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Microwave Passive Ground-Based Retrievals of Cloud and Rain Liquid Water Path in Drizzling Clouds: Challenges and Possibilities

机译:微波被动地基对毛毛雨云和雨状液态水路径的取回:挑战和可能性

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摘要

Satellite and ground-based microwave radiometers are routinely used for the retrieval of liquid water path (LWP) under all atmospheric conditions. The retrieval of water vapor and LWP from ground-based radiometers during rain has proved to be a difficult challenge for two principal reasons: the inadequacy of the nonscattering approximation in precipitating clouds and the deposition of rain drops on the instrument's radome. In this paper, we combine model computations and real ground-based, zenith-viewing passive microwave radiometer brightness temperature measurements to investigate how total, cloud, and rain LWP retrievals are affected by assumptions on the cloud drop size distribution (DSD) and under which conditions a nonscattering approximation can be considered reasonably accurate. Results show that until the drop effective diameter is larger than ~200 μm, a nonscattering approximation yields results that are still accurate at frequencies less than 90 GHz. For larger drop sizes, it is shown that higher microwave frequencies contain useful information that can be used to separate cloud and rain LWP provided that the vertical distribution of hydrometeors, as well as the DSD, is reasonably known. The choice of the DSD parameters becomes important to ensure retrievals that are consistent with the measurements. A physical retrieval is tested on a synthetic data set and is then used to retrieve total, cloud, and rain LWP from radiometric measurements during two drizzling cases at the atmospheric radiation measurement Eastern North Atlantic site.
机译:卫星和地面微波辐射计通常用于在所有大气条件下获取液态水路径(LWP)。降雨期间,从地面辐射计中检索水汽和LWP已证明是一项艰巨的挑战,其主要原因有两个:降水云中非散射近似值的不足以及仪器天线罩上的雨滴沉积。在本文中,我们将模型计算与基于地面,天顶观测的无源微波辐射计实际亮度温度测量值相结合,以研究云滴大小分布(DSD)的假设如何影响总量,云量和雨量LWP的取回,以及在何种情况下在非散射近似条件下,可以认为是相当准确的。结果表明,直到墨滴有效直径大于〜200μm为止,无散射近似产生的结果在小于90 GHz的频率下仍是准确的。对于较大的液滴,表明较高的微波频率包含有用的信息,只要合理知道水凝物和DSD的垂直分布,这些信息就可以用于分离云和雨LWP。 DSD参数的选择对于确保与测量结果一致的检索非常重要。在合成数据集上测试了物理取回,然后将其用于在北大西洋东部辐射测量的两个毛毛雨情况下,从辐射度测量中获取总的,云量和雨量的LWP。

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