arXiv:2506.14022v3 Announce Type: replace-cross
Abstract: Subseasonal-to-seasonal forecasting is crucial for public health, disaster preparedness, and agriculture, and yet it remains a particularly challenging timescale to predict. We explore the use …
arXiv:2604.02850v1 Announce Type: cross
Abstract: The ocean interior regulates Earth’s climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware gene…
arXiv:2604.01215v1 Announce Type: cross
Abstract: AI weather prediction has advanced rapidly, yet no unified mathematical framework explains what determines forecast skill. Existing theory addresses specific architectural choices rather than the learn…
arXiv:2603.10305v2 Announce Type: replace
Abstract: Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While…
arXiv:2406.14399v3 Announce Type: replace-cross
Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datas…
arXiv:2603.27288v1 Announce Type: cross
Abstract: Global AI weather forecasting still relies mainly on uniform-resolution models, making it hard to combine regional refinement, two-way regional-global coupling, and affordable training cost. We introdu…