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三频雷达遥感云参数能力的进一步模拟与分析

Further Simulation and Analysis of the Triple-frequency Radar in Detecting Cloud

  • 摘要: 本文在研究团队前期工作基础上,针对非降水云中多种形态的冰晶粒子开展了三种典型短波雷达(X波段-9.5GHZ-3cm、Ka波段-35GHZ-8mm、W波段-94GHZ-3mm)探测的模拟,对比分析了三种波长情况下粒子物理特征改变所引起的雷达回波变化情况,分析了联合利用三波长雷达探测数据识别冰晶形态的可能性。研究结果表明,W波段尽管波长短,灵敏度高,但是冰晶后向散射特征相比X波段和Ka波段要复杂很多,入射角度、粒子形态、粒子大小等的不同均会引起后向散射剧烈而复杂的改变,对基于统计或经验拟合法来提取云粒子微物理特征的反演方法带来挑战。已有分析结果亦显示,利用多波长雷达观测量之间的差异特征,有助于提供云内粒子微物理信息。例如,冰云中当W波段与Ka波段或W波段与X波段之间的反射率因子差值明显较大时,例如低于-3dBZ时,则很可能存在“长”粒子,这些工作为基于多波长雷达回波反演云微物理参数算法的建立和精度提高提供了参考和经验。

     

    Abstract: Based on our prior research, this paper carries out the simulation of three typical short-wavelength radar detections (X-band-9.5GHz-3cm, Ka-band-35GHz-8mm, and W-band-94GHz-3mm) for non-precipitation cloud particles, particularly ice crystals. It further compares and analyzes the radar reflectivities stemming from variations in the particles" physical attributes. Additionally, the study explores the feasibility of employing these three radar wavelengths to discern the morphology of ice crystals. The results show that, despite the W-band"s short wavelength and heightened sensitivity, the backscattering characteristics of ice crystals exhibit a considerably more intricate pattern than those observed in the X-band and Ka-band. Variations in incident angle, particle morphology, and particle size elicit dramatic and complex alterations in backscattering, posing a formidable challenge to traditional inversion methods that rely on statistical or empirical fitting approaches to extract cloud particle microphysical properties. It has also been shown that utilizing the characteristics of the differences between multi-wavelength radar observations can help to provide microphysical information about particles in clouds. For example, in the ice cloud, a pronounced disparity in reflectivity factor between the W-band and either the Ka-band or X-band, particularly when it dips below -3 dBZ, suggests the likelihood of prolate particle presence. This offers invaluable insights and practical experience for refining and enhancing the accuracy of algorithms designed to invert cloud microphysical features based on triple-frequency radar data.

     

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