Learning joint probabilistic weather forecasts from station observations alone
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2608.30795v1 Announce Type: cross Abstract: End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the nume...
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.
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.
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.
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.
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.