Development of an R-CLIPER model using GSMaP and TRMM precipitation data for tropical cyclones affecting Vietnam

Hang Nguyen Thu, Nga Pham Thi Thanh (), Hang Vu Thanh, Ha Pham Thanh, Long Trinh Tuan, The Doan Thi, Thuc Tran Duy and Hao Nguyen Thi Phuong
Additional contact information
Hang Nguyen Thu: National Center for Hydrometeorological Forecasting
Nga Pham Thi Thanh: Viet Nam Institute of Meteorology, Hydrology and Climate Change
Hang Vu Thanh: VNU Hanoi University of Science
Ha Pham Thanh: VNU Hanoi University of Science
Long Trinh Tuan: VNU Hanoi University of Science
The Doan Thi: Viet Nam Institute of Meteorology, Hydrology and Climate Change
Thuc Tran Duy: Viet Nam Institute of Meteorology, Hydrology and Climate Change
Hao Nguyen Thi Phuong: Vietnam National Space Center, Vietnam Academy of Science and Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 2, No 2, 1263 pages

Abstract: Abstract Tropical cyclone (TC)-induced rainfall is one of the most extreme rain phenomena, usually resulting in severe flooding and inundation when a TC makes landfall and is still considered the biggest challenge for TC forecasting. This study focuses on developing a R-CLIPER (Tropical Cyclone Rainfall Climatology and Persistence) model for TC-induced rainfall forecasting over the Vietnam region using precipitation data from GSMaP (Global Satellite Mapping of Precipitation) and TRMM (The Tropical Rainfall Measuring Mission). We used the best track data for 199 TCs affecting the Vietnam region during the period 2000–2021, obtained from the Regional Specialized Meteorological Center (RSMC) Tokyo Typhoon Center. The performance of the established R-CLIPER models for TC rainfall forecasting was assessed under different circumstances by categorizing the rain thresholds and TC intensities. The evaluations are conducted by comparing the 24-hour R-CLIPER model’s rainfall forecast against satellite-estimated and surface-observed rains, indicating a reasonable prediction of two model versions, namely, the R-GSMaP and R-TRMM models, with slight outperformance of the R-GSMaP over the R-TRMM model. In detailed analyses of three case studies, we found that the results of predicted TC-induced rainfall largely vary depending on both weather conditions and TC tracks. In addition, using the developed R-CLIPER model as a baseline, we evaluate the performances of three global NWP models in TC-induced rainfall prediction by calculating a percentage of improvement in the statistical scores over those of the R-CLIPER model. The results revealed the greatest improvement in the forecast by the Integrated Forecasting System (IFS) model.

Keywords: Statistics; Climatology and persistence; TC-induced rain; GSMaP; TRMM (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06828-2 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:spr:nathaz:v:121:y:2025:i:2:d:10.1007_s11069-024-06828-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-06828-2

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().