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
-
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。
-
关键词:
- Ka/Ku波段双波段云雷达 /
- 云微物理动力参数反演 /
- 回波强度误差和衰减的订正方法
Abstract: Reflectivity calibration errors, attenuation in regions prone to rainfall, and water cover over cloud radar antennas have severely impacted the microphysical and dynamic parameters retrieved using reflectivity spectral density data; examining the errors involved with retrieving microphysical and dynamic parameters is a crucial problem that needs resolution. This paper presents a retrieval algorithm for vertical air motion (Vair) and raindrop size distribution (DSD) based on reflectivity spectral density data and dual-wavelength ratio (DWR) constraints with a Ka/Ku dual-wavelength cloud radar (DWCR), which aims to reduce the effects of observation bias introduced by calibration and attenuation of water cover over cloud radar antenna on reflectivity. The disdrometer data were employed to analyze the retrieved parameters. Furthermore, the effects of vertical air speed on retrieved microphysical parameters are discussed. In the algorithm (DWR-SZ), Vair retrieved from a single Ka/Ku band CR (ST) and DWCR algorithms (DWSZ) are merged to form Vair in all observation areas, following which the initial DSD and attenuation are retrieved using the DWSZ algorithm. Finally, DWR between the first and last ranges in the liquid area in a beam is utilized to adjust the reflectivity bias and the retrieved final DSD to minimize the difference between the observed cloud radar and the calculated DWR. Two convective precipitation cases on June 8, 2020, and June 1, 2021, in Longmen, Guangdong Province, were used to examine the retrieved results. The results demonstrate that the radar sensitivity variations have little effect on Vair obtained from the DWSZ; however, the DWSZ cloud is only employed in the areas containing large raindrops (diameter > 1.8 mm). ST algorithms with Ka and Ku data underestimated Vair; however, the Vairr is reasonable at low altitudess with reflectivity weaker than 35 dBZ. A highly sensitive work mode with pulse compressions could enhance the Vair retrieval bias. Merged Vair from the ST and DWSZ algorithms is reasonable. The attenuation and far radar range could introduce the underestimation of Vair with ST algorithms. Moreover, the underestimations of Vair in the solid precipitation area are negotiable due to the sharp variation and narrow reflectivity spectral density data. Furthermore, employing the constraint conditions of DWR reduced the bias of observed reflectivity and the effects of water cover over the antenna, thereby improving the retrieval results. Vair from the ST used in DWR-SZ overestimated the drop number, liquid water content (LWC), and attenuation coefficient and underestimated the drop size; however, it has no effect on the reflectivity bias produced by water cover over the antenna. -
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（ZKus和ZKas）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 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
-
[1] Adhikari N B, Iguchi T, Seto S, et al. 2007. Rain retrieval performance of a dual–frequency precipitation radar technique with differential-attenuation constraint [J]. IEEE Trans. Geosci. Remote Sens., 45(8): 2612−2618. doi: 10.1109/TGRS.2007.893555 [2] Barber P, Yeh C. 1975. Scattering of electromagnetic waves by arbitrarily shaped dielectric bodies [J]. Appl. Opt., 14(12): 2864−2872. doi: 10.1364/AO.14.002864 [3] Ding Han, Liu Liping. 2020. Establishment and preliminary application of the forward modeling method for Doppler spectral density of ice particles [J]. Remote Sensing, 12(20): 3378. doi: 10.3390/rs12203378 [4] Firda J M, Sekelsky S M, McIntosh R E. 1999. Application of dual–frequency millimeter–wave Doppler spectra for the retrieval of drop size distributions and vertical air motion in rain [J]. J. Atmos. Oceanic Technol., 161(2): 216−236. doi: 10.1175/1520-0426(1999)016<0216:AODFMW>2.0.CO;2 [5] Frisch A S, Fairall C W, Snider J B. 1995. Measurement of stratus cloud and drizzle parameters in ASTEX with a Kα-band Doppler radar and a microwave radiometer [J]. J. Atmos. Sci., 52(16): 2788−2799. doi: 10.1175/1520-0469(1995)052<2788:MOSCAD>2.0.CO;2 [6] Gossard E E. 1994. Measurement of cloud droplet size spectra by Doppler radar [J]. J. Atmos. Oceanic Technol., 11(3): 712−726. doi: 10.1175/1520-0426(1994)011<0712:MOCDSS>2.0.CO;2 [7] Hauser D, Amayenc P. 1981. A new method for deducing hydrometeor–size distributions and vertical air motions from Doppler radar measurements at vertical incidence [J]. Journal of Applied Meteorology and Climatology, 20(5): 547−555. doi: 10.1175/1520-0450(1981)020<0547:ANMFDH>2.0.CO;2 [8] Hogan R J, Gaussiat N, Illingworth A J. 2005. Stratocumulus liquid water content from dual–wavelength radar [J]. J. Atmos. Oceanic Technol., 22(8): 1207−1218. doi: 10.1175/JTECH1768.1 [9] Jaffrain J, Berne A. 2011. Experimental quantification of the sampling uncertainty associated with measurements from PARSIVEL disdrometers [J]. Journal of Hydrometeorology, 12(3): 352−370. doi: 10.1175/2010JHM1244.1 [10] Kollias P, Albrecht B A, Marks Jr F D. 2003. Cloud radar observations of vertical drafts and microphysics in convective rain [J]. J. Geophys. Res., 108(D2): 4053. doi: 10.1029/2001JD002033 [11] Kollias P, Miller M A, Luke E P, et al. 2007. The atmospheric radiation measurement program cloud profiling radars: Second-generation sampling strategies, processing, and cloud data products [J]. J. Atmos. Oceanic Technol., 24(7): 1199−1214. doi: 10.1175/JTECH2033.1 [12] Kollias P, Rémillard J, Luke E, et al. 2011. Cloud radar Doppler spectra in drizzling stratiform clouds: 1. Forward modeling and remote sensing applications [J]. J. Geophys. Res., 116(D13): D13201. doi: 10.1029/2010JD015237 [13] Kozu T, Nakamura K. 1991. Rainfall parameter estimation from dual-radar measurements combining reflectivity profile and path-integrated attenuation [J]. J. Atmos. Ocean Technol., 8: 259−271. doi: 10.1175/1520-0426(1991)008<0259:RPEFDR>2.0.CO;2 [14] Liao L, Meneghini R. 2005. A study of air/space-borne dual–wavelength radar for estimation of rain profiles [J]. Adv. Atmos. Sci., 22(6): 841−851. doi: 10.1007/BF02918684 [15] Liu Liping, Gao Wenhua. 2020. Statistical analysis of microphysical and dynamical parameters for clouds and precipitation over Nauq Tibetan Plateau in summertime using Ka-band cloud radar [J]. Atmosphere, 11(8): 818. doi: 10.3390/atmos11080818 [16] 刘黎平, 谢蕾, 崔哲虎. 2014. 毫米波云雷达功率谱密度数据的检验和在弱降水滴谱反演中的应用研究 [J]. 大气科学, 38(2): 223−236. doi: 10.3878/j.issn.1006-9895.2013.12207Liu Liping, Xie Lei, Cui Zhehu. 2014. Examination and application of Doppler spectral density data in drop size distribution retrieval in weak precipitation by cloud radar [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 38(2): 223−236. doi: 10.3878/j.issn.1006-9895.2013.12207 [17] Liu Liping, Ding Han, Dong Xiaobo, et al. 2019. Applications of QC and merged Doppler spectral density data from Ka-band cloud radar to microphysics retrieval and comparison with airplane in situ observation [J]. Remote Sensing, 11(13): 1595. doi: 10.3390/rs11131595 [18] 刘黎平, 张扬, 丁晗. 2021. Ka/Ku双波段云雷达反演空气垂直运动速度和雨滴谱方法研究及初步应用 [J]. 大气科学, 45(5): 1099−1113. doi: 10.3878/j.issn.1006-9895.2104.20200Liu Liping, Zhang Yang, Ding Han. 2021. Vertical air motion and raindrop size distribution retrieval using a Ka/Ku dual–wavelength cloud radar and its preliminary application [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(5): 1099−1113. doi: 10.3878/j.issn.1006-9895.2104.20200 [19] 刘胜男, 王改利. 2020. DSD参数对双频雷达估测降水的影响研究 [J]. 高原气象, 39(3): 570−580. doi: 10.7522/j.issn.1000-0534.2019.00092Liu Shengnan, Wang Gaili. 2020. Study on the impacts of DSD parameters on precipitation estimation using dual–frequency radar [J]. Plateau Meteorology (in Chinese), 39(3): 570−580. doi: 10.7522/j.issn.1000-0534.2019.00092 [20] Shupe M D, Kollias P, Poellot M, et al. 2008. On deriving vertical air motions from cloud radar Doppler spectra [J]. J. Atmos. Oceanic Technol., 25(4): 547−557. doi: 10.1175/2007JTECHA1007.1 [21] Tang Qi, Xiao Hui, Guo Chunwei, et al. 2014. Characteristics of the raindrop size distributions and their retrieved polarimetric radar parameters in northern and southern China [J]. Atmospheric Research, 135–136: 59–75. doi:10.1016/j.atmosres.2013.08.003 [22] Tridon F, Battaglia A. 2015. Dual–frequency radar Doppler spectral retrieval of rain drop size distributions and entangled dynamics variables [J]. J. Geophys. Res., 120(11): 5585−5601. doi: 10.1002/2014JD023023 [23] Vivekanandan J, Zhang G, Politovich M K. 2001. An assessment of droplet size and liquid water content derived from dual–wavelength radar measurements to the application of aircraft icing detection [J]. J. Atmos. Oceanic Technol., 18(11): 1787−1798. doi: 10.1175/1520-0426(2001)018<1787:AAODSA>2.0.CO;2 [24] Wang Gaili, Liu Shengnan, Liu Liping. 2020. Uncertainties in retrieving microphysical properties of rain profiles using ground-based dual–frequency radar [J]. Meteorological Applications, 27(1): e1876. doi: 10.1002/met.1876 [25] 曾震瑜, 刘黎平, 郑佳锋, 等. 2021. 星载雷达DPR与地基雷达CR的匹配对比及系统偏差初探 [J]. 暴雨灾害, 40(3): 287−296. doi: 10.3969/j.issn.1004-9045.2021.03.007Zheng Zhenyu, Liu Liping, Zheng Jiafeng, et al. 2021. Matching comparison between Satellite radar DPR and ground-based radar CR and preliminary study of systematic error [J]. Torrential Rain and Disasters, 40(3): 287−296. doi: 10.3969/j.issn.1004-9045.2021.03.007 [26] 郑晨雨, 刘黎平. 2020. Ka/Ku双波段云雷达探测云降水滴谱和空气垂直运动速度的能力模拟分析 [J]. 高原气象, 39(2): 543−559. doi: 10.7522/j.issn.1000-0534.2019.00126Zheng Chenyu, Liu Liping. 2020. Simulation analysis on retrieving capabilities for rain drop size distribution and air vertical motion with single and dual wave length cloud radars [J]. Plateau Meteorology (in Chinese), 39(2): 543−559. doi: 10.7522/j.issn.1000-0534.2019.00126 [27] Zheng Jiafeng, Liu Liping, Zhu Keyun, et al. 2017. A method for retrieving vertical air velocities in convective clouds over the Tibetan Plateau from TIPEX-III cloud radar Doppler spectra [J]. Remote Sensing, 2017,9(9): 964. doi: 10.3390/rs9090964 -
下载:
点击查看大图
计量
- 文章访问数: 164
- HTML全文浏览量: 7
- PDF下载量: 25
- 被引次数: 0
