Typhoon activity and some important parameters in the South China Sea
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This study aims to statistically describe temporal and spatial variations of sea surface temperature (SST), latent heat flux (LHF), sensible heat flux (SHF), and precipitation rate with typhoon activity over the South China Sea. The correlations of the parameters and their connections with the physical phenomena are clearly presented. This is fundamental to predict a typhoon's intensity and track. The effects were investigated from 1991 to 2011 based on archived data from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR) and the number of typhoons were sourced from the International Best Track Archive for Climate Stewardship (IBTrACS). The results showed that most typhoons occurred in August and September, which was related to high temperature in the summer season and the southwest monsoon in the area. The maximum mean values of SST in May and June were related to the East Asian Monsoon. The average values of LHF were highest in July, and the mean values of SHF were highest in July and August. SHF varied gradually at different months compared with LHF. In addition, the average of precipitation rate was highest in November, which can be related to the northeasterly winter monsoon. The relationships of the aforementioned parameters were obtained using Pearson's correlation analysis. Moreover, the highest and lowest mean values of the parameters in different areas were considered, and their spatial relationships were analyzed.
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The lower summer recall and F1-scores are likely consistent with the more complex, localized, and microphysically diverse nature of warm-season convective rainfall, which presents a greater challenge for the QC algorithm.
“This performance pattern is likely consistent with the more complex, localized, and microphysically diverse nature of warm-season convective rainfall [69][70][71], which presents a greater challenge for the QC algorithm.”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [70]
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A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data
Sun, H., Zhou, Q., Shi, L., Li, C., Qin, S., Yao, D., Xu, M., Huang, Y., Hu, Q., & Guan, Y. (2025) · Remote Sensing · doi:10.3390/rs17243976 · Cited by 4 · see citing works
- Authors
- Haghroosta, T., & Ismail, W. R.
- Affiliations
- Universiti Sains Malaysia (MY)
- Corresponding author
- Haghroosta, Tahereh
- Venue
- Weather and Climate Extremes
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 17
- Pages
- 29-35
- ISSN
- 2212-0947 (ISSN-L)
- OpenAlex
- W2750240018
- Cited by
- 33
- References
- 0
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 7 · 2020: 4 · 2019: 4 · 2018: 1 · 2017: 1
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Weather and Climate Extremes (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Tropical and Extratropical Cyclones Research
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- typhoon activity, South China Sea, sea surface temperature, latent heat flux, sensible heat flux, precipitation rate, seasonal variation, southwest monsoon, East Asian monsoon, winter monsoon, spatial distribution
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