, Peter Haynes +2
Machine-learnt corrections can complement numerical weather prediction provided that they operate stably within an evolving numerical model. In this study, we couple the Met Office…
Physics-Informed Super-Resolution of Atmospheric Data
Chang Xu, Gencer Sumbul, Hugo Porta +3
In the context of global warming, extreme events have become more frequent and intense, making their trustworthy detection and forecasting more important than ever. Yet, atmospheri…
Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations
Oskar Bohn Lassen, Simon Driscoll, Stephen I. Thomson +2
Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically importa…
Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts
Fanny Lehmann, Firat Ozdemir, Yun Cheng +4
While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons. This…
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
Pritthijit Nath, Sebastian Schemm, Henry Moss +3
Weather and climate models rely on parametrisations to represent unresolved sub-grid processes. Traditional schemes rely on fixed coefficients that are weakly constrained and tuned…
FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models
Pritthijit Nath, Sebastian Schemm, Henry Moss +3
Sub-grid parameterisations in climate models are traditionally static and tuned offline, limiting adaptability to evolving states. This work introduces FedRAIN-Lite, a federated re…
Finetuning a Weather Foundation Model with Lightweight Decoders for Unseen Physical Processes
Fanny Lehmann, Firat Ozdemir, Benedikt Soja +3
Recent advances in AI weather forecasting have led to the emergence of so-called "foundation models", typically defined by expensive pretraining and minimal fine-tuning for downstr…
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
Kenza Amara, Rex Ying, Zitao Zhang +5
As one of the most popular machine learning models today, graph neural networks (GNNs) have attracted intense interest recently, and so does their explainability. Users are increas…