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공기괴 역궤적 분석을 위한 FLEXPART Lagrangian Particle Dispersion 모델의 최적화 및 자동화
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  • 공기괴 역궤적 분석을 위한 FLEXPART Lagrangian Particle Dispersion 모델의 최적화 및 자동화
저자명
김주일,박선영,박미경,리선란,김재연,조춘옥,김지윤,김경렬,Kim. Jooil,Park. Sunyoung,Park. Mi-Kyung,Li. Shanlan,Kim. Jae-Yeon,Jo. Chun Ok,Kim. Ji-Yoon,Kim.
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권/호정보
2013년|23권 1호|pp.93-102 (10 pages)
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한국기상학회
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

Atmospheric transport pathway of an air mass is an important constraint controlling the chemical properties of the air mass observed at a designated location. Such information could be utilized for understanding observed temporal variabilities in atmospheric concentrations of long-lived chemical compounds, of which sinks and/or sources are related particularly with natural and/or anthropogenic processes in the surface, and as well as for performing inversions to constrain the fluxes of such compounds. The Lagrangian particle dispersion model FLEXPART provides a useful tool for estimating detailed particle dispersion during atmospheric transport, a significant improvement over traditional "single-line" trajectory models that have been widely used. However, those without a modeling background seeking to create simple back-trajectory maps may find it challenging to optimize FLEXPART for their needs. In this study, we explain how to set up, operate, and optimize FLEXPART for back-trajectory analysis, and also provide automatization programs based on the open-source R language. Discussions include setting up an "AVAILABLE" file (directory of input meteorological fields stored on the computer), creating C-shell scripts for initiating FLEXPART runs and storing the output in directories designated by date, as wells as processing the FLEXPART output to create figures for a back-trajectory "footprint" (potential emission sensitivity within the boundary layer). Step by step instructions are explained for an example case of calculating back trajectories derived for Anmyeon-do, Korea for January 2011. One application is also demonstrated in interpreting observed variabilities in atmospheric $CO_2$ concentration at Anmyeon-do during this period. Back-trajectory modeling information introduced in this study should facilitate the creation and automation of most common back-trajectory calculation needs in atmospheric research.