arXiv Machine Learning

Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling

The study couples the Met Office Unified Model with distributed reinforcement learning agents, using a DDPG actor that applies bounded potential‑temperature corrections across 70 vertical levels. Training is performed on ten nudged forecasts, after which the frozen policy is evaluated in a non‑nudged forecast, demonstrating numerical stability. The learned policy reduces Z₅₀₀ MAE in four of six latitude bands—up to 45.8% in the northern tropics—and decreases MSLP error by up to 27.3% in certain bands, indicating promising bias‑correction potential.

Hugging Face Trending Papers

Sep 2

Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling

The study couples the Met Office Unified Model with distributed reinforcement learning agents that apply bounded temperature corrections across 70 vertical levels. Training on ten nudged forecasts and evaluating on a non‑nudged run, the learned policy remains numerically stable and improves forecast accuracy, reducing Z₅₀₀ MAE by up to 45.8% in tropical bands and MSLP error by up to 27.3% in certain latitudes. This experiment demonstrates the feasibility of online RL for bias correction in operational weather models.

agents reinforcement-learning safety

arXiv Machine Learning

Jul 27

Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning

arXiv:2601. 04268v3 Announce Type: replace Abstract: Weather and climate models rely on parametrisations to represent unresolved sub-grid processes.

By Pritthijit Nath, Sebastian Schemm, Henry Moss, Peter Haynes, Emily Shuckburgh, Mark J. Webb

agents reinforcement-learning safety

arXiv Machine Learning

Aug 12

An adaptive and evolvable deep reinforcement learning framework for weather prediction

arXiv:2608. 09948v1 Announce Type: cross Abstract: No single AI weather model excels at all variables, pressure levels, and lead times.

By Qiang Wu, Han Li, Jianping Huang

agents reinforcement-learning

arXiv Machine Learning

Sep 7

Advancing Subseasonal Forecasting with Machine Learning

The paper introduces Probabilistic Bias Correction (PBC), a machine learning framework that learns to correct historical probabilistic forecasts, thereby reducing systematic errors in subseasonal weather predictions. Applied to leading dynamical and AI models from ECMWF, PBC doubles the AI system’s modest subseasonal skill and improves the operationally-debiased dynamical model for most pressure, temperature, and precipitation targets. In ECMWF’s 2025 real‑time forecasting competition, PBC’s global forecasts ranked first across all weather variables and lead times, outperforming multiple operational and ensemble models.

By Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen, Haiyu Dong, Zekun Ni, Jeremy Berman, Genevieve Flaspohler, Alex Lu, Jakob Schloer, Joshua Talib, Jonathan A. Weyn, Lester Mackey

safety

arXiv Machine Learning

Jul 13

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction

arXiv:2604. 16238v2 Announce Type: replace Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes.

By Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen, Haiyu Dong, Zekun Ni, Jeremy Berman, Genevieve Flaspohler, Alex Lu, Jakob Schloer, Joshua Talib, Jonathan A. Weyn, Lester Mackey

safety

arXiv AI

Jul 28

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.

By Maria Luisa Taccari, Kenza Tazi, Ois\'in M. Morrison, Andreas Grafberger, Juan Colonese, Corentin Carton de Wiart, Christel Prudhomme, Cinzia Mazzetti, Matthew Chantry, Florian Pappenberger

fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Atmospheric Predictability Beyond 30 Days with Machine Learning

arXiv:2504. 20238v2 Announce Type: replace-cross Abstract: Atmospheric predictability research has long held that rapid error growth at small spatial scales imposes an intrinsic limit of roughly two weeks on deterministic weather forecast skill.

By P. Trent Vonich, Gregory J. Hakim

arXiv AI

Jun 3

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting

arXiv:2605. 08935v3 Announce Type: replace Abstract: Coupled spatiotemporal forecasting is important for predicting the future evolution of multiple interacting dynamical systems, such as in climate models.

By Hao Wu, Fan Xu, Yuxu Lu, Penghao Zhao, Fan Zhang, Hao Jia, Yuxuan Liang, Ruijian Gou, Qingsong Wen, Xian Wu, Xiaomeng Huang, Yuan Gao

agents benchmarks

arXiv AI

Aug 12

Rescene: band-limited stochastic forcing turns a frozen neural weather operator into a climate emulator

arXiv:2608. 09971v1 Announce Type: cross Abstract: Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has produced deterministic models whose medium-range skill matches or exceeds that of the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES).

By Minjong Cheon

llms

arXiv Machine Learning

Jun 5

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

arXiv:2510. 02415v3 Announce Type: replace-cross Abstract: Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed.

By Bosong Zhang, Timothy M. Merlis

benchmarks

arXiv Machine Learning

Sep 21

Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

arXiv:2601.21151v3 Announce Type: replace Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...

By Carlos A. Pereira, St\'ephane Gaudreault, Valentin Dallerit, Christopher Subich, Shoyon Panday, Siqi Wei, Sasa Zhang, Siddharth Rout, Eldad Haber, Raymond J. Spiteri, David Millard

diffusion benchmarks

arXiv Machine Learning

Jun 8

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

arXiv:2511. 02748v2 Announce Type: replace-cross Abstract: We argue that sixth-generation (6G) intelligence is not fluent token prediction but the capacity to imagine and choose -- to simulate future scenarios, weigh trade-offs, and act with calibrated uncertainty.

By Farhad Rezazadeh, Amir Ashtari Gargari, Hatim Chergui, Sandra Lagen, Merouane Debbah, Houbing Song, Lingjia Liu

llms agents

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