Spherical Physics-informed Neural Operator with Multi-scale Coupling for Meteorological Downscaling
We propose the Spherical Physics-informed Neural Operator, which utilizes a Spherical Laplacian Decomposition to partition atmospheric fields into hierarchical frequency components, maintaining exact point-wise correspondence across scales.
ProofPaper ↗
Key points
- Meteorological downscaling is crucial for high-resolution regional climate forecasting and disaster early warning.
- While neural operators have emerged as a promising paradigm for modeling complex spatiotemporal mappings, existing frameworks often struggle with spherical manifold geometric distortions, inherent atmospheric multi-scale coupling mismatches, and lack of explicit atmospheric laws.
- To evaluate these representations at arbitrary locations, we introduce a localized spherical integral operator that approximates continuous kernel transforms via geometry-aware attention.
- Extensive experiments demonstrate that our framework attains superior accuracy and zero-shot generalization across various meteorological variables and unseen queries, representing a robust and interpretable solution for global-to-regional meteorological downscaling.
Sources (1)
- [1]Spherical Physics-informed Neural Operator with Multi-scale Coupling for Meteorological DownscalingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 02:13 PM
We propose the Spherical Physics-informed Neural Operator, which utilizes a Spherical Laplacian Decomposition to partition atmospheric fields into hierarchical frequency components, maintaining exact point-wise correspondence across scales.
Meteorological downscaling is crucial for high-resolution regional climate forecasting and disaster early warning.
Extractive summary: sentences quoted from the sources.