Protocol to discover terrain-precipitation relationships with interpretable artificial intelligence
The relationship between terrain and precipitation is a crucial aspect of climate science. Here, we describe a protocol for uncovering the underlying laws governing this relationship via interpretable artificial intelligence. We describe the steps for preparing datasets, constructing algorithms, and discovering explicit equations. We then detail the construction of an iterative optimization of knowledge generation and utilization for the projection of future precipitation. For complete details on the use and execution of this protocol, please refer to Xu et al.<sup>1</sup>.
Authors
- Hao Xu (ORCID: https://orcid.org/0000-0001-8208-8904)
- Nina Li
- Zhenzhong Zeng (ORCID: https://orcid.org/0000-0001-6851-2756)
- Jian Li (ORCID: https://orcid.org/0000-0001-7223-0022)
- Dongxiao Zhang (ORCID: https://orcid.org/0000-0001-6930-5994)
- Yuntian Chen (ORCID: https://orcid.org/0000-0003-4566-8197)
Institutions
- China Meteorological Administration (CN)
- Southern University of Science and Technology (CN)
- Zhejiang Business Technology Institute (CN)
- Chinese Academy of Meteorological Sciences (CN)
- Peng Cheng Laboratory (CN)
- Shenzhen Technology University (CN)
- Ningbo Institute of Industrial Technology (CN)
Publication Details
- Journal
- STAR Protocols
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1016/j.xpro.2025.104062
- Primary Topic
- Meteorological Phenomena and Simulations
- Type
- article
- Field-Weighted Citation Impact
- 0.00