Comparison of the Applicability of Mainstream Objective Circulation Type Classification Methods in China
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| 發表在: | Atmosphere vol. 16, no. 11 (2025), p. 1231-1249 |
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| 主要作者: | |
| 其他作者: | , |
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MDPI AG
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| 主題: | |
| 在線閱讀: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 022 | |a 2073-4433 | ||
| 024 | 7 | |a 10.3390/atmos16111231 |2 doi | |
| 035 | |a 3275501601 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231428 |2 nlm | ||
| 100 | 1 | |a Ma Minjin | |
| 245 | 1 | |a Comparison of the Applicability of Mainstream Objective Circulation Type Classification Methods in China | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Circulation type classification (CTC) is an important method in atmospheric sciences, which reveals the relationship between atmospheric circulation and regional weather and climate. Accurate circulation classification helps to improve weather forecasting accuracy and supports climate change research. China has complex topography and significant spatiotemporal variability in its circulation patterns, making the study of circulation type classification in this region highly significant. This study aims to evaluate the applicability of several mainstream objective CTC methods in the China region. We applied methods including T-mode principal component analysis (PCT), Ward linkage, K-means, and Self-Organizing Maps (SOM) to classify the sea-level pressure daily mean fields from 1993 to 2023 in the study area, and compared the classification results in terms of internal metrics, continuity, seasonal variation, separability of related meteorological variables (e.g., temperature, precipitation), and stability to spatiotemporal resolution. The results show that each method has its advantages in different contexts, with the K-means method showing the best overall performance. Additionally, an optimized approach combining PCT and K-means is proposed. | |
| 651 | 4 | |a China | |
| 653 | |a Weather forecasting | ||
| 653 | |a Climate change | ||
| 653 | |a Islands | ||
| 653 | |a Seasonal variation | ||
| 653 | |a Classification | ||
| 653 | |a Forecast accuracy | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Atmospheric circulation | ||
| 653 | |a Weather | ||
| 653 | |a Sea level pressure | ||
| 653 | |a Seasonal variations | ||
| 653 | |a Climatic classifications | ||
| 653 | |a Atmospheric sciences | ||
| 653 | |a Circulation patterns | ||
| 653 | |a Precipitation | ||
| 653 | |a Climate and weather | ||
| 653 | |a Linkage analysis | ||
| 653 | |a Self organizing maps | ||
| 653 | |a Circulation | ||
| 653 | |a Climate change research | ||
| 653 | |a Circulation types | ||
| 653 | |a Methods | ||
| 700 | 1 | |a Chen, Ran | |
| 700 | 1 | |a Zhang Xingyu | |
| 773 | 0 | |t Atmosphere |g vol. 16, no. 11 (2025), p. 1231-1249 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275501601/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275501601/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275501601/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |