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ISSN 1006-9895

CN 11-1768/O4

基于Ka/Ku双波段回波强度差约束和多普勒功率谱的微物理和动力参数反演方法和应用
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中国气象科学研究院灾害天气国家重点实验室 北京100086

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国家自然科学基金项目41875036,国家重点研发计划项目2018YFC1507400


Air vertical motion and raindrop size distribution retrieval algorithm based on reflectivity spectral density data and dual wavelength ratio constraint with Ka/Ku dual-wavelength cloud radar and its preliminary applicationLiu Liping
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    摘要:

    回波强度定标误差、天线水膜衰减和雨区衰减造成的回波强度偏差对云雷达反演微物理和动力参数有非常重要的影响,准确分析这些偏差对提高反演精度至关重要。为了消除云雷达因定标和天线罩等引起的回波强度和功率谱大小的影响,实现高精度和雷达全观测范围的反演,本文提出了基于Ka/Ku双波段云雷达回波强度差约束和回波强度谱密度数据的降水内空气垂直运动速度和雨滴谱反演方法(DWR-SZ),并将该方法应用到2020年6月8日和 2021年6月1日华南二次对流性云降水垂直结构观测数据,利用雨滴谱仪数据分析了该方法反演结果的改进程度,分析了上升速度对反演的回波强度和微物理参数的影响。该方法首先融合双波段云雷达反演(DWSZ)和单波段小粒子跟踪方法(ST)方法反演的云内空气垂直速度Vair,形成全观测域的Vair,然后利用DWSZ方法得到微物理参数初估值,并计算衰减影响,最后利用双波段回波强度差(DWR)调整回波强度系统偏差和反演的微物理参数,使DWR-SZ方法正演得到的DWR与雷达观测值差到达极小。结果表明:(1)采用脉冲压缩技术的高雷达灵敏度模式与采用短脉冲的低灵敏度模式相比,DWSZ方法反演的Vair与雷达灵敏度相关性非常小,结果稳定,但这种方法只能应用于含有大粒子的液体降水区(粒子直径大于1.8mm);小粒子跟踪ST方法通常低估Vair,但在低层的35dBZ以下降水Vair低估程度不大,且灵敏度提高会极大改进Ka波段雷达反演能力;两种方法融合的Vair比较合理;(2)雨区衰减和距离是造成ST方法 低估Vair的主要原因;而固态降水的功率谱非常窄而且陡,灵敏度对固态降水区Vair影响不大;(3)采用DWR作为约束,有效减小了回波强度的系统偏差和天线水膜影响,提高了微物理参数的反演准确率;(4)ST方法反演的Vair高估了粒子数密度,液体含水量(LWC)和衰减系数,低估了粒子大小,但对天线水膜引起的回波强度系统偏差影响不大。

    Abstract:

    Reflectivity calibration error, attenuation in rain area and water cover over cloud radar antenna had serious effects on retrieved microphysical and dynamic parameters with reflectivity spectral density data, it is key problem to analyze these errors in retrieving microphysical and dynamic parameters. Aiming at reduce the effects of observation bias of reflectivity introduced by calibration and attenuation of water cover over cloud radar antenna, a retrieval algorithm for air vertical motion (Vair) and raindrop size distribution (DSD) based on reflectivity spectral density data and dual wavelength ratio (DWR) constraint with Ka/Ku dual-wavelength cloud radar (DWCR) are presented in this paper, The disdrometer data were used to examine the retrieved parameters. The effects of air vertical speed on retrieved microphysical parameters are discussed. In the algorithm (DWR-SZ), Vair retrieved from single Ka/Ku band CR (ST) and DWCR algorithms (DWSZ) are merged to form Vair in all of observation area, then the initial DSD and attenuation are retrieved by using DWSZ algorithm. Finally, DWR between first and last ranges in liquid area in a beam are used to adjust the reflectivity bias and retrieved final DSD to minimize the difference between the cloud radar observed and calculated DWR. Two convective precipitation cases in June 8, 2020 and June 1 2021 in Longmen, Guangdong Province, are used to examine the retrieved results. The results show that the radar sensitivity variations have little effects on Vair retrieved from DWSZ, however, the DWSZ cloud only used in the areas containing big rain drop (diameter large larger than 1.8mm). ST algorithms wit Ka and Ku data underestimated Vair, however, the Vair are reasonable in low levels with reflectivity weaker than 35dBZ. Highly sensitive work mode with pulse compressions could improve the Vair retrieval bias. Merged Vair from ST and DWSZ algorithms are reasonable. The attenuation and far radar range could introduce the underestimations of Vair with ST algorithms, the underestimations of Vair in solid precipitation area are negational due the sharp variation and narrow of reflectivity spectral density data. Using constraint condition of DWR reduced the bias of observed reflectivity and effects of water cover over antenna, improve the retrieval results. Vair from ST used in DWR-SZ overestimated drop number, liquid water content (LWC) and attenuation coefficient and underestimated drop size, however, it has no effects on reflectivity bias produced by water cover over antenna.

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  • 收稿日期:2021-10-28
  • 最后修改日期:2022-03-09
  • 录用日期:2022-06-16
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