双月刊

ISSN 1006-9895

CN 11-1768/O4

基于微雨雷达的降水参数反演和粒子相态识别
作者:
作者单位:

1.山东省气象防灾减灾重点实验室;2.中国科学院大气物理研究所云降水物理与强风暴实验室;3.山东省人民政府人工影响天气办公室,;4.山东省人民政府人工影响天气办公室;5.山东省气象台;6.山东省气象信息中心

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基金项目:

ZR2020MD054,中国气象局云雾物理环境重点开放实验室开放课题2019Z01607,山东省气象局重点项目2020sdqxz08Funded by Shandong Provincial Natural Science Foundation(Grant ZR2020MD054), OpenProjectofKeyLaboratoryforCloud Physics of China MeteorologicalAdministration(Grant 2019Z01607), ProgramofShandongProvinceMeteorologicalBureau(Grant2020sdqxz08)


Inversion of Precipitation parameters and Precipitation type classification based on Micro Rain Radar
Author:
Affiliation:

Key Laboratory of Cloud–Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences

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    摘要:

    本文基于微雨雷达原始的后向散射信号,在功率谱计算、噪声去除、退模糊等处理的基础上计算了雷达基本参量, 并反演了液态降水参数,例如雷达反射率因子、雨强等, 同时采用RaProM算法对粒子相态进行识别。算法综合考虑粒子下落速度、等效雷达反射率因子、不同相态粒子的尺度特征以及是零度层亮带位置等信息,可识别的粒子相态包括雪、毛毛雨、雨、冰雹以及混合相态。选取了三个山东地区较为典型的个例对反演算法进行验证,即2021年7月2日典型层状云降水个例、2019年12月25日雨雪转换个例以及2018年3月4日零度层高度逐渐降低的降水个例。结果显示:粒子识别方法应用于典型层状云降水,垂直方向上不同相态粒子的分层较为明显,过冷层里的固态降水雪花、零度层附近冰水转换区的混合相态降水以及零度层以下的液态降水符合现有认识,验证了反演算法以及粒子识别算法的有效性。将结果进一步在雨雪转换降水相态识别中和零度层高度的检测,该反演算法均能得到较好应用,与同址同步观测的微波辐射计、云雷达、二维视频雨滴谱仪等观测结论一致。另外,与微雨雷达标准反演算法对比,RaProM算法的优势是没有粒子相态的原始假设,且考虑降水粒子向上的速度,反演结果与的微波辐射计、云雷达在垂直结构上有较高的一致性。与地面激光雨滴谱仪观测数据对比显示,该算法也有效提升了微雨雷达对雨滴谱和雨强的反演能力。

    Abstract:

    Based on the original backscattering signal of Micro Rain Radar and the RaProM algorithm, the equivalent radar reflectivity, the particle falling velocity and Doppler spectrum width are calculated after power spectrum calculation, noise removal and deblurring. Furthermore, the precipitation type are identified. Considering particles falling velocity, the equivalent radar reflectivity, particle size characteristics of different precipitation type and whether there is a bright band, RaProM algorithm can identify particle phase including snow, drizzle, rain, hail and mixed type. In addition, the liquid precipitation parameters, such as radar reflectivity factor and rain intensity, are calculated. Subsequently, three typical cases of stratiform cloud precipitation on July 2, 2021, rain-snow conversion on December 25, 2019, and gradually decreasing bright band height on March 4, 2018 are selected to verify and discuss the results. The method of precipitation type classification is applied to typical stratiform precipitation, the vertical structure shows snowflakes in the supercooled water area, mixed type precipitation in the ice-liquid conversion zone near the 0 ℃ layer and liquid precipitation below the bright band, which verified the validity of the method. The methods are further applied in precipitation type classification and bright band detection, and the results show that compared with standard inversion process of Micro Rain Radar, RaProM algorithm has the advantage of no assumption of precipitation type and considering the upward velocity of particles (such as snowflakes). The results of RaProM algorithm are in good agreement with the co-located microwave radiometer and cloud radar in the vertical structure, and the deviations from the ground disdrometer in the raindrop size distribution and rain intensity are reduced compared with the products of Micro Rain Radar.

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历史
  • 收稿日期:2021-11-10
  • 最后修改日期:2022-01-05
  • 录用日期:2022-04-13
  • 在线发布日期: 2022-04-14
  • 出版日期: