Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
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A Review of the Observation-based Framework for the Study of Aerosol-Cloud-Precipitation Interactions (CAPI)

에어로솔-구름-강수 상호작용 (CAPI) 연구를 위한 관측 방법론 고찰

  • Kim, Byung-Gon (Department of Atmospheric Environmental Sciences, Gangneung-Wonju National University)
  • 김병곤 (강릉원주대학교 대기환경과학과)
  • Received : 2012.08.13
  • Accepted : 2012.09.25
  • Published : 2012.12.31
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Abstract

There is still large uncertainty in estimating aerosol indirect effect despite ever-escalating efforts and virtually exponential increase in published studies concerning aerosol-cloud-precipitation interactions (CAPI). Probably most uncertainty comes from a wide range of observational scales and different platforms inappropriately used, and inherent complex chains of CAPI. Therefore, well-designed field campaigns and data analysis are required to address how to attribute aerosol signals along with clouds and precipitation to the microphysical effects of aerosols. Basically, aerosol influences cloud properties at the microphysical scales, "process scale", but observations are generally made of bulk properties over a various range of temporal and spatial resolutions, "analysis scale" (McComiskey & Feingold, 2012). In the most studies, measures made within the wide range of scales are erroneously treated as equivalent, probably resulting in a large uncertainty in associated with CAPI. Therefore, issues associated with the disparities of the observational resolution particular to CAPI are briefly discussed. In addition, the dependence of CAPI on the cloud environment such as stability and adiabaticity, and observation characteristics with varying situations of CAPI are also addressed together with observation framework optimally designed for the Korean situation. Properly designed and observation-based CAPI studies will likely continue to accumulate new evidences of CAPI, to further help understand its fundamental mechanism, and finally to develop improved parameterization for cloud-resolving models and large scale models.

Keywords

References

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