Vol.29, NO.6, 2018
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Article Navigation >  Journal of Applied Meteorological Science  > 2018  >  29(6): 724-736
Mao Jiajia, Zhang Xuefen, Wang Zhicheng, et al. Comparison of brightness temperature of multi-type ground-based microwave radiometers. J Appl Meteor Sci, 2018, 29(6): 724-736. DOI:  10.11898/1001-7313.20180608.
Citation: Mao Jiajia, Zhang Xuefen, Wang Zhicheng, et al. Comparison of brightness temperature of multi-type ground-based microwave radiometers. J Appl Meteor Sci, 2018, 29(6): 724-736. DOI:  10.11898/1001-7313.20180608.
shu

Comparison of Brightness Temperature of Multi-type Ground-based Microwave Radiometers

DOI: 10.11898/1001-7313.20180608
  • 1.

    Meteorological Observation Center of CMA, Beijing 100081

  • 2.

    Chengdu University of Information Technology, Chengdu 610225

  • 3.

    Key Laboratory of Atmosphere Sounding, China Meteorological Administration, Chengdu 610225

  • 4.

    Yantai Meteorological Administration of Shandong Province, Yantai 264000

  • Received Date: 2018-07-21
  • Rev Recd Date: 2018-08-31
  • Publish Date: 2018-11-30
    Abstract
  • Ground-based microwave radiometer (MWR) can detect temperature and humidity profiles continuously and steadily, which compensate the shortcoming of the conventional sounding because of the long observation time interval. As a result, it is very helpful to explore the thermal process evolution of meso-scale synoptic system. At present, many types of ground-based MWR are developed at home and abroad. They are of different technical systems and their suitability for wide operational use is much concerned in scientific research institutions and management departments.The error of MWR product includes the contribution of both algorithm and hardware system, which is hard to distinguish. Therefore, to evaluate the observation performance of hardware system of the MWR, the brightness temperature of MWR is directly compared in this experiment. Using observations of 4-type radiometers and operational sounding data at the testbed of China Meteorological Adminatration from January 2016 to March 2018, and the simulated brightness temperature based on forward calculation from sounding data of MonoRTM as the reference, the accuracy of radiometers in different weather and seasons is compared and analyzed.Results show that the accuracy of brightness temperature of the domestic radiometer is similar to that of the imported radiometer. The observed brightness temperature of 4 radiometers are well related with simulated brightness temperature, and correlation coefficients basically are above 0.9, reaching a significant level of 0.001. Under clear sky conditions, the average of mean square root between the observed and simulated brightness temperature of four radiometers is 2.08-3.75 K. And the MWR-G shows the smallest error of brightness temperature, whose average deviation of each channel is 1.08 K, and the root mean square error is 2.08 K. The brightness temperature errors are minimum in winter and maximum in summer. Under precipitation conditions, the effectiveness of the brightness temperature observation of MWR is obviously reduced.Certainly, there are also some errors in sounding data itself. And it is difficult to completely avoid the drifting problem of sounding balloon, although a variety of ground-based remote sensing methods are used to assist the identification. It suggests to develop and apply calibration system with high accuracy and high stability, to ensure the accurate measurement of the radiometer. In addition, best observation mode of MWR during precipitation, and the material selection, replacement and maintenance of the radome need to be tested and verified, to expand the effective detection range of MWR.
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  • Fig. 1  Input parameters of temperature and humidity profiles

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    Fig. 2  Effects of height and thickness of inversion layer with temperature increasing range on simulated brightness temperature

    (a)increasing range is 5 K, inversion layer thickness is 2 km, (b)inversion layer bottom height is 0, inversion layer thickness is 2 km, (c)temperature increasing range is 5 K, inversion layer bottom height is 0.5 km

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  Effects of cloud thickness, cloud height and liquid water content on simulated brightness temperature

    (a)cloud height is 2 km, liquid water content is 0.3 g·m-3, (b)cloud thickness is 1 km, liquid water content is 0.3 g·m-3, (c)cloud thickness is 1 km, cloud bottom height is 2 km

    DownLoad: Full-Size Img PowerPoint

    Fig. 4  Brightness temperature error of radiometers in different seasons

    (a)MWR-G, (b)MWR-A, (c)MWR-C1, (d)MWR-C2

    DownLoad: Full-Size Img PowerPoint

    Fig. 5  Correlation between brightness temperature of MWR-C1 and sounding temperature profile

    (a)non-precipitation, (b)precipitation

    DownLoad: Full-Size Img PowerPoint

    Fig. 6  Correlation between brightness temperature of MWR-C1 and sounding water vapor density profile

    (a)non-precipitation, (b)precipitation

    DownLoad: Full-Size Img PowerPoint

    Table  1  The main performance of microwave radiometers involved in the test

    设备编号 产地 通道数 测量高度/km 接收机技术体制 测量周期
    MWR-G 德国 14 0~10 多路直接检波 秒级
    MWR-A 美国 22 0~10 超外差本振跳频 分钟级
    MWR-C1 中国 22 0~10 超外差本振跳频 分钟级
    MWR-C2 中国 16 0~10 多路直接检波 秒级
    DownLoad: Download CSV

    Table  2  The central frequency of the simulated bright channel

    水汽通道序号 中心频率/GHz 氧气通道序号 中心频率/GHz
    1 22.24 8 51.26
    2 23.04 9 52.28
    3 23.84 10 53.86
    4 25.44 11 54.94
    5 26.24 12 56.66
    6 27.84 13 57.30
    7 31.40 14 58.00
    DownLoad: Download CSV

    Table  3  Abnormity elimination of brightness temperature data

    通道序号 MWR-G MWR-A MWR-C1 MWR-C2
    剔除样本量 剔除率/% 剔除样本量 剔除率/% 剔除样本量 剔除率/% 剔除样本量 剔除率/%
    1 6922 7.24 17637 29.70 11812 15.86 4620 10.48
    2 6805 7.12 5072 8.54 9745 13.09 5608 12.73
    3 7591 7.94 5009 8.43 10577 14.21 4375 9.93
    4 8139 8.51 5679 9.56 8872 11.92 5440 12.35
    5 8702 9.10 8287 13.95 5185 6.96 4098 9.30
    6 8579 8.97 3993 6.72 6085 8.17 3609 8.19
    7 11159 11.67 9417 15.86 5847 7.85 5126 11.63
    8 7227 7.56 3760 6.33 7610 10.22 79 0.18
    9 6634 6.94 3204 5.40 5334 7.16 113 0.26
    10 3680 3.85 1584 2.67 2808 3.77 124 0.28
    11 508 0.53 396 0.67 7447 10.00 302 0.69
    12 251 0.26 150 0.25 665 0.89 185 0.42
    13 275 0.29 635 1.07 511 0.69 70 0.16
    14 251 0.26 1037 1.75 450 0.60 41 0.09
    DownLoad: Download CSV

    Table  4  Deviation of observed and simulated brightness temperature in clear sky(unit:K)

    中心频率/GHz MWR-G MWR-A MWR-C1 MWR-C2
    平均偏差 均方根误差 平均偏差 均方根误差 平均偏差 均方根误差 平均偏差 均方根误差
    22.24 1.16 3.12 -1.92 4.74 3.63 5.19 2.77 5.24
    23.04 1.35 3.04 -0.87 4.82 0.79 2.95 2.30 5.19
    23.84 1.05 2.69 -1.61 4.34 1.60 3.32 1.97 4.24
    25.44 0.74 1.91 -0.08 2.90 2.82 3.84 1.96 4.26
    26.24 0.65 1.76 -1.93 4.15 1.30 1.91 1.83 3.70
    27.84 0.49 1.63 -2.21 4.89 1.10 1.64 1.58 3.42
    31.40 0.50 1.40 -1.47 3.61 0.70 1.52 1.60 3.18
    51.26 4.09 4.36 4.66 4.87 2.89 5.39 4.01 5.13
    52.28 2.99 3.38 4.02 4.17 4.90 5.64 3.87 4.68
    53.86 3.38 3.50 1.48 1.71 4.48 4.60 2.81 3.73
    54.94 0.10 0.44 -0.35 0.70 1.61 2.31 -0.27 2.43
    56.66 -0.48 0.66 -0.89 1.07 1.14 1.54 -1.33 2.47
    57.30 -0.46 0.67 -0.96 1.24 1.16 1.58 -1.42 2.44
    58.00 -0.42 0.64 -1.02 1.27 1.15 1.59 -1.55 2.43
    DownLoad: Download CSV

    Table  5  Deviation of observed and simulated brightness temperature in cloud samples(unit:K)

    中心频率/GHz MWR-G MWR-A MWR-C1 MWR-C2
    平均偏差 均方根误差 平均偏差 均方根误差 平均偏差 均方根误差 平均偏差 均方根误差
    22.24 0.56 3.43 -1.85 4.59 3.07 5.01 3.17 5.75
    23.04 0.72 3.40 -0.03 4.06 0.42 3.53 2.25 5.38
    23.84 0.30 3.26 -1.16 3.54 1.14 3.74 1.72 4.43
    25.44 -0.18 3.09 -0.03 3.28 2.25 4.06 1.55 4.39
    26.24 -0.35 3.17 -1.81 3.99 0.51 3.39 1.27 4.04
    27.84 -0.69 3.48 -2.04 4.57 0.22 3.57 0.94 3.87
    31.40 -0.93 4.08 -1.89 4.54 -0.46 4.17 0.86 4.16
    51.26 2.27 5.85 3.02 5.98 -0.02 6.18 2.23 5.92
    52.28 1.56 4.57 2.86 4.98 2.71 5.37 2.50 4.89
    53.86 3.06 3.35 1.33 1.86 3.88 4.12 2.58 3.49
    54.94 0.14 0.45 -0.31 0.67 2.04 2.65 -0.16 2.24
    56.66 -0.43 0.57 -0.72 0.93 1.12 1.45 -1.21 2.26
    57.30 -0.42 0.59 -0.74 0.92 1.16 1.48 -1.31 2.23
    58.00 -0.38 0.55 -0.99 1.23 1.27 1.63 -1.49 2.21
    DownLoad: Download CSV

    Table  6  Deviation of observed and simulated brightness temperature in cloud samples(unit:K)

    中心频率/GHz MWR-G MWR-A MWR-C1 MWR-C2
    平均偏差 均方根误差 平均偏差 均方根误差 平均偏差 均方根误差 平均偏差 均方根误差
    22.24 -35.68 70.45 -62.92 76.12 -19.72 51.28 -38.66 58.00
    23.04 -39.56 73.98 -66.91 80.20 -25.85 55.73 -41.13 60.97
    23.84 -45.00 80.25 -74.45 88.00 -29.19 59.55 -49.26 69.02
    25.44 -55.83 91.23 -84.45 98.81 -39.39 66.81 -59.42 80.78
    26.24 -60.28 95.69 -92.33 107.09 -44.63 71.42 -66.89 87.45
    27.84 -67.74 103.12 -101.39 116.55 -53.34 78.35 -73.29 94.98
    31.40 -81.37 115.12 -116.45 131.08 -69.85 91.08 -85.66 107.72
    51.26 -71.06 91.68 -90.02 101.08 -63.13 76.34 -154.44 155.22
    52.28 -53.67 69.76 -68.09 76.72 -46.22 56.42 -117.24 117.81
    53.86 -10.62 16.10 -17.04 19.54 -8.96 12.20 -27.17 27.35
    54.94 -0.19 3.37 -2.41 3.26 0.53 2.16 -2.56 3.43
    56.66 0.70 2.22 -0.90 1.50 1.06 1.37 0.07 2.05
    57.30 0.73 2.18 -0.89 1.60 1.28 1.60 -0.10 1.89
    58.00 0.79 2.14 -1.03 1.52 1.15 1.45 -0.11 2.02
    DownLoad: Download CSV
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    • Received : 2018-07-21
    • Accepted : 2018-08-31
    • Published : 2018-11-30
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