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首页> 外文期刊>Asia-Pacific Journal of Atmospheric Sciences >Calibration of radar reflectivity measurements from the KMA operational radar network
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Calibration of radar reflectivity measurements from the KMA operational radar network

机译：通过KMA运行雷达网络对雷达反射率测量进行校准

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摘要

A systematic procedure for calibrating system gain bias (so-called “calibration error”) of radar reflectivity measurements from the Korea Meteorological Administration (KMA) operational radar network is presented. First, the RJNI radar located at Jindo Island is calibrated by comparing with radar reflectivities simulated theoretically by a scattering algorithm using drop spectra collected by a disdrometer from June 19 to 29, 2009. Once the RJNI radar is calibrated, the reflectivity measurements from nearby radars are compared with the RJNI radar reflectivities to determine existing gain biases of nearby radars. This radar-radar calibration procedure was repeated with the other radars within the network. For isolating a system gain bias, echoes affected by partial beam blockage due to ground clutter and by attenuation due to precipitation were removed. The system gain biases of the RJNI and RPSN radars were −3 and −4.2 dB, respectively, during the experimental period. The RBRI and RDNH radars revealed relatively large biases, above −8 dB. The other radars (RKSN, RGSN, RSSP, RKWK, RGDK, RIIA, and RMYN) revealed biases from −6 to −7 dB. Thus, the reflectivity measurements from all of the KMA radars were severely biased. New R-Z relations of R = 3.350 × 10−2Z0.624 (Z = 231.1R 1.6) for stratiform and R=1.546 × 10−2 Z 0.714 (Z = 342.4R 1.4) for convective precipitations were derived using disdrometer data. Using these R-Z relations, the radar-derived total rainfall amounts from the reflectivity measurements without calibration produced significant underestimations, compared to gauge measurements at about 80 sites, with a normalized bias of about −56%. On the other hand, after calibrating the above system biases, the radar-derived rainfall amounts corresponded well with the gauge measurements, with a normalized bias of about −3%. In conclusions, the radar reflectivity measurements from the KMA radar network are severely biased and the procedure presented in this study can be used to resolve the system gain biases.
机译：提出了一种校准韩国气象局（KMA）运行雷达网络的雷达反射率测量的系统增益偏差（所谓的“校准误差”）的系统程序。首先，通过与使用散射计从2009年6月19日至29日收集的液滴光谱的散射算法理论模拟的雷达反射率进行比较，对位于金岛的RJNI雷达进行了校准。一旦校准了RJNI雷达，就可以测量附近雷达的反射率将其与RJNI雷达反射率进行比较，以确定附近雷达的现有增益偏差。网络中的其他雷达重复了此雷达-雷达校准程序。为了隔离系统增益偏差，消除了由于地物杂波引起部分光束阻塞和降水引起的衰减影响的回波。在实验期间，RJNI和RPSN雷达的系统增益偏差分别为-3和-4.2 dB。 RBRI和RDNH雷达显示出相对较大的偏置，高于-8 dB。其他雷达（RKSN，RGSN，RSSP，RKWK，RGDK，RIIA和RMYN）显示出从-6到-7 dB的偏置。因此，所有KMA雷达的反射率测量值都存在严重偏差。层状和R = 1.546×10 <的新RZ关系为R = 3.350×10 −2 Z 0.624 （Z = 231.1R 1.6 ）对流降水的sup> −2 Z 0.714 （Z = 342.4R 1.4 ）是由测风仪数据导出的。使用这些R-Z关系，与在约80个地点进行的标尺测量相比，未经校准的反射率测量得出的雷达得出的总降雨量产生了明显的低估，归一化偏差约为-56％。另一方面，在校准上述系统偏差之后，雷达得出的降雨量与仪表测量值很好地对应，归一化偏差约为-3％。总之，来自KMA雷达网络的雷达反射率测量结果存在严重偏差，本研究中介绍的过程可用于解决系统增益偏差。

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