Skilful nowcasting of extreme precipitation with NowcastNet

Yuchen Zhang, Mingsheng Long (), Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael I. Jordan () and Jianmin Wang ()
Additional contact information
Yuchen Zhang: Tsinghua University
Mingsheng Long: Tsinghua University
Kaiyuan Chen: Tsinghua University
Lanxiang Xing: Tsinghua University
Ronghua Jin: China Meteorological Administration
Michael I. Jordan: University of California
Jianmin Wang: Tsinghua University

Nature, 2023, vol. 619, issue 7970, 526-532

Abstract: Abstract Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details1–3. Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation4 and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation5. We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional meteorologists from across China, our model ranks first in 71% of cases against the leading methods. NowcastNet provides skilful forecasts at light-to-heavy rain rates, particularly for extreme-precipitation events accompanied by advective or convective processes that were previously considered intractable.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.nature.com/articles/s41586-023-06184-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:619:y:2023:i:7970:d:10.1038_s41586-023-06184-4

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-023-06184-4

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().