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A novel method for ground-based remote sensing of profiles of cloud microphysical properties

云微物理特性剖面地基遥感新方法

基本信息

批准号：
256770551
负责人：
Professor Dr. Bernhard Mayer
金额：
--
依托单位：
Meteorologisches Institut
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/256770551?language=en
关键词：
novel method ground based remote

项目摘要

In this project we develop and apply a novel, ground-based combined passive and active remote sensing method to retrieve vertical profiles of microphysical parameters of convective clouds. Deep convection plays an essential role for climate since it has a large influence on the solar radiation budget as well as on the global water cycle. The lifetime and albedo of deep convective clouds are influenced by the interactions between the cloud droplet growth along the cloud profile and the aerosol uptake. As remote sensing applications from space usually can only observe cloud tops, the ground-based remote sensing of cloud sides can be an essential tool for the investigation of cloud droplet growth. Since classical, passive retrievals of the cloud droplet size are commonly based on 1D radiative transfer calculations they fail when faced with complex cloud geometries on a high spatial resolution. In our project we investigate the implications of a inhomogeneous cloud geometry for the remote sensing of cloud droplet profiles and consider the distinct threedimensional nature of convective clouds by extensive 3D radiative transfer calculations. As an essential part of this project we are combining a passive spectrometer with an active cloud radar into a synchronous measurement system. Based on data measured with the passive spectrometer and active cloud radar, we develop a geometry-aware retrieval of cloud microphysics. With this proposal we provide an interim report of the first funding period and apply for one additional year for the PhD candidate in order to enable him to successfully complete this project and to obtain his doctorate degree. In this additional year the methods developed for the spectral imager and the cloud radar will be combined into a geometry-aware retrieval of cloud microphysics. This retrieval will then be tested on measurements and validated with in-situ data gathered during the HDCP2 field campaign.

在本项目中，我们开发并应用了一种新的地面被动与主动相结合的遥感方法来检索对流云的微物理参数垂直剖面。深层对流对气候有重要作用，因为它对太阳辐射收支和全球水循环有很大的影响。深对流云的寿命和反照率受云滴沿云廓线生长和气溶胶吸收的相互作用的影响。由于空间遥感应用通常只能观测云顶，云侧地面遥感可以成为研究云滴生长的重要工具。由于云滴大小的经典被动检索通常基于一维辐射传输计算，当面对高空间分辨率的复杂云几何形状时，它们会失败。在我们的项目中，我们研究了非均匀云几何对云滴剖面遥感的影响，并通过广泛的三维辐射传输计算考虑对流云的独特三维性质。作为这个项目的一个重要组成部分，我们将一个被动光谱仪与一个主动云雷达结合成一个同步测量系统。基于被动光谱仪和主动云雷达测量的数据，我们开发了一种几何感知的云微物理检索方法。根据此建议，我们将提供第一资助期的中期报告，并为博士候选人申请一年的额外资助，以使他能够顺利完成该项目并获得博士学位。在这额外的一年里，为光谱成像仪和云雷达开发的方法将被结合到云微物理的几何感知检索中。然后，将在HDCP2现场作业期间对测量结果进行测试，并使用现场收集的数据进行验证。