Skillful high-resolution weather forecasting independent of physical models
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911723762483200 |
|---|---|
| author | Zhao, Pengcheng Xiang, Siqi Jin, Weixin Ni, Zekun Bian, Jiang Fang, Zuliang Sun, Hongyu Zhang, Bin Turner, Richard E. Weyn, Jonathan Dong, Haiyu Thambiratnam, Kit Zhang, Qi |
| author_facet | Zhao, Pengcheng Xiang, Siqi Jin, Weixin Ni, Zekun Bian, Jiang Fang, Zuliang Sun, Hongyu Zhang, Bin Turner, Richard E. Weyn, Jonathan Dong, Haiyu Thambiratnam, Kit Zhang, Qi |
| contents | Accurate and timely weather forecasts are critical for high-impact decisions in modern society. Machine-learning-based weather prediction is emerging as an alternative for producing initial conditions, forecasts, and even both in end-to-end systems. These methods deliver predictions faster and often with higher skill than traditional numerical weather prediction (NWP). However, even end-to-end models typically rely on NWP-generated reanalyses for supervision, thereby inheriting the biases and resolution limitations of those NWPs, and limiting adaptation to settings where suitable reanalysis products are unavailable, infrequently updated, or expensive to produce. Here we introduce ObsCast, a regional system that generates both analysis and predictions, without using any NWP-derived data in either training or inference, while still achieving state-of-the-art performance in short-term high-resolution regional modeling. Over the contiguous United States and Europe, ObsCast outperforms operational NWP for near-surface variables through 18 h and produces skillful precipitation forecasts. It provides a simpler and more adaptable route to build and refine regional forecasting services directly from local observations, without the need to develop complex and costly traditional forecasting pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28153 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Skillful high-resolution weather forecasting independent of physical models Zhao, Pengcheng Xiang, Siqi Jin, Weixin Ni, Zekun Bian, Jiang Fang, Zuliang Sun, Hongyu Zhang, Bin Turner, Richard E. Weyn, Jonathan Dong, Haiyu Thambiratnam, Kit Zhang, Qi Atmospheric and Oceanic Physics Machine Learning Accurate and timely weather forecasts are critical for high-impact decisions in modern society. Machine-learning-based weather prediction is emerging as an alternative for producing initial conditions, forecasts, and even both in end-to-end systems. These methods deliver predictions faster and often with higher skill than traditional numerical weather prediction (NWP). However, even end-to-end models typically rely on NWP-generated reanalyses for supervision, thereby inheriting the biases and resolution limitations of those NWPs, and limiting adaptation to settings where suitable reanalysis products are unavailable, infrequently updated, or expensive to produce. Here we introduce ObsCast, a regional system that generates both analysis and predictions, without using any NWP-derived data in either training or inference, while still achieving state-of-the-art performance in short-term high-resolution regional modeling. Over the contiguous United States and Europe, ObsCast outperforms operational NWP for near-surface variables through 18 h and produces skillful precipitation forecasts. It provides a simpler and more adaptable route to build and refine regional forecasting services directly from local observations, without the need to develop complex and costly traditional forecasting pipelines. |
| title | Skillful high-resolution weather forecasting independent of physical models |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2605.28153 |