Meteorological Phenomena and Simulations

Latest papers

• Benchmarking Generative Models for Weather Data Assimilation on Real Station Observations

  Ruizhe Huang, Qidong Yang, Jonathan Giezendanner, Sherrie Wang · 2 October 2026

  Weather reanalysis products rely on computationally intensive numerical weather predictions followed by data assimilation that corrects the forecast toward observations. Deep generative models offer a cheaper alternative that shifts much of this cost from inference to offline training. However, exis…

• Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

  Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis, Themistoklis P. Sapsis · 28 September 2026

  Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather than by frequently observed bulk dynamics. We propose a mechanism-aware conditioning plug-in framework that turns a nudged coarse ensemble into a n…

• Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces

  Yang Xu, Dibyajyoti Chakraborty, Haiwen Guan, Sen Wang, Romit Maulik · 25 September 2026

  Atmospheric observations are sparse, heterogeneous, and unevenly distributed, whereas many generative atmospheric models learn distributions over regularly gridded multivariate states. Once pretrained, diffusion models can supply atmospheric priors that can be combined with observation-derived likel…

• Improving Ensemble Filters with Flow Matching

  Haoyuan Chen, Alexandre Thi\'ery · 24 September 2026

  Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample covariance and affine Gaussian distribution. We introduce the Flow Ensemble Filter (FlowEF), which uses conditional flow mat…

• West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation

  Nazak Rouzegari, Vesta Afzali Gorooh, Agniv Sengupta, Phu Nguyen, Kuo-Lin Hsu, Amir AghaKouchak, Soroosh Sorooshian, F. Martin Ralph, Luca Delle Monache · 23 September 2026

  We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topograph…

• UniGIO: Unified Generative Global In-situ Weather Modeling from Spatiotemporal Incomplete Observations

  Songru Yang, Zili Liu, Tao Han, Ben Fei, Lei Bai, Chang Liu, Zhengxia Zou, Xiangyang Ji, Wanli Ouyang, Zhenwei Shi · 22 September 2026

  Global In-situ Observation (GIO) provides fine-scale, direct records of the global weather system from sparse point stations, making it an indispensable source for capturing localized and transient dynamics beyond the reach of satellite gridded data, and playing a critical role in key fields such as…

• StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting

  Xiao Wang, Changjian Chen, Rongwen Li, Hongwu Liu, Kun Fang, Zhuo Tang · 22 September 2026

  Multi-station multivariate weather forecasting aims to forecast future weather variables at multiple weather stations from historical surface observations. Existing station forecasting models learn statistical dependencies among discrete stations, but lack explicit physical evolution. Meanwhile, PDE…

• Chronosphere: Space-Time Tessellation of Local Climate Experts

  Daniel Cher, Eric Xing, Kexing Li, Brian Wei, Isaac Corley, Nathan Jacobs · 21 September 2026

  We introduce Chronosphere, a spatio-temporal neural field that learns representations of climate. A central challenge in geographic representation learning is modeling environmental processes whose spatial and temporal complexity varies widely. Yet existing location encoders typically fix a single l…

• WaveHiTS: Wavelet-Enhanced Hierarchical Time Series Modeling for Wind Direction Nowcasting in Eastern Inner Mongolia

  Hailong Shu, Weiwei Song, Yue Wang, Jiping Zhang · 16 September 2026

  Wind direction forecasting plays a crucial role in optimizing wind energy production, but faces significant challenges due to the circular nature of directional data, error accumulation in multi-step forecasting, and complex meteorological interactions. This paper presents a novel model, WaveHiTS, w…

• 4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling

  Deifilia Kieckhefen, Juan Pedro Guti\'errez Hermosillo Muriedas, Lars Helge Heyen, Mathis Bode, Iida Hakulinen, Andreas Herten, Chelsea Maria John, Thorsten Kurth, Anni Moisala, Asena Karolin \"Ozdemir, Kaleb Phipps, Oskar Taubert, Arvid Weyrauch, Markus G\"otz, Charlotte Debus · 14 September 2026

  We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty. To overcome the associated computational bottlenecks, we devise an orthogonal 4D-parallelization scheme that …

• A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway

  Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen, Georgios Leontidis, Arnt-B{\o}rre Salberg, S\'ebastien Lef\`evre, Robert Jenssen, Kristoffer Wickstr{\o}m · 11 September 2026

  Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains unclear in challenging environments such as Northern Norway, where narrow fjords and rapidly changing weather result in hi…

• Agent-Based ML-LLM Fusion with Self-Optimizing Prompts for Plateau Weather Alerts

  Shuai Yan, Yang Xu, Shan He · 10 September 2026

  To address insufficient contextualization, weak generalization, and poor scenario adaptation in tourism meteorological services, we propose SmartWeatherAgent--a unified three-stage architecture integrating intent recognition, hazard prediction, and reasoning-enhanced generation. The system fuses rul…

• SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting

  Hiep V. Dang, Antonios Mamalakis · 4 September 2026

  Subseasonal-to-seasonal (S2S) precipitation forecasting has substantial financial and societal impact, yet remains challenging because of weak predictive signals, high associated uncertainty, and the computational cost of operational systems, which constrains simulation fidelity. We introduce SimCas…

• Improving precipitation forecasts in an AI weather model using observational data

  Julian F. Schmitt, Bertrand Delorme, Robert C. King, Yashica Patodia, Tapio Schneider, Aditi Sheshadri, Ravi Jain · 4 September 2026

  Artificial intelligence weather prediction (AIWP) systems now surpass state-of-the-art physical models for medium-range weather forecasting. Current global AIWP models are trained almost exclusively using one reanalysis dataset, ERA5, but it has known biases, particularly for precipitation. Here we …

• From Nowcasting to Forecasting: Adapting a Reanalysis-Trained

  Mikko Partio, Leila Hieta, Ossi Laine · 4 September 2026

  Accurate cloud-cover forecasts are important for temperature prediction, radiation forecasting, and solar-power operations. Short-range forecasting methods can preserve observed cloud placement during the first forecast hours, but their skill decreases when cloud fields evolve through formation, dis…

• WeatherNext 3: Increasing resolution and performance of global weather models with raw observations

  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 · 4 September 2026

  State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spatial and temporal resolution than the best physics-based models and they are exclusively initialized with and trained on a…

• Distilling deep optical flow stereo methods to retrieve dense three-dimensional wind fields

  Thomas J. Vandal, Dong L. Wu, James L. Carr, Derek J. Posselt, Elise Penn, Tristan Ballard, August Posch, Kate Duffy · 4 September 2026

  Geostationary atmospheric motion vectors (AMVs) provide the dense horizontal wind vectors (u,v) and heights ingested into data assimilation systems. Traditional AMVs track features using window-based cross-correlation and estimate heights via infrared brightness temperatures paired with numerical we…

• Diffusion Distillation for Efficient Weather Ensembles

  Yiming Yang, Valentin Brekke, James Briant, Serge Guillas · 31 August 2026

  Diffusion models generate skillful weather ensembles but require costly iterative sampling. We introduce a supervised energy-distance distillation method that compresses a multi-step diffusion teacher into a single-step student by aligning student forecasts with teacher samples and ground-truth obse…

• Learning Continuous Regional Temperature Fields with Lead-Time and Resolution Queries

  Chunlei Shi, Jiong Wang, Yi-Lin Wei, Junming Hou, Jinjin Liu, Yecheng Zhang, Dan Niu · 27 August 2026

  Accurate regional near-surface temperature forecasting is fundamental to short-range weather services and downstream risk assessment. Existing deep learning-based regional forecasters commonly produce a fixed set of future frames on a prescribed grid, limiting their use when forecast products must b…

• AFDBench: A Reasoning-First AI Scientist for NationalWeather Service Forecast Discussions

  Manmeet Singh, Somnath Luitel, Prabhjot Singh, Manraaj Banga, Naveen Sudharsan, Josh Durkee · 27 August 2026

  Large language models (LLMs) hallucinate numerical values when generating high-stakes meteorological text, posing risks for weather communication. We present AFDBench, an AI meteorologist that generates professional Area Forecast Discussions (AFDs) by reasoning through structured AI weather forecast…

• OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation

  Wei Wu, Xiang Wang, Hongze Leng, Qingye Min, Junxing Zhu, Junqiang Song · 18 August 2026

  Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs, while existing deep learning approaches predominantly rely on structured-grid architectures, incurr…

• TERRA: A Hierarchical Parallel Training and Memory Orchestration Framework for High-Resolution AI-based Earth Modeling

  Ruohan Wu, Ziqi Zhu, Yang Zhao, Jiarui Tang, Yingzhe Cui, Junshi Chen, Zhao Jing, Jun Shi, Hong An · 18 August 2026

  Training high-resolution AI-based Earth forecasting models is memory-intensive. Window-based Swin Transformers reduce the quadratic cost of global attention, but existing distributed systems such as AERIS primarily target pixel-level models and do not jointly support convolutional sampling modules a…

• Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling

  Yang Zhao, Peisong Niu, Tian Zhou, Ziqing Ma, Guanlong Ma, Rong Jin, Huiling Yuan, Liang Sun · 18 August 2026

  The development of 0.1$^{\circ}$ global weather forecasting models based on machine learning (ML) is constrained by the limited availability of high-resolution data, as decades of reanalysis are only available at 0.25$^{\circ}$ resolution. While existing approaches fine-tune 0.25$^{\circ}$ forecast …

• Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

  Pedro Sousa (Department of Computer Science, University of Cambridge), Will Tebbutt (Department of Engineering, University of Cambridge), Sadiq Jaffer (Department of Computer Science, University of Cambridge), Robin Young (Department of Computer Science, University of Cambridge), Anil Madhavapeddy (Department of Computer Science, University of Cambridge), Richard E. Turner (Department of Engineering, University of Cambridge) · 13 August 2026

  Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties. Existing probabilistic downscalers ad…

• Do AI weather models miss extremes?

  Marvin Vincent Gabler, Roberto Molinaro, Niall Siegenheim, Henry Martin, Mark Frey, Niels Poulsen, Philipp Seitz, Olivier Lam · 12 August 2026

  First-generation AI weather models are often reported to underperform at extremes, mostly in reanalysis-based evaluations of deterministic regression systems. We verify eleven physical and AI forecast systems against European synoptic, solar, and rain-gauge stations over ten months for 10 m wind, 2 …