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Comparison Analysis on Detection Performance of Ground-based Microwave Radiometers Under Different Weather Conditions
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摘要: 利用探空数据和毫米波云雷达数据，对在大气探测试验基地同址观测的国内外3种型号地基微波辐射计进行1年（2016年10月—2017年9月）的比对分析，重点分析不同型号地基微波辐射计在晴空和云天下温、湿观测性能特征。结果表明：3种型号地基微波辐射计温度与探空相关系数均超过0.98，达到0.01显著性水平；晴空条件下，德国及国产地基微波辐射计温度平均误差均在±1℃以内（前者为负偏差，后者为正偏差），误差较小，美国地基微波辐射计系统偏差约为-1.8℃；3种型号地基微波辐射计均方根误差随高度递增，整体均方根误差以德国地基微波辐射计2.2℃为最小，美国地基微波辐射计3.8℃为最大；在有云条件下，3种型号地基微波辐射计平均误差分布较晴空条件下无明显变化，均方根误差较晴空条件有约增加0.5℃。3种型号地基微波辐射计均呈晴空相对湿度误差小于云天误差，低空误差小于中高空误差的特点；晴空条件下，美国与国产地基微波辐射计相对湿度均方根误差分别为15%和18%左右，小于德国地基微波辐射计；云天条件下3种型号微波辐射计均方根误差均较大（26%左右）。Abstract: Ground-based microwave radiometer (MWR) detects atmospheric temperature and humidity by receiving atmospheric microwave radiation, which can conduct 24-hour unattended, high-resolution observation. It can detect short-time variation of atmospheric elements. MWR is an important supplement to routine sounding. However, it has different observation accuracy at different times, seasons and weather conditions. Observation accuracy and influencing factors analyses are essential in scientific experiments and operation processes.In order to minimize error effects of radiosonde migration, results from three types of remote sensing devices under two weather conditions are compared. By analyzing differences of temperature and relative humidity between sounding and three MWRs at home and abroad of different technology systems in no-cloud and cloud samples, performances of these MWRs are evaluated.In the aspect of temperature, the correlation coefficient for MWRs and sounding is above 0.98. In no-cloud condition, errors of MWR-G and MWR-A are less than ±1℃ (the former is negative and the latter is positive). MWR-A has -1.8℃ deviation. Root mean square errors (RMSEs) of three types of MWRs increase with height. RMSEs of MWR-G, MWR-A and MWR-C are 2.2℃, 3.0℃ and 3.8℃. In cloud condition, vertical distributions of temperature error of three microwave radiometers have no significant changes in comparison with no-cloud condition. The RMSEs of three microwave radiometers in cloud condition are 0.5℃ higher than those in no-cloud condition. Microwave radiometer can identify the near surface radiation inversion layer accurately. But it's hard to identify the high-altitude inversion layer. In the aspect of relative humidity, the error of cloud samples is higher than the error of no-cloud samples, and the error of middle-high cloud samples is higher than that of low cloud samples. In no-cloud condition, RMSEs of MWR-A and MWR-C are 15% and 18%, less than the RMSE of MWR-G. In cloud condition, RMSEs of MWR-G, MWR-A and MWR-C (about 26%) are larger than those in no-cloud condition. The existence of cloud has a major influence on the detection of microwave radiometer relative humidity:No matter which height level, errors of low-middle cloud samples are bigger than those of no-cloud samples. Errors of high cloud samples are bigger than those of no-cloud samples, and the amplification of RMSE is about 10%-20%. This comparison analysis will provide some reference basis for the further improvement of the accuracy of MWR atmospheric profiles and the scientific research, promotion and operation processes of MWRs.
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图 1 保留的云天个例(2017年7月14日13:15)
(a)云雷达基本反射率因子，(b)激光云高仪云底高
Fig. 1 Typical case at 1315 BT 15 July 2017
(a)base reflectivity of cloud radar, (b)cloud base height of laser ceilometer
图 2 辐射计温度廓线与探空时序
(a)降雨标志，(b)探空，(c)MWR-G，(d)MWR-A，(e)MWR-C
Fig. 2 The sequence of microwave radiometers and sounding temperature profiles
(a)rain flag, (b)sounding, (c)MWR-G, (d)MWR-A, (e)MWR-C
图 3 辐射计与探空温度差异时序
(a)降雨标志，(b)MWR-G，(c)MWR-A，(d)MWR-C
Fig. 3 Temperature difference sequence among microwave radiometers and sounding
(a)rain flag, (b)MWR-G, (c)MWR-A, (d)MWR-C
图 4 逆温出现时辐射计温度廓线
(a)2016年12月2日07:00，(b)2016年12月31日07:00，(c)2017年1月2日19:00，(d)2017年1月3日07:00，(e)2017年1月13日07:00，(f)2017年1月15日19:00，(g)2017年2月6日07:00，(h)2017年1月14日19:00
Fig. 4 Temperature profiles of microwave radiometers in temperature inversion condition
(a)0700 BT 2 Dec 2016, (b)0700 BT 31 Dec 2016, (c)1900 BT 2 Jan 2017, (d)0700 BT 3 Jan 2017, (e)0700 BT 13 Jan 2017, (f)1900 BT 15 Jan 2017, (g)0700 BT 6 Feb 2017, (h)1900 BT 14 Jan 2017
图 5 辐射计与探空相对湿度廓线时序
(a)降雨标志，(b)探空，(c)MWR-G，(d)MWR-A，(e)MWR-C
Fig. 5 The sequence of microwave radiometers and sounding relative humidity profiles
(a)rain flag, (b)sounding, (c)MWR-G, (d)MWR-A, (e)MWR-C
图 6 辐射计晴空温度样本与探空拟合
(a)MWR-G，(b)MWR-A，(c)MWR-C
Fig. 6 Temperature fitting between microwave radiometers and sounding in no-cloud condition
(a)MWR-G, (b)MWR-A, (c)MWR-C
图 7 辐射计晴空温度样本误差分布
(a)平均误差，(b)均方根误差
Fig. 7 Temperature errors of microwave radiometers in no-cloud condition
(a)mean error, (b)root mean square error
图 8 辐射计晴空相对湿度样本误差分布
(a)平均误差，(b)均方根误差
Fig. 8 Relative humidity errors of microwave radiometers in no-cloud condition
(a)mean error, (b)root mean square error
图 9 辐射计云天温度样本误差分布
(a)平均误差，(b)均方根误差
Fig. 9 Temperature errors of microwave radiometers in cloud condition
(a)mean error, (b)root mean square error
图 10 辐射计云天相对湿度样本误差分布
(a)平均误差，(b)均方根误差
Fig. 10 Relative humidity errors of microwave radiometers in cloud condition
(a)mean error, (b)root mean square error
图 11 MWR-C辐射计云天和晴天相对湿度均方根误差分布
(a)低云样本，(b)中云样本，(c)高云样本，(d)中低云样本
Fig. 11 Relative humidity root mean square error of MWR-C in no-cloud and cloud conditions
(a)low cloud, (b)middle cloud, (c)high cloud, (d)low-middle cloud
表 1 3台辐射计实测亮温平均误差与均方根误差(单位：K)
Table 1 The mean error and root mean square error of three microwave radiometers(unit: K)
通道频率/GHz MWR-G MWR-A MWR-C 平均误差 均方根误差 平均误差 均方根误差 平均误差 均方根误差 22.24 0.8574 4.0925 -0.4877 4.0592 2.1631 4.9768 23.04 0.6795 3.7895 -0.2190 3.7839 -0.2361 3.7079 23.84 0.4690 3.2603 -0.9239 3.3203 1.0372 3.0210 25.44 0.2308 2.3124 0.3042 2.3903 2.4818 3.2500 26.24 0.0522 2.1165 -1.4183 2.9852 0.6053 1.8368 27.84 0.0177 1.9188 -1.6491 2.9130 0.7589 1.5521 31.40 0.1240 1.6238 -0.8981 2.1127 0.1778 1.4191 51.26 4.1329 4.5071 4.5629 4.8050 3.3149 4.8146 52.28 2.5576 3.2039 3.6608 3.8599 4.3149 5.3508 53.86 2.7609 3.1892 0.7982 1.7638 3.8261 4.3381 54.94 -0.6435 2.1828 -0.9212 2.3392 0.2680 3.1427 56.66 -1.1438 2.9987 -1.3116 3.1192 0.3860 3.3706 57.30 -1.1575 3.1308 -1.4247 3.2401 0.4923 3.3628 58.00 -1.0578 3.1764 -1.3797 3.3010 0.4982 3.4993 
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