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Comparison of Reflectivity Factor of Dual Polarization Radar and Dual-frequency Precipitation Radar
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摘要: 为了研究星载测雨雷达和地基雷达探测数据存在差异的本质原因，将GPM（Global Precipitation Measurement Mission）星载双频测雨雷达（dual-frequency precipitation radar，DPR）和南京信息工程大学C波段双偏振雷达（CDP）的反射率因子进行时空匹配，并基于水凝物分类定量分析两者探测的相似性和差异性。结果表明：GPM DPR与CDP探测的反射率因子整体一致性较好，经过衰减订正和波段修正，两者的相关系数约为0.86，达到0.001显著性水平，均方根误差约为3.33 dB。基于T-矩阵法拟合C和Ku波段探测不同水凝物等效反射率因子的波段修正公式，在衰减订正基础上针对不同水凝物类型的回波进行波段修正，二者探测湿雪、霰、大滴和中雨回波的相关性较好；受干雪几何形状影响，探测干雪回波的相关性较低；探测大雨和冰晶回波的相关性较差。DPR中NS和HS模式探测存在差异，DPR NS模式对强回波敏感，而DPR HS模式对弱回波敏感。
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关键词:
- GPM星载双频测雨雷达;
- 时空匹配;
- 水凝物分类;
- 波段修正;
- 反射率因子差异
Abstract: To find the root cause of the difference between spaceborne radar and ground-based radar data, their similarities and differences are quantitatively analyzed using GPM (Global Precipitation Measurement Mission) DPR (dual-frequency precipitation radar) and C-band dual-polarization radar (CDP) at Nanjing University of Information Science & Technology with respect to reflectivity factor classification of hydrometeor types by spatial-temporal matchup. The comparison reveals a high correlation of 0.86 between reflectivity factor detected by GPM DPR and CDP from 2015 to 2017 and a small root mean square error(RMSE) of 3.33 dB after attenuation correction and band correction, and the correlation passes the test of 0.001 level. The band correction formulas for detecting different hydrometeors reflectivity factor in C- and Ku-band are fitted by T-matrix method, applied the formula of dry snow to dry snow and graupel, applied the formula of wet snow is applicable to wet snow and rain hail, applied the formula of water to moderate rain, applied big drop and heavy rain and the band correction formula of ice to ice crystal. Band correction is carried out for different hydrometeors echoes after attenuation correction, the echo consistency of wet snow, graupel, big drops and moderate rain is well, and the correlation coefficient is over 0.85, the RMSE is less than 4 dB and echo differences of wet snow, graupel, big drops and moderate rain are small. The echo correlation coefficient of dry snow is relatively less than 0.8 due to the complex shape of dry snow which leads to difference between horizontal and vertical directions of CDP and difference between Mie scattering simulation and actual situation of dry snow, and further study on simulation of dry snow reflectivity factor is deserved. Due to the detection resolution of DPR and insufficient effective irradiation volume of CDP, the echo correlation coefficient of heavy rain and ice crystal is less than 0.4, and the reflectivity factor of heavy rain and ice crystal detected by DPR is less than CDP. The difference of reflectivity factor between DPR and CDP is mainly caused by dry snow, heavy rain and ice crystal. The amount of band correction is less than the amount of attenuation correction, then attenuation is the main factor. Band correction improves the matching situation on the basis of attenuation correction. NS mode and HS mode in DPR are different. NS mode can detect high reflectivity factor and is sensitive to strong echo, but is weak in detecting small reflectivity factor, while HS mode can detect small reflectivity factor and is sensitive to weak echo, but is weak in detecting high reflectivity factor. -
图 1 DPR和GR反射率因子对比散点及差异散点
Fig. 1 Scatter plot and difference scatter plot of reflectivity factor of DPR and GR
图 2 2016年10月26日00:25南京信息工程大学C波段双偏振雷达不同仰角反射率因子、差分反射率、协相关系数、水凝物识别分布(相邻距离圈间隔为30 km)
Fig. 2 Z, ZDR, ρ, hydrometeor distribution of CDP at NUIST with different degrees at 0025 UTC 26 Oct 2016(the distance between adjacent range rings is 30 km)
图 4 波段修正后不同水凝物对比散点以及概率密度分布
Fig. 4 Scatter plot and probability density function of different hydrometeors after band correction
图 5 波段修正后不同水凝物统计参数
Fig. 5 Curves of statistical parameters of different hydrometeors after band correction
表 1 水凝物分布
Table 1 Distribution of hydrometeors
高度 水凝物类型 低于零度层亮带底部 中雨、大滴、大雨、雨雹混合物 零度层亮带底部—零度层亮带 湿雪、霰、中雨、大滴、大雨、雨雹混合物 零度层亮带—零度层亮带顶部 干雪、湿雪、霰、冰晶、大滴、雨雹混合物 高于零度层亮带顶部 干雪、霰、冰晶、雨雹混合物 
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表 2 拟合系数
Table 2 Fitted curve coefficients
类别 a0 a1 a2 a3 a4 a5 冰 0.0120 0.0111 -0.0020 6.8441×10-5 -7.8862×10-7 3.0245×10-9 水 -0.0377 0.0530 -0.0020 -7.5728×10-7 1.5135×10-7 湿雪 0.0165 -0.0189 -4.2491×10-5 2.4098×10-7 干雪 0.0088 -0.1131 0.0077 -1.9627×10-4 2.2309×10-6 -9.3026×10-9
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