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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

刘黎平

刘黎平. 2023. 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[J]. 大气科学, 47(X): 1−16 doi: 10.3878/j.issn.1006-9895.2203.21199
引用本文: 刘黎平. 2023. 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[J]. 大气科学, 47(X): 1−16 doi: 10.3878/j.issn.1006-9895.2203.21199
LIU Liping. 2023. 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 Applications [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(X): 1−16 doi: 10.3878/j.issn.1006-9895.2203.21199
Citation: LIU Liping. 2023. 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 Applications [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(X): 1−16 doi: 10.3878/j.issn.1006-9895.2203.21199

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

doi: 10.3878/j.issn.1006-9895.2203.21199
基金项目: 国家自然基金项目41875036,国家重点研发计划项目2018YFC1507400
详细信息
    作者简介:

    刘黎平,男,1963年出生,博士,研究员,主要从事多波段垂直观测云雷达、X波段相控阵和偏振雷达探测技术和云降水微物理动力参数反演及其应用研究。E-mail: liulp@cma.gov.cn

  • 中图分类号: P412

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 Applications

Funds: National Natural Science Foundation of China (Grant 41875036), National Key Research and Development Program of China (Grant 2018YFC1507400)
  • 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日华南两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35 dBZ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。
  • 图  1  2020年6月8日03:16h~09:48h(北京时,下同)利用雨滴谱仪数据计算和采用DWR-SZ(a)约束前和(b)约束后300 m高度上正演得到的Ka、Ku回波强度(ZKusZKas)随时间变化曲线。(c、d)同(a、b),但为2021年6月1日05:44~12:47时段结果。图中,黑实线和红实线为雨滴谱观测的Ku和Ka波段的回波强度,黑虚线和红虚线分别表示雷达正演的回波强度

    Figure  1.  Comparisons between reflectivity estimated using the disdrometer data and obtained reflectivity at the height of 300 m (a) before and (b) after using DWR-SZ constrain conditions during 0316 BJT–0948 BJT (Beijing time) on June 8, 2020. (c) and (d) are same as (a) and (b), but for results during 0544 BJT–1247 BJT on June 1, 2021. The black and red solid lines are for the Ka and Ku band reflectivity from the disdrometer, respectively. The dashed lines are for the calculated reflectivity at the height of 300 m from DWR-SZ

    图  2  2020年6月8日03:16h~06:48h(a)未经过DWR约束时DWR-SZ计算得到的300 m高度上的雨滴谱和(b)经过约束订正后的雨滴谱及其(c)雨滴谱仪观测的雨滴谱的对比

    Figure  2.  Retrieved raindrop size distributions (a) before, (b) following DWR constrain at the height of 300 m, and (c) that observed by the disdrometer during 0316 BJT–0648 BJT on June 8, 2020

    图  3  2020年6月8日03:16h~06:48hM3和M4模式融合的(a)Ku波段、(b)Ka波段原始回波强度,DWR约束前计算的(c)Ku波段、(d)Ka波段回波强度,DWR约束后的计算的Ku波段(e)和Ka波段(f)回波强度Fig. 9 Time–height distributions of (a) Nw (number density, units: mm−1 m−3), (b) Dm (mass mean diameter, units: mm), (c) LWC (liquid water content, units: g m−3) retrieved by DWR-SZ drove by the Vair from STku algorithm and (d) simulated reflectivity during 0316 BJT–0648 BJT on June 8, 2020Same as Fig. 8, but for drove by Vair from STKu

    Figure  3.  Time–height figures of (a) Ku and (b) Ka bands merged reflectivity fields from the M3 and M4 modes, calculated (c) Ku and (d) Ka-band reflectivity before (e) and (f) after DWR constrained condition by DWR-SZ algorithm during 0316 BJT–0648 BJT on June 8, 2020

    图  4  (a)2020年6月8日06:05和(b)2021年6月1日07:46 Ka波段原始回波强度(raw)、第一次订正(cor1)和第二次订正(cor2)后的回波强度廓线的比较

    Figure  4.  Comparison of the profiles of raw Z (raw), corrected Z after attenuation (cor1), and that after systemic bias corrected (cor2) (a) at 0605 BJT on June 8, 2020 and (b) 0746 BJT on June 1, 2021

    图  5  2020年6月8日03:16h~06:48h(a)Ku波段、(b)Ka波段观测的回波强度、(c)M3和(d)M4数据DWSZ反演的Vair和(e)两者的偏差(M3减M4)时间—高度分布

    Figure  5.  Time–height distributions of (a) Ku-band and (b) Ka-band raw reflectivity, Vair from the (c) M3 and (d) M4 modes, and (e) their bias (M3 minus M4) using DWSZ during 0316 BJT–0648 BJT on June 8, 2020

    图  6  2020年6月8日03:16h~06:48h(a、b)STKa、(c、d)STKu方法分别使用(a、c)M3、(b、d)M4数据反演的Vair的时间—高度分布,(e)和(f)分别为两种方法使用不同模式结果的Vair偏差(M3减M4)时间—高度分布

    Figure  6.  Time–height distributions of Vair fields by the (a, b) STKa, (c, d) STKu algorithm by using (a, b) M3 and (c, d) M4 modes, and (e, f) their biases of Vair (M3 minus M4) during 0316 BJT–0648 BJT on June 8, 2020

    图  7  2020年6月8日03:16h~06:48h利用DWR反演的风场驱动DWR-SZ反演得到的(a)Nw(数密度,单位:mm−1 m−3),(b)Dm(质量平均直径,单位:mm)、(c)LWC(液态水含量,单位:g m−3)和(d)DWR反演的LWC的时间—高度分布

    Figure  7.  Time–height distributions of (a) Nw (number density, units: mm−1 m−3), (b) Dm (mass mean diameter, units: mm), (c) LWC (liquid water content, units: g m−3) retrieved by DWR-SZ drove by the Vair from DWR algorithm and (d) LWC by DWR during 0316 BJT–0648 BJT on June 8, 2020

    图  8  图7类似,但为STKa反演的Vair驱动

    Figure  8.  as Fig.7 but for drove by drove by Vair from STKa

    图  9  2020年6月8日03:16h~06:48h利用STKu反演的风场驱动DWR-SZ反演得到的(a)Nw(数密度,单位:mm−1 m−3),(b)Dm(质量平均直径,单位:mm)、(c)LWC(液态水含量,单位:g m−3)和(d)正演的Ka波段回波强度的时间—高度分布

    Figure  9.  Time–height distributions of (a) Nw (number density, units: mm−1 m−3), (b) Dm (mass mean diameter, units: mm), (c) LWC (liquid water content, units: g m−3) retrieved by DWR-SZ drove by the Vair from STku algorithm and (d) simulated reflectivity during 0316 BJT–0648 BJT on June 8, 2020 Same as Fig. 8, but for drove by Vair from STKu

    图  10  采用DWR-SZ、STKa和STKu方法得到的Vair计算得到的2020年6月8日06:05(a)回波强度、(b)Nw、(c)Dm和(d)LWC廓线的比较

    Figure  10.  Comparison of the profiles of (a) Z, (b) Nw, (c) Dm and (d) LWC retrieved by DWSZ with Vair from DWR-SZ, STKa, and STKu at 0605 BJT on June 8, 2020

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  • 收稿日期:  2022-07-04
  • 录用日期:  2022-06-16
  • 网络出版日期:  2022-06-24

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