인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
This study classifies summer atmospheric states using single-point vertical profiling from the Boseong Meteorological Tower (June~August, 2015~2021) and links the resulting clusters to precipitation characteristics and large-scale circulation patterns. Principal component analysis (PCA) and K-means clustering were applied independently to monthly anomaly profiles of potential temperature (θ), relative humidity (RH), and zonal and meridional wind components (u', v'), yielding three clusters per month (nine clusters in total). The nine clusters were subsequently categorized into four precipitation regimes — Favorable, Frequency-type, Intensity-type, and Dry — based on precipitation occurrence probability and the 90<sup>th</sup>-percentile precipitation amount. Composite analysis using ERA5 reanalysis data revealed that the Favorable regime is characterized by negative 500-hPa geopotential height anomalies coupled with south-westerly low-level moisture transport, forming a canonical precipitation-enhancing structure. In contrast, certain Dry-regime clusters exhibited a paradoxical suppression of precipitation despite strong dynamical forcing, attributable to a thermodynamic moisture deficit in the lower troposphere. Some of the Frequency-type clusters showed anomalously high precipitation frequency despite upper-level anticyclonic circulation, underscoring the dominant role of low-level moisture convergence. The Intensity-type regime demonstrated that dynamical forcing can preferentially amplify precipitation intensity rather than frequency, suggesting that these two aspects of precipitation are governed by distinct atmospheric mechanisms. The profiling-based classifications were linked to precipitation characteristics, and ERA5 composites showed distinct large-scale circulation patterns for each regime. The proposed framework holds potential for portability to other observation sites and for integration with numerical prediction systems, offering a cost-effective approach to precipitation diagnosis in observation-limited regions.
#Atmospheric state classification
#Single-point vertical profiling
#K-means clustering
#Precipitation regime
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목차
- Abstract
- 1. 서론
- 2. 자료 및 분석 방법
- 3. 결과
- 4. 요약 및 결론
- REFERENCES
참고문헌
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