arXiv Statistics ML
Oct 2

M$^2$Weather: A Benchmark for Joint Multi-Station and Multi-Variable Weather Forecasting

M$^2$Weather is a new benchmark for joint multi‑station and multi‑variable weather forecasting that gathers 2,809 high‑quality stations with five physically coupled weather variables across France, Europe, and the globe. The benchmark includes unified training and evaluation protocols and a lightweight adapter that can introduce missing station or variable relationships without retraining. Systematic evaluation of 16 models shows that jointly modeling station and variable relationships reduces mean‑squared error across all datasets.

By Rongwen Li, Xiao Wang, Mingyang Wang, Hongwu Liu, Changjian Chen, Zhuo Tang, Kenli Li
arXiv Machine Learning
Oct 2

Methodological Changes to the Attention ResUNet Hourly Precipitation Postprocessor

The note updates the Attention ResUNet hourly precipitation postprocessor, replacing 192 month‑ and lead‑specific models with a single season‑conditioned model using Feature‑wise Linear Modulation. Lead time is extended to 72 h and two new input channels—local solar hour and a monthly‑varying precipitation climatology—are added. Verification shows a modest, consistent improvement in Brier Skill Score and reliability compared to the previous training.

By Thomas M. Hamill
arXiv AI
Jun 3

AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret

arXiv:2606. 02663v1 Announce Type: cross Abstract: Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors.

By Saptarishi Dhanuka (Ashoka University), Sarvesh Iyer (Ashoka University), Manmeet Singh (Western Kentucky University), Mihir More (Ashoka University), Rushil Gupta (Ashoka University), Dhruman Gupta (Ashoka University), Parthasarathi Mukhopadhyay (Ashoka University), Sandeep Juneja (Ashoka University)
Hugging Face Trending Papers
Jul 6

AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales

Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent at shorter horizons. Errors accumulate over long autoregressive rollouts, systematic biases grow with lead time, and several years of data must be held out for independent verification, even though machine-learning models otherwise benefit from longer training records.

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