微雨雷达雨滴谱和降雨参数反演精度分析OA
Precision Evaluation of Raindrop Size Distribution and Rainfall Parameters Retrieved by a Micro Rain Radar
本文基于北京地区 2016~2018年暖季(5~9月)降水观测数据,利用二维视频雨滴谱仪(2DVD)和X波段双偏振雷达(XPOL),系统评估了微雨雷达(MRR)在层状云和对流云降水中的雨滴谱(Raindrop size distribution,DSD)及降雨参数(降雨强度 R、液态含水量 W、反射率因子 Z、质量加权平均直径 Dm和标准化伽马分布截距参数 Nw)的反演精度.针对 MRR原始功率谱数据,提出了一种新处理方法(REP),通过退折叠和垂直气流订正优化反演流程,并与标准输出数据(AVG)对比.结果表明:(1)两种数据在层状云降水中的反演精度优于对流云降水,其中 REP数据显著提升了 DSD和降雨参数的反演精度,特别是对流云降水中,Z偏差从 AVG的-23.74 dB改善至 0.74 dB.(2)与 2DVD 相比,AVG 数据系统性低估 DSD参数和降雨参数,且偏差随降雨强度增大而加剧.而REP数据的相关系数(Z、R、W、Dm和Nw的相关系数分别达0.96、0.94、0.93、0.87和 0.78)和误差指标均显著优于 AVG数据.(3)与 AVG DSD 模拟结果相比,REP DSD 模拟的反射率(Z)和差分反射率(ZDR)与 XPOL观测值的吻合度更高,REP(AVG)模拟的 Z和 ZDR偏差分别为-1.14(-3.04)和 0.02(0.12)dB.(4)MRR垂直分辨率对反演结果影响显著:层状云降水中,30 m、100 m和 200 m分辨率下反演的 DSD和 2DVD一致性较高;而对流云降水中,30 m分辨率表现最优,200 m略优于 100 m.总之,退折叠和垂直气流订正可有效提升 MRR对层状云和对流云降水 DSD和降雨参数的反演能力,为降水精细化垂直微物理结构研究提供了更可靠的技术手段.
The performance of MRR(Micro Rain Radar)in estimating DSD(raindrop size distribution)and integral rainfall parameters(rainfall intensity R,liquid water content W,radar reflectivity factor Z,mean mass-weighted raindrop diameter Dm,and intercept parameter Nw of the normalized gamma distribution)was comprehensively evaluated using coincident data obtained from a 2DVD(two-dimensional video disdrometer)and an XPOL(X-band dual-polarization radar)in Beijing in the warm season(May-September)during 2016-2018.A comparative analysis between standard MRR products(hereinafter referred to as AVG data)and reprocessed data(hereinafter referred to as REP data)derived from raw MRR spectra was conducted to assess the accuracy of the MRR in estimating stratiform and convective rain types.The key findings were as follows:(1)The retrieval accuracy of the DSD and rainfall parameters for the REP data outperformed those from the AVG data,with device performance being superior for stratiform rain than for convective rain.The Z bias improved from-23.74 dB(for AVG data)to 0.74 dB(for REP data)in the case of convective rain.(2)The AVG data systematically underestimated the DSD and rainfall parameters compared to 2DVD measurements,an underestimation that intensified as rainfall intensity increased.In contrast,the REP data exhibited not only higher correlation coefficients(Z:0.96;R:0.94;W:0.93;Dm:0.87;lgNw:0.78),but also lower absolute biases and reduced root mean square errors.(3)Simulations of Z and differential reflectivity(ZDR)based on the REP DSD showed better agreement with XPOL observations,with biases of-1.14 dB(-3.04 dB for the AVG data)and 0.02 dB(0.12 dB for the AVG data),respectively.(4)The vertical resolution of the MRR had a significant impact on the retrieval performance.For stratiform rain,the MRR-retrieved DSD and rainfall parameters at resolutions of 30 m,100 m,and 200 m showed high consistency with 2DVD measurements.For convective rain,however,the optimal agreement was observed at the 30-m resolution.Meanwhile,the performance at a resolution of 200 m was marginally better than that at 100 m.In summary,these results demonstrate that applying de-aliasing and vertical wind correction to MRR raw spectra significantly enhances MRR's capability to retrieve the DSD and rainfall parameters for both stratiform and convective rain types,providing a more reliable technical means for investigating the fine-scale vertical microphysical structure of precipitation.
罗丽;赵建民;郭佳;陈羿辰;冯亮
北京城市气象研究院,北京 100089内蒙古自治区包头市气象局,包头 014030华云敏视达雷达(北京)有限公司,北京 100094北京市人工影响天气中心,北京 100089中国科学院大气物理研究所云降水物理与强风暴重点实验室,北京 100029
天文与地球科学
微雨雷达(MRR)雨滴谱(DSD)降雨参数反演精度综合评估
Micro Rain Radar(MRR)Raindrop size distribution(DSD)Rainfall parametersRetrieved accuracyComprehensive evaluation
《大气科学》 2026 (2)
399-416,18
国家重点研发计划项目2022YFC3003901,中央级公益性科研院所基本科研业务费专项基金项目IUMKY202432,中国气象局智能气象观测技术重点开放实验室开放研究课题青年项目ZNGC2024QN10 National Key Research and Development Program of China(Grant 2022YFC3003901),Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institutes(Grant IUMKY202432)Science and Open Project of the Key Laboratory of Intelligent Meteorological Observation Technology,China Meteorological Administration(Grant ZNGC2024QN10)
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