He et al. (2025) Extreme precipitation

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-05
• Authors: Yaqian He, Mei-Hua Huang, Shahrin Shahpar, Guanzhou Wei, Xiao Liu, Zhuosen Wang
• DOI: 10.1016/b978-0-443-33803-8.00020-2

Research Groups

• Department of Geosciences, University of Arkansas, Fayetteville, AR, United States
• Department of Electrical Engineering and Computer Science, University of Arkansas, Fayetteville, AR, United States
• Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
• Earth System Science Interdisciplinary Center, University of Maryland College Park, College Park, MD, United States
• Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States
• Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States
• Department of Geography, Indiana University Bloomington, Bloomington, IN, United States

Short Summary

This paper addresses the critical need for accurate and timely nowcasting of extreme precipitation events, highlighting the limitations of traditional numerical weather prediction models and advocating for data-driven Earth observation approaches to enhance disaster management.

Objective

• To explore and develop data-driven Earth observation methods for nowcasting extreme precipitation events, aiming to improve disaster preparedness and management by overcoming the computational and resolution constraints of traditional numerical weather prediction models.

Study Configuration

• Spatial Scale: Mesoscale and small scale (local weather details).
• Temporal Scale: Nowcasting, covering a period from the present up to 6 hours ahead.

Methodology and Data

• Models used: The paper discusses the limitations of Numerical Weather Prediction (NWP) models (e.g., COSMO) for nowcasting. The implied focus of the paper is on data-driven models for extreme precipitation nowcasting.
• Data sources: Earth observation data (implied by the paper title). Specific sources are not detailed in the provided introduction.

Main Results

The provided text is an introduction and does not contain the main results of the study.

Contributions

This paper contributes by exploring or proposing data-driven Earth observation approaches as a solution for accurate and timely nowcasting of extreme precipitation events, aiming to overcome the computational and resolution constraints of traditional numerical weather prediction models.

Funding

Not specified in the provided text.

Citation

@article{He2025Extreme,
  author = {He, Yaqian and Huang, Mei-Hua and Shahpar, Shahrin and Wei, Guanzhou and Liu, Xiao and Wang, Zhuosen},
  title = {Extreme precipitation},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-33803-8.00020-2},
  url = {https://doi.org/10.1016/b978-0-443-33803-8.00020-2}
}