环境科学
能见度
可预测性
气象学
风速
湍流动能
雾
大气科学
下蒙蒙细雨
液态水含量
云计算
气溶胶
湍流
地理
地质学
物理
量子力学
作者
Ismail Gultepe,Andrew J. Heymsfield,Harindra J. S. Fernando,Eric R. Pardyjak,Clive E. Dorman,Qian Wang,E. Creegan,Sebastian W. Hoch,D. D. Flagg,Ryoji Yamaguchi,R. Krishnamurthy,Saša Gaberšek,William Perrie,A. O. Perelet,D. K. Singh,Robert P. H. Chang,B. Nagare,Santosh Wagh,Song-Ming Wang
标识
DOI:10.1007/s10546-021-00659-5
摘要
Our goal is to provide an overview of the microphysical measurements made during the C-FOG (Toward Improving Coastal Fog Prediction) field project. In addition, we evaluate microphysical parametrizations using the C-FOG dataset. The C-FOG project is designed to advance understanding of liquid fog formation, particularly its development and dissipation in coastal environments, so as to improve fog predictability and monitoring. The project took place along eastern Canada’s (Nova Scotia and Newfoundland) coastlines and open water environments from August−October 2018, where environmental conditions play an important role for late-season fog formation. Visibility, wind speed, and atmospheric turbulence along coastlines are the most critical weather-related factors affecting marine transportation and aviation. In the analysis, microphysical observations are summarized first and then, together with three-dimensional wind components, used for fog intensity (visibility) evaluation. Results suggest that detailed microphysical observations collected at the supersites and aboard the Research Vessel Hugh R. Sharp are useful for developing microphysical parametrizations. The fog life cycle and turbulence-kinetic-energy dissipation rate are strongly related to each other. The magnitudes of three-dimensional wind fluctuations are higher during the formation and dissipation stages. An array of cutting-edge instruments used for data collection provides new insight into the variability and intensity of fog (visibility) and microphysics. It is concluded that further modifications in microphysical observations and parametrizations are needed to improve fog predictability of numerical-weather-prediction models.
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