7 papers · 1 filter
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…