Vol.29, NO.3, 2018
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Article Navigation >  Journal of Applied Meteorological Science  > 2018  >  29(3): 282-295
Wang Zhicheng, Zhang Xuefen, Mao Jiajia, et al. Comparison analysis on detection performance of ground-based microwave radiometers under different weather conditions. J Appl Meteor Sci, 2018, 29(3): 282-295. DOI:  10.11898/1001-7313.20180303.
Citation: Wang Zhicheng, Zhang Xuefen, Mao Jiajia, et al. Comparison analysis on detection performance of ground-based microwave radiometers under different weather conditions. J Appl Meteor Sci, 2018, 29(3): 282-295. DOI:  10.11898/1001-7313.20180303.
shu

Comparison Analysis on Detection Performance of Ground-based Microwave Radiometers Under Different Weather Conditions

DOI: 10.11898/1001-7313.20180303
  • 1.

    Chengdu University of Information Technology, Chengdu 610225

  • 2.

    CMA Key Laboratory of Atmospheric Sounding, Chengdu 610225

  • 3.

    Meteorological Observation Center of CMA, Beijing 100081

  • Received Date: 2017-10-30
  • Rev Recd Date: 2018-02-28
  • Publish Date: 2018-05-31
    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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    References(19)

  • Fig. 1  Typical case at 1315 BT 15 July 2017

    (a)base reflectivity of cloud radar, (b)cloud base height of laser ceilometer

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    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

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    Fig. 3  Temperature difference sequence among microwave radiometers and sounding

    (a)rain flag, (b)MWR-G, (c)MWR-A, (d)MWR-C

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    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

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    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

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    Fig. 6  Temperature fitting between microwave radiometers and sounding in no-cloud condition

    (a)MWR-G, (b)MWR-A, (c)MWR-C

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    Fig. 7  Temperature errors of microwave radiometers in no-cloud condition

    (a)mean error, (b)root mean square error

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    Fig. 8  Relative humidity errors of microwave radiometers in no-cloud condition

    (a)mean error, (b)root mean square error

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    Fig. 9  Temperature errors of microwave radiometers in cloud condition

    (a)mean error, (b)root mean square error

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    Fig. 10  Relative humidity errors of microwave radiometers in cloud condition

    (a)mean error, (b)root mean square error

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    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

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    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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    • Received : 2017-10-30
    • Accepted : 2018-02-28
    • Published : 2018-05-31
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