Hwang et al. (2025) Satellite Cloud-Top Temperature-Based Method for Early Detection of Heavy Rainfall Triggering Flash Floods

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water
• Year: 2025
• Date: 2025-12-15
• Authors: Seokhwan Hwang, Heejun Park, Jung Soo Yoon, Narae Kang
• DOI: 10.3390/w17243552

Research Groups

[Information not provided in the paper text.]

Short Summary

This study proposes an early-warning system for heavy rainfall based on the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT). The method, which quantifies CTT rise-peak-fall-trough patterns, achieved an 87.5% probability of detection with 1.3–8.6 hours lead time, providing 1–3 hours more lead time than radar nowcasting.

Objective

• To develop a practical early-warning approach for heavy rainfall detection using the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT).

Study Configuration

• Spatial Scale: Regional (applied to eight heavy rainfall events in Korea).
• Temporal Scale: Events occurring in July 2025; lead times ranging from 1.3 hours to 8.6 hours.

Methodology and Data

• Models used: Pattern-based analysis of Cloud-Top Temperature dynamics (WATCH method).
• Data sources: Satellite-derived Cloud-Top Temperature (CTT), radar-observed rainfall.

Main Results

• A rapid rise followed by a sharp fall in CTT was identified as a precursor signal for convective intensification and heavy rainfall.
• The CTT WATCH method achieved a probability of detection (POD) of 87.5% for heavy rainfall events.
• Potential heavy rainfall could be detected approximately 1.3–8.6 hours in advance using the CTT WATCH method.
• The CTT WATCH method retained predictive skill up to 3 hours before numerical model guidance became effective.
• The method improved early-warning capability by providing 1–3 hours of additional lead time relative to radar nowcasting in rapidly evolving convective environments.

Contributions

• Introduces a clear methodological novelty by presenting a physically interpretable, pattern-based metric for heavy rainfall early warning.
• Bridges the forecast gap in short-term prediction by providing predictive skill before numerical model guidance becomes effective.
• Offers an interpretable, low-cost module suitable for operational early-warning systems and flood preparedness applications.

Funding

[Information not provided in the paper text.]

Citation

@article{Hwang2025Satellite,
  author = {Hwang, Seokhwan and Park, Heejun and Yoon, Jung Soo and Kang, Narae},
  title = {Satellite Cloud-Top Temperature-Based Method for Early Detection of Heavy Rainfall Triggering Flash Floods},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17243552},
  url = {https://doi.org/10.3390/w17243552}
}