Zhang Lin, Li Feng, Wu Lei, et al. Non-precipitation identification technique for CINRAD/SAD dual polarimetric weather radar. J Appl Meteor Sci, 2022, 33(6): 724-735. DOI:  10.11898/1001-7313.20220607.
Citation: Zhang Lin, Li Feng, Wu Lei, et al. Non-precipitation identification technique for CINRAD/SAD dual polarimetric weather radar. J Appl Meteor Sci, 2022, 33(6): 724-735. DOI:  10.11898/1001-7313.20220607.

Non-precipitation Identification Technique for CINRAD/SAD Dual Polarimetric Weather Radar

DOI: 10.11898/1001-7313.20220607
  • Received Date: 2022-06-04
  • Rev Recd Date: 2022-09-06
  • Available Online: 2022-11-21
  • Publish Date: 2022-11-17
  • In China, the operational upgrade of dual polarimetric weather radar is being promoted. CINRAD/SAD dual polarimetric weather radar in some provinces such as Guangdong, Jiangsu, Shandong and Zhejiang has been upgraded in operation. By June of 2021, there are 69 dual polarimetric weather radars in national radar network, and it will increase to more than 100 in the future. The dual polarimetric radar is an important detection equipment for studying the microphysical process of precipitation, which can provide multiple polarizations including raindrop spectrum information, and thus better describe the microphysical characteristics of precipitation. The technical upgrade will bring revolutionary changes for data quality control, hydrogel classification and quantitative precipitation estimation. With the measurement parameters such as correlation coefficient or differential reflectivity, the dual-polarimetric weather radar can effectively remove non-precipitation echoes such as ground clutter, anomalous propagation, electromagnetic interference, sea waves, clear air clutter and so on. Based on the non-precipitation identification technique on S-band WSR-88D dual polarization weather radar, the distribution characteristics of correlation coefficient and differential reflectivity in precipitation echo and clutter are analyzed. The CINRAD/SAD dual-polarimetric weather radar data are used to test and improve the algorithm to adapt domestic weather radar, the differential reflectivity texture feature is added in the improved algorithm and the distribution characteristics of differential reflectivity horizontal texture on precipitation echo and clutter are analyzed, to better remove non-precipitation echo. During the evaluation of algorithm, several cases such as hail, melting layer, typhoon and different types of clutters during May-October in 2019 and 2020 are investigated. The results show that the improved algorithm can identify 95.2% of non-precipitation echoes, and the error rate of precipitation is 2.6%. For the large area clear air clutter, after adding the differential reflectivity texture feature, combining with the correlation coefficient texture feature, the accuracy of the algorithm is improved from 68.6% to 96.8% for one case, but the overall accuracy is less than 90% for many cases, and it needs to be improved by deep learning method in the future. Non-precipitation identification algorithm on CINRAD/SAD is applied in mosaic image, showing great application prospect in the future for precipitation classification and quantitative precipitation estimation. It can provide high quality data and play an important role in real-time operation.
  • Fig. 1  Correlation coefficient and differential reflectivity for precipitation echo and clutter

    (a)correlation coefficient of precipitation, (b)correlation coefficient of non-precipitation, (c)differential reflectivity of precipitation, (d)differential reflectivity of non-precipitation

    Fig. 2  Parameters observed by CINRAD/SAD dual-polarimetric radar of Xuzhou at 013019 BT 3 Sep 2019

    (distance of adjacent circles is 50 km, the same hereinafter)

    Fig. 3  Recognition before and after WSR-88D quality control for the case in Fig. 2

    Fig. 4  Texture features of correlation coefficient and differential reflectivity for precipitation and non-precipitation

    (a)correlation coefficient texture of precipitation, (b)correlation coefficient texture of non-precipitation, (c)differential reflectivity texture of precipitation, (d)differential reflectivity texture of non-precipitation

    Fig. 5  Flow chart of non-precipitation identification algorithm on CINRAD/SAD dual-polarimetric weather radar

    Fig. 6  Reflectivity factor of large clear air before and after radar quality control

    Fig. 7  Reflectivity factor of hailstorm and melting layer before and after radar quality control observed by CINRAD/SAD dual-polarimetric weather radar of Xuzhou at 1335 BT 1 Aug 2019

    Fig. 8  Reflectivity factor of typhoon precipitation before and after radar quality control observed by CINRAD/SAD dual-polarimetric weather radar of Yancheng at 0005 BT 11 Aug 2019

    Fig. 9  Reflectivity factor of ground clutter, anomalous propagation and electromagnetic interference process before and after radar quality control observed by CINRAD/SAD dual-polarimetric weather radar of Yancheng at 2303 BT 30 Jul 2019

    Fig. 10  Reflectivity factor of precipitation and large clear air process before and after radar quality control algorithm observed by CINRAD/SAD dual-polarimetric weather radar of Xuzhou at 0336 BT 2 Sep 2019

    Fig. 11  Mosaic image of Gaoyou Tornado before and after quality control observed by six CINRAD/SAD dual-polarimetric weather radars at 1400 BT 12 Jun 2020

    Table  1  Quality control algorithm evaluation of cases

    种类 非降水回波识别准确率/% 降水回波误判率/%
    冰雹、融化层 96.5 1.2
    台风降水 1.8
    电磁干扰、小面积晴空 99.2
    地物、超折射、电磁干扰 95.7 2.2
    大面积晴空 90.9 2.0
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