arXiv Statistics ML
M$^2$Weather: A Benchmark for Joint Multi-Station and Multi-Variable Weather Forecasting
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WeatherNext 3: Increasing resolution and performance of global weather models with raw observations
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.
arXiv Machine Learning
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StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting
StationPDE is a station-oriented surface PDE learning model designed for multi-station multivariate weather forecasting. It builds a terrain-aware continuous surface field from discrete station observations and separates its physical evolution into surface wind transport and upper-air inference, the latter approximating missing upper-air effects via learnable horizontal diffusion. The model also includes a data-driven diffusion branch and an adaptive router to combine forecasts, achieving a 9.6% average MSE reduction over state-of-the-art baselines on Weather2K and MeteoNet datasets.
Hugging Face Trending Papers
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Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and glob...
arXiv Machine Learning
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UniGIO: Unified Generative Global In-situ Weather Modeling from Spatiotemporal Incomplete Observations
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.
arXiv Machine Learning
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Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
Varda‑single‑1.0 is a medium‑range, data‑driven weather prediction system designed for Switzerland’s Alpine region, delivering hourly deterministic forecasts at 1 km resolution and global forecasts at 31 km. It uses two independently trained stretched‑grid Graph Transformer models—an autoregressive forecaster and a temporal downscaler—trained through a curriculum that starts with ERA5 reanalysis, proceeds to a 20‑year regional reanalysis, and ends with fine‑tuning on operational analyses. Over a one‑year verification, Varda‑single matches or surpasses MeteoSwiss’s numerical weather prediction baselines for most key metrics, though it underestimates local wind peaks and produces smoother convective precipitation fields. whyItMatters":"The system demonstrates that high‑resolution machine‑learning models can rival traditional numerical weather prediction in complex terrain, offering a complementary tool for operational forecasting and research."
arXiv Machine Learning
Jul 7
Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations
arXiv:2607. 04862v1 Announce Type: new Abstract: Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due to systemic intensity underestimation and spatial displacement.