Arctic Cloud Microphysics Retrievals from Surface-Based Remote Sensors at SHEBA

M. Shupe, T. Uttal, S. Matrosov
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引用次数: 151

Abstract

Abstract An operational suite of ground-based, remote sensing retrievals for producing cloud microphysical properties is described, assessed, and applied to 1 yr of observations in the Arctic. All measurements were made in support of the Surface Heat Budget of the Arctic (SHEBA) program and First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE) in 1997–98. Retrieval techniques and cloud-type classifications are based on measurements from a vertically pointing 35-GHz Doppler radar, microwave and infrared radiometers, and radiosondes. The retrieval methods are assessed using aircraft in situ measurements from a limited set of case studies and by intercomparison of multiple retrievals for the same parameters. In all-liquid clouds, retrieved droplet effective radii Re have an uncertainty of up to 32% and liquid water contents (LWC) have an uncertainty of 49%–72%. In all-ice clouds, ice particle mean sizes Dmean can be retrieved with an uncertainty of ...
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谢巴地面遥感器北极云微物理反演
摘要:本文描述、评估了一套用于产生云微物理特性的地面遥感检索操作套件,并将其应用于北极1年的观测。所有的测量都是为了支持1997-98年的北极地表热收支(SHEBA)计划和第一国际卫星云气候学计划区域实验(FIRE)北极云实验(ACE)。检索技术和云类型分类基于垂直指向的35 ghz多普勒雷达、微波和红外辐射计以及无线电探空仪的测量结果。通过对有限的案例研究的飞机现场测量和对相同参数的多次检索的相互比较,对检索方法进行了评估。在全液态云中,回收的液滴有效半径Re的不确定度高达32%,液态水含量(LWC)的不确定度为49% ~ 72%。在全冰云中,冰粒子平均尺寸Dmean的不确定度为…
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