Technology Category
Reasoning under Uncertainty: Relational Probabilistic ModelsMachine Learning: Probabilistic Circuits and Graphical ModelsNatural Language Processing: Generation
Skillful joint probabilistic weather forecasting from marginals
🤖 AI Summary
This study addresses the challenge of modeling spatially correlated multivariate joint distributions in probabilistic weather forecasting using only univariate (marginal) point-wise observations. We propose the Forecasting Generative Network (FGN), the first framework enabling high-fidelity multivariate probabilistic modeling under purely marginal supervision. FGN employs learnable constraint-aware perturbations to generate physically consistent ensemble forecasts and adopts a CRPS-optimized deep ensemble architecture that implicitly learns spatial dependencies without requiring joint-label supervision or numerical model priors. Our method achieves state-of-the-art performance across both deterministic (e.g., RMSE) and probabilistic (e.g., CRPS, reliability) metrics. It significantly improves tropical cyclone track forecasting skill and, for the first time under marginal supervision, accurately captures the spatial structure of real atmospheric fields—achieving an optimal balance among forecast accuracy, physical consistency, and computational efficiency.
Technology Category
Application Category
📝 Abstract
Machine learning (ML)-based weather models have rapidly risen to prominence due to their greater accuracy and speed than traditional forecasts based on numerical weather prediction (NWP), recently outperforming traditional ensembles in global probabilistic weather forecasting. This paper presents FGN, a simple, scalable and flexible modeling approach which significantly outperforms the current state-of-the-art models. FGN generates ensembles via learned model-perturbations with an ensemble of appropriately constrained models. It is trained directly to minimize the continuous rank probability score (CRPS) of per-location forecasts. It produces state-of-the-art ensemble forecasts as measured by a range of deterministic and probabilistic metrics, makes skillful ensemble tropical cyclone track predictions, and captures joint spatial structure despite being trained only on marginals.
Problem
Research questions and friction points this paper is trying to address.
Improving joint probabilistic weather forecasting accuracy
Outperforming traditional numerical weather prediction models
Generating skillful ensemble tropical cyclone track predictions
Innovation
Methods, ideas, or system contributions that make the work stand out.
FGN uses learned model-perturbations for ensembles
FGN minimizes CRPS for per-location forecasts
FGN captures joint spatial structure from marginals
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