STCFormer is an adaptive spatio‑temporal Transformer that dynamically clusters weather stations within each temporal patch, combining fine‑grained local attention inside clusters with global attention over regional summaries. The model’s design is supported by a Lipschitz upper bound that suggests robustness benefits, and it achieves the lowest 24‑hour mean squared error across eight temperature and wind forecasting tasks on three real‑world datasets, ranking first or second in 47 of 48 comparisons. Ablation studies and case analyses confirm the advantages of locally adaptive grouping and complementary local‑global interactions.
By Rongwen Li, Haixin Xie, Mingyang Wang, Hongwu Liu, Kun Fang, Changjian Chen, Zhuo Tang, Kenli Li
arXiv:2607. 13101v1 Announce Type: cross Abstract: Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions.
By Songru Yang, Zili Liu, Tao Han, Ben Fei, Fenghua Ling, Lei Bai, Chang Liu, Xiangyang Ji, Zhenwei Shi, Zhengxia Zou
UniGIO is a generative framework that models global in‑situ weather dynamics directly from incomplete GIO data, unifying forecasting, imputation, and generation across arbitrary missing ratios. It employs an Observation Mixer, Event Aligner, Adaptive Temporal Mixer, and a Mixture‑of‑Experts structure to capture station‑level complementarity, temporal dependencies, and extreme events, refining outputs with a Local Refiner. Experiments on the Weather‑5K dataset show state‑of‑the‑art performance, improving accuracy, fidelity, and extreme event capture by 11%, 12%, and 5% respectively.
By Songru Yang, Zili Liu, Tao Han, Ben Fei, Lei Bai, Chang Liu, Zhengxia Zou, Xiangyang Ji, Wanli Ouyang, Zhenwei Shi
arXiv:2408. 11336v3 Announce Type: replace Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns.
By Tajamul Ashraf, Janibul Bashir
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:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.
By Tao Han, Zhibin Wen, Zhenghao Chen, Dazhao Du, Song Guo, Lei Bai
This study presents a new mobile‑sensing dataset from Surat, India, capturing PM2.5 concentrations along with meteorological and land‑use variables. The authors model the data as a graph using two node‑definition strategies—uniform segmentation and DBSCAN clustering—and introduce a Spatially Attentive Graph Neural Network (SA‑GNN) that combines cluster‑specific GRUs with a Graph Attention Network to forecast fine‑grained, short‑term PM2.5 levels. SA‑GNN outperforms traditional LSTM, RNN, GRU, and ANN baselines, achieving an R² of 0.95, RMSE of 6.8, and MAE of 4.2 µg/m³ on the dataset.
By Om Chiddarwar, Priyanka Mandal, Praveen Kumar Chandaliya, Shriniwas Arkatkar
arXiv:2607. 11896v1 Announce Type: cross Abstract: Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms.
By Shuangshuang He, Shuo Wang
arXiv:2606. 27277v1 Announce Type: new Abstract: Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions.
By Junwei Luo, Shuai Yuan, Zhenya Yang, Yansheng Li, Zhe Liu, Hengshuang Zhao
arXiv:2506.12045v2 Announce Type: replace-cross
Abstract: Accurate reconstruction of latent environmental fields from sparse, indirect observations is a fundamental challenge across scientific domain...
By Kazuma Kobayashi, Tapas Tripura, Jay Phil Yoo, Diab Abueidda, Seid Koric, Souvik Chakraborty, Syed Bahauddin Alam
SimCast‑S2S is a generative latent‑diffusion framework designed for probabilistic subseasonal‑to‑seasonal precipitation forecasting. It tackles three key challenges: it uses a diffusion‑based generative pipeline for uncertainty quantification, operates in a compact latent space learned by VAEs for efficient large‑ensemble generation, and employs transfer learning with LoRA to overcome limited training data. On reanalysis data, it outperforms deep‑learning baselines and competes with or surpasses state‑of‑the‑art operational systems such as ECMWF‑S2S.
By Hiep V. Dang, Antonios Mamalakis
arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.
By Matteo Peduto, Qidong Yang, Jonathan Giezendanner, Devis Tuia, Sherrie Wang