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GNSS极化测量降雨正演模拟与实验对比

苏豆豆 白伟华 杜起飞 孙越强 谭广远

苏豆豆, 白伟华, 杜起飞, 孙越强, 谭广远. GNSS极化测量降雨正演模拟与实验对比[J]. 空间科学学报, 2022, 42(5): 961-972. doi: 10.11728/cjss2022.02.210409049
引用本文: 苏豆豆, 白伟华, 杜起飞, 孙越强, 谭广远. GNSS极化测量降雨正演模拟与实验对比[J]. 空间科学学报, 2022, 42(5): 961-972. doi: 10.11728/cjss2022.02.210409049
SU Doudou, BAI Weihua, DU Qifei, SUN Yueqiang, TAN Guangyuan. Forward Simulation and Comparative Experiment Analysis of Polarimetric GNSS Radio Occultations Detecting Rainfall Events (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 961-972 doi: 10.11728/cjss2022.02.210409049
Citation: SU Doudou, BAI Weihua, DU Qifei, SUN Yueqiang, TAN Guangyuan. Forward Simulation and Comparative Experiment Analysis of Polarimetric GNSS Radio Occultations Detecting Rainfall Events (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 961-972 doi: 10.11728/cjss2022.02.210409049

GNSS极化测量降雨正演模拟与实验对比

doi: 10.11728/cjss2022.02.210409049
详细信息
    作者简介:

    苏豆豆:E-mail:sudoudou18@mails.ucas.ac.cn

  • 中图分类号: P412

Forward Simulation and Comparative Experiment Analysis of Polarimetric GNSS Radio Occultations Detecting Rainfall Events

  • 摘要: 研究证明,全球导航卫星系统(GNSS)极化无线电掩星(PRO)技术可以用于探测降雨。利用GPM DPR降雨率数据与PAZ卫星极化相移观测数据匹配,筛选出代表性降雨事件。通过选用TB等7种雨滴形状和MP等5种雨滴谱模型,采用T矩阵法对各事件进行正演,并分析PAZ极化相移的线性校正值、天线相位校正值与正演模拟值之间的关系。对比分析得出线性校正值、相位校正值与模拟值的相关系数分别为0.9994和0.9933,均方根差分别为0.3429和1.2765。模拟值与实测值之间高度相关,且更接近线性校正值。进一步的研究表明,模拟降雨率在1 mm·h–1以下的事件时,雨滴谱采用MP或JD分布,雨滴形状采用SC或PB的模拟精度更高;降雨率在1 mm·h–1以上的事件,雨滴谱采用MP或 SS分布,雨滴形状采用TB的模拟结果最优。

     

  • 图  1  降雨正演模拟实验设计流程

    Figure  1.  Flow chart of rainfall forward

    图  2  2018年12月19日降雨事件雨区模拟

    Figure  2.  Rain zone simulation of the rain event on 19 December 2018

    图  3  2018年12月19日 16:54 UT掩星切点轨迹变化

    Figure  3.  Tangent points trajectory of the occultation event at 16:54 UT on 19 December 2018

    图  4  2019年12月19日降雨事件雨区模拟

    Figure  4.  Rain zone simulation of the rain event on 19 December 2019

    图  5  2019年12月19日21:14 UT时的掩星切点轨迹

    Figure  5.  Tangent points trajectory of the occultation event at 21:14 UT on 19 December 2019

    图  6  所有降雨事件的参数

    Figure  6.  Parameter value of all rainfall events

    图  7  $ \Delta \varPhi $$ \Delta {\varPhi }_{\mathrm{l}\mathrm{i}\mathrm{n}} $$ \Delta {\varPhi }_{\mathrm{a}\mathrm{n}\mathrm{t}} $ 的关系

    Figure  7.  Relationship between $ \Delta \varPhi $ and $ \Delta {\varPhi }_{\mathrm{l}\mathrm{i}\mathrm{n}} $, $ \Delta {\varPhi }_{\mathrm{a}\mathrm{n}\mathrm{t}} $

    表  1  正演所用雨滴模型

    Table  1.   Raindrop models used in forwarding performance

    雨滴模型模型公式
    BC (Beard-Chuang)$b/a=1.0048+5.7\times 1{0}^{-4}D-2.628\times 1{0}^{-2} {D}^{2}$
    $+3.682\times 1{0}^{-3} {D}^{3}-1.677\times 1{0}^{-4} {D}^{4}$
    ($ D\leqslant 7\;\mathrm{m}\mathrm{m} $)
    obA (Oguchi)$b/a=1-0.1 \dfrac{D}{2}$ (较大雨滴)
    obB (Oguchi)$b/a=1-\dfrac{0.41}{9} D$ (较大雨滴)
    PB (Pruppacher-Beard)$b/a=1.03-0.062 D$ ($ 1\leqslant D\leqslant 9\;\mathrm{m}\mathrm{m} $)
    TB (Thurai-Bringi)$b/a=1.065-6.25\times 1{0}^{-2} D-3.99\times 1{0}^{-3} {D}^{2}$
    $ +7.66\times 1{0}^{-4}{D}^{3}-4.095\times 1{0}^{-5} {D}^{4} $
    ($ \text{1.5}\leqslant D\leqslant 9\;\mathrm{m}\mathrm{m} $)
    SC (Steinert-Chandra)${b}/{a}=1-10^{-2}\;{D^2}$
    GR (Green)$b/a=1.0148-2.0465\times 1{0}^{-2} D-2.0048\times 1{0}^{-2} {D}^{2}$
    $+3.09\times 1{0}^{-3} {D}^{3}-1.543\times 1{0}^{-4} {D}^{4}$
    下载: 导出CSV

    表  2  正演所用雨滴谱分布

    Table  2.   Raindrop size distribution models used in forwarding performance

    分布模型表达式$/({\rm{m}}^{-3} \cdot {\rm{mm}}^{-1}) $经验参数
    Marshall–Palmer (MP)分布 $N\left(D\right)={N}_{0}{{\rm{e}}}^{-\varLambda D}$ $ {N}_{0}=8000 $,$ \varLambda =4.1{R}^{-0.21} $
    Laws-Parsons (LP)分布 $ {N}_{0}=5100{R}^{-0.03} $,$ \varLambda =3.8{R}^{-0.2} $
    Sekhon-Srivastava (SS)分布 $ {N}_{0}=7000{R}^{0.37} $,$ \varLambda =3.8{R}^{-0.14} $
    Joss分布 毛毛雨 (JD) $ {N}_{0}=30000 $,$ \varLambda =5.7{R}^{-0.21} $
    广布雨 (JW) $ {N}_{0}=7000 $,$ \varLambda =4.1{R}^{-0.21} $
    雷暴雨 (JT) ${N}_{0}=1400 $,$ \varLambda =3.0{R}^{-0.21} $
    下载: 导出CSV

    表  3  2018年12月19日降雨事件正演结果

    Table  3.   Forward results of the rain event on 19 December 2018

    雨滴形状雨滴谱分布模型$ {\Delta }{\varPhi }_{\mathrm{l}\mathrm{i}\mathrm{n}} $$ {\Delta }{\varPhi }_{\mathrm{a}\mathrm{n}\mathrm{t}} $
    MPLPSSJDJW
    BC9.72859.555013.28068.873817.02564.72286.0154
    obA21.671419.649627.971831.074937.9248
    obB19.712217.868425.437128.277434.4919
    PB14.559813.981319.571414.045025.4842
    TB2.18433.70854.5574–12.02053.8248
    SC5.66365.51467.67815.86499.9055
    GR11.710211.438515.931410.504820.4895
    下载: 导出CSV

    表  4  2019年12月19日降雨事件正演结果

    Table  4.   Forward results of the rain event on 19 December 2019

    雨滴形状雨滴谱分布模型$ {\Delta }{\varPhi }_{\mathrm{l}\mathrm{i}\mathrm{n}} $$ {\Delta }{\varPhi }_{\mathrm{a}\mathrm{n}\mathrm{t}} $
    MPLPSSJDJW
    BC42.674837.383054.756042.655474.693950.243052.8257
    obA74.691962.034098.3237106.8904130.7056
    obB67.880956.375389.371597.2678118.7923
    PB58.864550.514876.371265.3379103.0087
    TB26.956226.020032.9855–3.622047.1618
    SC24.629721.683531.541325.390643.1193
    GR50.185343.698664.662951.375687.8163
    下载: 导出CSV

    表  5  降雨事件信息以及模型最优选择统计

    Table  5.   Statistics of rainfall events information and optimal model selection

    RO事件 RO范围GPM经过
    时刻(UT)
    沿射线路径平均
    降雨率/(mm·h–1
    正演最优组合
    日期时间(UT)纬度/(°)经度/(°)雨滴谱最优雨滴模型最大粒径/mm
    2018-06-01 10:44 –20-–18 122-125 11:15 0.8353 JD SC 5.6
    2018-06-10 08:02 –47-–44 168-171 06:47 0.4297 LP obA 5.6
    2018-07-16 03:22 6-10 52-56 01:56 1.5094 MP TB 5.6
    2018-08-24 22:14 –3-2 98-103 23:19 8.0735 LP TB 1.6
    2018-10-16 06:14 3-8 158-165 04:10 14.3313 SS TB 1.6
    2018-12-19 16:54 34-38 –147-–144 16:16 1.4317 SS TB 5.6
    2018-12-25 11:21 –8-–4 113-116 10:29 1.4509 SS TB 5.6
    2019-02-09 16:05 51-59 –172-–166 19:12 2.9561 LP TB 1.9
    2019-02-15 14:43 –20-–17 –148-–143 12:08 5.1476 LP TB 1.9
    2019-03-19 19:48 –30-–29 139-142 21:11 1.2528 JD GR 5.6
    2019-03-21 12:48 –13-–8 –121-–116 13:13 2.3603 SS TB 2
    2019-03-23 16:08 4-9 42-46 13:49 0.5395 MP SC 5.6
    2019-04-18 01:34 –7-–4 74-77 03:51 0.3570 SS PB 5.6
    2019-04-18 03:06 3-6 54-57 05:26 0.5205 SS PB 5.6
    2019-05-07 07:49 –30-–22 135-141 06:58 1.8151 LP TB 2
    2019-07-13 09:04 –43-–39 122-128 12:53 0.2626 SS obA 5.6
    2019-07-16 02:47 9-12 60-64 03:13 1.4592 MP TB 5.6
    2019-07-24 02:43 –47-–44 –112-–109 01:57 0.6810 JD SC 5.6
    2019-07-28 11:02 –52-–50 98-104 10:54 1.9016 SS SC 5.6
    2019-08-26 10:41 –31-–29 123-126 09:30 1.9969 JW TB 2
    2019-10-18 01:53 –7-–1 –136-–130 00:28 0.3141 MP SC 5.6
    2019-12-19 21:14 0-3 142-145 23:39 3.9332 MP GR 5.6
    2019-12-21 00:51 –8-–2 –87-–83 02:40 1.3798 MP TB 2.5
    2019-12-21 02:24 –11-–4 –108-–105 04:14 2.0559 MP TB 2
    2019-12-26 08:10 –7-–2 –20-–16 08:23 1.8006 JW TB 2
    2019-12-31 11:26 –2-4 –67-–63 10:18 1.3038 MP TB 5.6
    下载: 导出CSV
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  • 收稿日期:  2021-04-09
  • 录用日期:  2021-05-27
  • 修回日期:  2022-02-03
  • 网络出版日期:  2022-10-13

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