Tyche achieves competitive probabilistic weather forecasting skill and calibration using a single-step flow model with JVP-regularized training and rollout finetuning.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A dual-scale learning framework for S2S temperature prediction shows that predictability is organized by interacting temporal components, spatial heterogeneity, and large-scale coherence, with fusion weights shifting systematically by season and location.
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Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting
Tyche achieves competitive probabilistic weather forecasting skill and calibration using a single-step flow model with JVP-regularized training and rollout finetuning.
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Dual-Scale Temporal Fusion Reveals Structured Predictability in Subseasonal-to-Seasonal Temperature Prediction
A dual-scale learning framework for S2S temperature prediction shows that predictability is organized by interacting temporal components, spatial heterogeneity, and large-scale coherence, with fusion weights shifting systematically by season and location.