基于云雷达和激光雷达协同的冰云特性检索

C. Tinel, J. Testud, J. Pelon, R. Hogan, A. Protat, J. Delanoë, D. Bouniol
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引用次数: 69

摘要

云是地球气候系统的重要组成部分。为了改进辐射传输计算,需要更好地描述它们的微物理性质。在地球、云、气溶胶和辐射探测器(EarthCARE)任务准备的框架中,由法国皮埃尔·西蒙·拉普拉斯研究所开发的雷达-激光雷达(RALI)机载系统可以用作机载演示器。本文提出了一种结合云雷达(94-95 GHz)和激光雷达数据推导云的辐射和微物理特性的方法。它结合了雷达的视后向散射反射率和激光雷达的视后向散射系数。该算法的原理依赖于消光系数与雷达比衰减之间的关系,该关系来源于机载微物理数据和Mie散射计算。为了求解云区的雷达和激光雷达方程,必须知道某参考距离z0处的消光系数。由于该算法对于从z0范围向发射器进行的反演是稳定的,因此在激光雷达观测到的更远的云边界处选择z0。然后,在消光系数与后向散射系数之间存在一定关系的假设下,推导出消光系数、视反射率、云物性参数、有效半径和冰含水量剖面。该算法应用于下视仪器的盲测,其中原始剖面来自于原位测量。它也被应用于真实的激光雷达和雷达数据,这些数据是在1998年云激光雷达和雷达实验(CLARE ' 98)现场项目中获得的,当时一架原型机载RALI系统指向最低点飞行。协同算法的计算结果与现场实测结果吻合较好。
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The Retrieval of Ice-Cloud Properties from Cloud Radar and Lidar Synergy
Clouds are an important component of the earth’s climate system. A better description of their microphysical properties is needed to improve radiative transfer calculations. In the framework of the Earth, Clouds, Aerosols, and Radiation Explorer (EarthCARE) mission preparation, the radar–lidar (RALI) airborne system, developed at L’Institut Pierre Simon Laplace (France), can be used as an airborne demonstrator. This paper presents an original method that combines cloud radar (94–95 GHz) and lidar data to derive the radiative and microphysical properties of clouds. It combines the apparent backscatter reflectivity from the radar and the apparent backscatter coefficient from the lidar. The principle of this algorithm relies on the use of a relationship between the extinction coefficient and the radar specific attenuation, derived from airborne microphysical data and Mie scattering calculations. To solve radar and lidar equations in the cloud region where signals can be obtained from both instruments, the extinction coefficients at some reference range z0 must be known. Because the algorithms are stable for inversion performed from range z0 toward the emitter, z0 is chosen at the farther cloud boundary as observed by the lidar. Then, making an assumption of a relationship between extinction coefficient and backscattering coefficient, the whole extinction coefficient, the apparent reflectivity, cloud physical parameters, the effective radius, and ice water content profiles are derived. This algorithm is applied to a blind test for downward-looking instruments where the original profiles are derived from in situ measurements. It is also applied to real lidar and radar data, obtained during the 1998 Cloud Lidar and Radar Experiment (CLARE’98) field project when a prototype airborne RALI system was flown pointing at nadir. The results from the synergetic algorithm agree reasonably well with the in situ measurements.
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