Cast3: Translating numerical weather prediction principles into data-driven forecasting

TL;DR

Cast3 is a novel data-driven weather forecasting framework that integrates NWP principles with generative modeling to improve ensemble predictions and mesoscale realism.

Contribution

It systematically incorporates NWP knowledge into a generative model, creating diverse super-ensembles and a novel generative nudging method for enhanced weather forecasts.

Findings

Cast3 outperforms existing baselines in synoptic-scale skill and spectral fidelity.

It produces ensemble forecasts that combine large-scale accuracy with mesoscale realism.

The framework demonstrates the value of NWP principles in data-driven Earth system modeling.

Abstract

Data-driven weather models have made rapid advances in recent years, reaching and in some metrics surpassing the large-scale forecast skill of operational numerical weather prediction. This progress, however, has been built almost entirely on the reanalysis data that NWP produced, while the methodological knowledge that the NWP community distilled over decades of multi-scale atmospheric modelling remains largely unused. Here we present Cast3, a generative forecasting framework that systematically absorbs NWP meta-knowledge to close this gap. Cast3 operates on variable-resolution cubed-sphere grids for scale-aware representation and constructs structurally diverse super-ensembles that sample the complementary biases of different grid discretizations, delivering state-of-the-art ensemble prediction. It further introduces generative nudging, a posterior-sampling strategy that distils the collective information of the full ensemble into a single forecast possessing both the large-scale accuracy of the ensemble mean and the mesoscale realism of a high-resolution member. Evaluated across synoptic-scale skill, spectral fidelity, station-level surface verification, and tropical cyclone prediction, Cast3 outperforms established deterministic and generative baselines across various dimensions. More broadly, these results demonstrate that the design principles embedded in computational atmospheric science offer a rich and largely untapped foundation for the next generation of data-driven Earth system modelling.