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“The results revealed a clear and meteorologically consistent seasonal distribution in sample counts, characterized by a pronounced peak during summer months and a distinct minimum in winter [69].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [69]
In the citing paper
“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 [69]
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Metadata
- Authors
- Sui, Y., Jiang, D., & Tian, Z.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Sui, Yue
- Venue
- Theoretical and Applied Climatology
- Published
- 2012-12-07
- Volume
- 113
- Issue
- 3-4
- Pages
- 599-610
- ISSN
- 0066-6424 (ISSN-L)
- 0177-798X
- 1434-4483
- OpenAlex
- W1972442935
- Cited by
- 85
- References
- 35
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 4 · 2023: 11 · 2022: 10 · 2021: 9 · 2020: 16 · 2019: 4 · 2018: 4 · 2017: 3 · 2016: 3 · 2015: 5 · 2014: 1 · 2013: 3
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Theoretical and Applied Climatology (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Keywords
- precipitation seasonality, precipitation climatology, precipitation trends, interannual variability, decadal variability, winter precipitation increase, China
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