Short-Term Dynamic Radar Quantitative Precipitation Estimation Based on Wavelet Transform and Support Vector Machine

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  • Corresponding author: Changjiang ZHANG, zcj74922@zjnu.edu.cn
  • Funds:

    Supported by the National Natural Science Foundation of China (41575046), Project of Commonweal Technique and Application Research of Zhejiang Province of China (2016C33010), and Project of Shanghai Meteorological Center of China (SCMO-ZF- 2017011)

  • doi: 10.1007/s13351-020-9036-7

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  • Currently, Doppler weather radar in China is generally used for quantitative precipitation estimation (QPE) based on the ZR relationship. However, the estimation error for mixed precipitation is very large. In order to improve the accuracy of radar QPE, we propose a dynamic radar QPE algorithm with a 6-min interval that uses the reflectivity data of Doppler radar Z9002 in the Shanghai Qingpu District and the precipitation data at automatic weather stations (AWSs) in East China. Considering the time dependence and abrupt changes of precipitation, the data during the previous 30-min period were selected as the training data. To reduce the complexity of radar QPE, we transformed the weather data into the wavelet domain by means of the stationary wavelet transform (SWT) in order to extract high and low-frequency reflectivity and precipitation information. Using the wavelet coefficients, we constructed a support vector machine (SVM) at all scales to estimate the wavelet coefficient of precipitation. Ultimately, via inverse wavelet transformation, we obtained the estimated rainfall. By comparing the results of the proposed method (SWT-SVM) with those of Z = 300 × R1.4, linear regression (LR), and SVM, we determined that the root mean square error (RMSE) of the SWT-SVM method was 0.54 mm per 6 min and the average Threat Score (TS) could exceed 40% with the exception of the downpour category, thus remaining at a high level. Generally speaking, the SWT-SVM method can effectively improve the accuracy of radar QPE and provide an auxiliary reference for actual meteorological operational forecasting.
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  • Fig. 1.  Locations of the AWSs in the radar coverage.

    Fig. 2.  Data matching process.

    Fig. 3.  Structure of the SWT-SVM method.

    Fig. 4.  Rainfall estimation processing with training data during the past 6 × S min.

    Fig. 5.  Four wavelet basis functions: (a) Haar, (b) Daubechies, (c) Symlets, and (d) Morlet.

    Fig. 6.  RMSE for different training data during the past 6, 12, ···, and 120 min.

    Fig. 7.  Scatter plots for comparison of the estimated 6-min rainfall (mm) from (a) Z = 300 × R1.4, (b) LR, (c) SVM, and (d) SWT-SVM methods with the gauge 6-min rainful (mm). The black solid line shows a 1 : 1 relationship.

    Fig. 8.  TSs of estimated rainfall for four category rainfall using different methods (colored bars): Z = 300 × R1.4, LR, SVM, and SWT-SVM.

    Fig. 9.  Evaluation results of (a) RMSE, (b) CC (%), (c) MB, (d) MAE for the estimated rainfall [mm (6 min)–1], and TSs (%) of (e) light/moderate rain, (f) heavy rain, (g) rainstorm, and (h) downpour for 74 AWSs using the proposed SWT-SVM, Z = 300 × R1.4, LR, and SVM.

    Fig. 10.  Choropleth maps from 0706–0712 local time (LT) on 10 June 2017 of (a) radar reflectivity, (b) gauge rainfall, and QPE by (c) the proposed SWT-SVM method, (d) Z = 300 × R1.4, (e) LR, and (f) SVM.

    Fig. 11.  Choropleth maps from 1124–1130 LT 12 August 2017 of (a) radar reflectivity, (b) gauge rainfall, and QPE by (c) the proposed SWT-SVM method, (d) Z = 300 × R1.4, (e) LR, and (f) SVM.

    Table 1.  Technical characteristics of Doppler radar Z9002

    CharacteristicValue
    Position31º4'30''N, 120º57'28''E
    Height (above ground level)39 m
    Polarization typeSingle-polarization
    Wavelength10 cm (S-band)
    Maximum range460 km
    Useful range230 km
    Bin resolution1 km
    Scan interval (VCP21)6 min
    No. of elevations (VCP21)9 (0.5° –19.5°)
    Download: Download as CSV

    Table 2.  Categories of 6-min rainfall

    CategoryDrizzleLight/moderate rainHeavy rainRainstormDownpour
    6-min rainfall (mm)[0, 0.1)[0.1, 0.7)[0.7, 1.5)[1.5, 4)[4, +∞)
    Download: Download as CSV

    Table 3.  Evaluation results of estimated rainfall using different methods

    MethodRMSE
    [mm (6 min)–1]
    MB
    [mm (6 min)–1]
    MAE
    [mm (6 min)–1]
    CC
    (%)
    Z–R1.12 0.260.5352.74
    LR0.69–0.390.5854.32
    SVM0.85–0.350.3852.14
    SWT-SVM0.54–0.210.3774.72
    Download: Download as CSV
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