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Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment

Paper recorded by Signals 4 on 2026-10-08 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09

Category: cs.LG · 机器学习 · first seen 2026-10-09

Abstract

Kilometer-scale regional weather forecasting is essential for local weather warnings and weather-sensitive decisions. Existing data-driven approaches often rely on numerical forecasts for large-scale guidance or require additional training of global forecasting components. Pretrained global weather models offer an efficient source of large-scale forecasts, motivating their reuse to guide high-reso

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#326 most recent of 749 cs.LG papers we have recorded · ↑ newer: WOVEN: Weaving Visual World Modeling into Multimodal LLMs · ↓ older: Prospective Prediction of OOD Degradation from Source-Side Training Dy
Cite this page: Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment: the #326 most recent of 749 cs.LG papers we have recorded (as of 2026-10-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-kilometer-scale-weather-prediction-with-global-regional-alignment.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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