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The Thermodynamic Structure of Arctic Coastal Fog Occurring During the Melt Season over East Greenland

机译:在东格陵兰融化期间发生北极沿海雾的热力学结构

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An automated method to classify Arctic fog into distinct thermodynamic profiles using historic in-situ surface and upper-air observations is presented. This classification is applied to low-resolution Integrated Global Radiosonde Archive (IGRA) soundings and high-resolution Arctic Summer Cloud Ocean Study (ASCOS) soundings in low- and high-Arctic coastal and pack-ice environments. Results allow investigation of fog macrophysical properties and processes in coastal East Greenland during melt seasons 1980-2012. Integrated with fog observations from three synoptic weather stations, 422 IGRA soundings are classified into six fog thermodynamic types based on surface saturation ratio, type of temperature inversion, fog-top height relative to inversion-base height and stability using the virtual potential temperature gradient. Between 65-80% of fog observations occur with a low-level inversion, and statically neutral or unstable surface layers occur frequently. Thermodynamic classification is sensitive to the assigned dew-point depression threshold, but categorization is robust. Despite differences in the vertical resolution of radiosonde observations, IGRA and ASCOS soundings yield the same six fog classes, with fog-class distribution varying with latitude and environmental conditions. High-Arctic fog frequently resides within an elevated inversion layer, whereas low-Arctic fog is more often restricted to the mixed layer. Using supplementary time-lapse images, ASCOS microwave radiometer retrievals and airmass back-trajectories, we hypothesize that the thermodynamic classes represent different stages of advection fog formation, development, and dissipation, including stratus-base lowering and fog lifting. This automated extraction of thermodynamic boundary-layer and inversion structure can be applied to radiosonde observations worldwide to better evaluate fog conditions that affect transportation and lead to improvements in numerical models.
机译:提出了一种自动化方法,将北极雾分成不同的历史原位表面和上空气观测的不同热力学配置。该分类适用于低分辨率集成全球无线电集团档案(IGRA)探测和高分辨率北极夏季云海洋研究(ASCOS)探测,在低北极沿海和包装冰环境。在1980 - 2012年熔体季节期间,散发季节沿海东格陵兰雾雾宏观物理特性及过程调查。与三个舞台气象站的雾观测集成,基于表面饱和度比,422 IGRA探测分为六种雾热力学类型,温度反转类型,相对于使用虚拟电位温度梯度的反转基础高度和稳定性。在65-80%的雾观察中发生在低水平的反转中发生,并且常见地发生静态中性或不稳定的表面层。热力学分类对分配的露点抑制阈值敏感,但分类是强大的。尽管垂直分辨率的垂直分辨率差异,但IGRA和ASCOS探测产生了相同的六个雾类,雾类分布随纬度和环境条件而变化。高北极雾经常存在于升高的反转层内,而低北极雾更常用于混合层。使用补充时间流逝图像,ASCOS微波辐射计检索和气动回溯轨迹,我们假设热力学课程代表平流雾形成,开发和耗散的不同阶段,包括层压下降和雾化提升。这种自动提取热力学边界层和反转结构可以应用于全球的无线电探测观察,以更好地评估影响运输的雾条件,并导致数值模型的改进。

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