AFDBench is a new benchmark that evaluates how well large language models can generate professional Area Forecast Discussions (AFDs) for the National Weather Service by reasoning through structured AI weather forecast data. It contains 7,732 expert-written discussions paired with real forecast inputs and introduces three metrics—Met-Align, Style-Align, and Input-Grounding—to assess numerical accuracy, professional dialect adherence, and fidelity to source data. Zero-shot tests show open-source LLMs perform poorly on style and grounding, but reinforcement learning with Group Relative Policy Optimization nearly doubles style alignment and improves grounding, enabling a 7B-parameter model to write like a professional meteorologist.
By Manmeet Singh, Somnath Luitel, Prabhjot Singh, Manraaj Banga, Naveen Sudharsan, Josh Durkee
arXiv:2607. 03501v1 Announce Type: cross Abstract: Copernicus, the European Union's Earth observation program, produces petabytes of Earth observation and climate data, offering immense potential for research, policy, and applications.
By Yi Zhang, Farhad Nooralahzadeh, Jonathan F\"urst, Fabio Scherrer, Antonis Bezes, Vassiliki Kotroni, Kurt Stockinger
WeatherNext 3 is a new AI‑driven global weather model that improves both spatial and temporal resolution by generating hourly forecasts at 0.1° resolution, matching the best physics‑based models. It incorporates low‑latency geostationary satellite data and learns to predict satellite‑derived precipitation, tropical cyclones, and station observations, enabling 2 m temperature and dewpoint predictions anywhere and anytime. By directly using raw observations instead of relying solely on analysis data, WeatherNext 3 sets a new state‑of‑the‑art for probabilistic medium‑range forecasting skill.
By Stephan Rasp, Boris Babenko, Dominic Masters, Andrew El-Kadi, Samier Merchant, Guy Shalev, Ilan Price, Fred Zyda, Remi Lam, Sasha Shysheya, Matthew Willson, Stratis Markou, Shreya Agrawal, Suhani Vora, Mohammed Alewi Hassen, Sunny Mak, Tom R. Andersson, Megan Bela, Akib Uddin, Nofar Peled Levi, Ben Gaiarin, Ferran Alet, Aaron Bell, Peter Battaglia, Alvaro Sanchez-Gonzalez
arXiv:2610.14624v1 Announce Type: cross
Abstract: Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive se...
By Fadillah Adamsyah Maani, Numan Saeed, Jiaqi Ma, Dat Tien Nguyen, Youssef Wehbe, Duncan Watson-Parris, Salman Khan
arXiv:2603. 11479v3 Announce Type: replace-cross Abstract: Time Series Event Detection (TSED) aims to localize semantically meaningful events in time series data, with critical applications in high-stakes domains.
By Sky Chenwei Wan, Yifei Y. Wang, Tianjun Hou, Xiqing Chang, Aymeric Jan
arXiv:2608. 20117v1 Announce Type: new Abstract: The emergence of large-scale Weather and Climate (W&C) datasets offers new opportunities for modeling extreme Earth events (ExEE) and their impacts using deep learning.
By Hugo Porta, Emanuele Dalsasso, Chang Xu, Theo Gnassounou, Devis Tuia
The paper introduces SmartWeatherAgent, a three‑stage architecture that combines intent recognition, hazard prediction, and reasoning‑enhanced generation to improve tourism meteorological services. It fuses rule‑based methods with large language models and a LightGBM model enriched with highland‑specific features, achieving an F1‑Macro score of 0.605 and 1.60 ms latency on high‑wind, precipitation, and low‑temperature events. A 12‑round micro‑step prompt self‑optimization loop raises the composite warning quality score from 4.2 to 8.9, with notable gains in data source citation, physical mechanism explanation, and scientific rigor through explicit uncertainty statements.
By Shuai Yan, Yang Xu, Shan He
arXiv:2608. 12374v1 Announce Type: cross Abstract: Language models (LMs) struggle with logical tasks like reasoning on syllogisms.
By Hanna Abi Akl, Fabien Gandon, Catherine Faron, Pierre Monnin
To address insufficient contextualization, weak generalization, and poor scenario adaptation in tourism meteorological services, we propose SmartWeatherAgent--a unified three-stage architecture integr...
arXiv:2603. 01121v2 Announce Type: replace Abstract: While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge.
By Shuo Tang, Jiadong Zhang, Gengxian Zhou, Qizhao Jin, Qinxuan Wang, Yi Hu, Ning Hu, Hongchang Ren, Lingli He, Shiming Xiang, Jingtao Ding, Jian Xu, Jiaolan Fu, Cheng-Lin Liu
arXiv:2611.14401v1 Announce Type: new
Abstract: South Asian monsoon precipitation depends on the thermodynamic structure of the lower troposphere, but how its ingredients combine to shape precipitati...
By Savannah L. Ferretti, Jane W. Baldwin, Michael S. Pritchard, Tom Beucler
arXiv:2610.19021v1 Announce Type: new
Abstract: Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, L...
By Zijun Wu, Zhitian Zhang, Hossein Hajimirsadeghi, Nikita Dvornik, Alexander Pashevich