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

+ Author Affiliations + Find other works by these authors
  • Corresponding author: Changjiang ZHANG,
  • 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


  • 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.
  • 加载中
  • 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

    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

    [mm (6 min)–1]
    [mm (6 min)–1]
    [mm (6 min)–1]
    Z–R1.12 0.260.5352.74
    Download: Download as CSV
  • [1]

    Ayat, H., M. Reza Kavianpour, S. Moazami, et al., 2018: Calibration of weather radar using region probability matching method (RPMM). Theor. Appl. Climatol., 134, 165–176. doi:  10.1007/s00704-017-2266-7.

    Brandes, E. A., 1975: Optimizing rainfall estimates with the aid of radar. J. Appl. Meteor., 14, 1339–1345. doi:  10.1175/1520-0450(1975)014<1339:OREWTA>2.0.CO;2.

    Chen, H. N., and V. Chandrasekar, 2015a: Estimation of light rainfall using Ku-band dual-polarization radar. IEEE Trans. Geosci. Remote Sens., 53, 5197–5208. doi:  10.1109/TGRS.2015.2419212.

    Chen, H. N., and V. Chandrasekar, 2015b: The quantitative precipitation estimation system for Dallas–Fort Worth (DFW) urban remote sensing network. J. Hydrol., 531, 259–271. doi:  10.1016/j.jhydrol.2015.05.040.

    Chen, H. N., V. Chandrasekar, and R. Bechini, 2017: An improved dual-polarization radar rainfall algorithm (DROPS2.0): Application in NASA IFloodS field campaign. J. Hydrometeorol., 18, 917–937. doi:  10.1175/JHM-D-16-0124.1.

    Chumchean, S., A. Sharma, and A. Seed, 2006: An integrated approach to error correction for real-time radar-rainfall estimation. J. Atmos. Oceanic Technol., 23, 67–79. doi:  10.1175/JTECH1832.1.

    Cifelli, R., V. Chandrasekar, S. Lim, et al., 2011: A new dual-polarization radar rainfall algorithm: Application in Colorado precipitation events. J. Atmos. Oceanic Technol., 28, 352–364. doi:  10.1175/2010JTECHA1488.1.

    Cortes, C., and V. Vapnik, 1995: Support-vector networks. Machine Learning, 20, 273–297. doi:  10.1023/A:1022627411411.

    Crosson, W. L., C. E. Duchon, R. Raghavan, et al., 1996: Assessment of rainfall estimates using a standard Z-R relationship and the probability matching method applied to composite radar data in central Florida. J. Appl. Meteor., 35, 1203–1219. doi:  10.1175/1520-0450(1996)035<1203:AOREUA>2.0.CO;2.

    Eldardiry, H., E. Habib, and Y. Zhang, 2015: On the use of radar-based quantitative precipitation estimates for precipitation frequency analysis. J. Hydrol., 531, 441–453. doi:  10.1016/j.jhydrol.2015.05.016.

    Fujiwara, M., 1965: Raindrop-size distribution from individual storms. J. Atmos. Sci., 22, 585–591. doi:  10.1175/1520-0469(1965)022<0585:RSDFIS>2.0.CO;2.

    Gou, Y. B., Y. Z. Ma, H. N. Chen, et al., 2018: Radar-derived quantitative precipitation estimation in complex terrain over the eastern Tibetan Plateau. Atmos. Res., 203, 286–297. doi:  10.1016/j.atmosres.2017.12.017.

    Gou, Y. B., Y. Z. Ma, H. N. Chen, et al., 2019: Utilization of a C-band polarimetric radar for severe rainfall event analysis in complex terrain over eastern China. Remote Sens., 11, 22. doi:  10.3390/rs11010022.

    Habib, E., W. F. Krajewski, and A. Kruger, 2001: Sampling errors of tipping-bucket rain gauge measurements. J. Hydrol. Eng., 6, 159–166. doi:  10.1061/(asce)1084-0699(2001)6:2(159).

    He, J. J., K. Chen, J. S. Chen, et al., 2017: A multi-time scales SVM Method for local short-term rainfall prediction. Meteor. Mon., 43, 402–412. (in Chinese) doi:  10.7519/j.issn.1000-0526.2017.04.002.

    Jamaluddin, F. N., S. A. Ahmad, S. B. M. Noor, et al., 2015: Performance of DWT and SWT in muscle fatigue detection. Proc. 2015 IEEE Student Symposium in Biomedical Engineering & Sciences. IEEE, Shah Alam, Malaysia, 50–53, doi:  10.1109/ISSBES.2015.7435892.

    Jones, D. M. A., 1956: Rainfall Drop Size-distribution and Radar Reflectivity. ISWS Contract Report CR 009, Illinois State Water Survey, Illinois.

    Jung, J. Y., S. H. Jin, and M. S. Park, 2008: Precipitation analysis based on spatial linear regression model. Korean J. Appl. Stat., 21, 1093–1107. doi:  10.5351/KJAS.2008.21.6.1093.

    Kou, L. L., Z. H. Wang, and F. Xu, 2018: Three-dimensional fusion of spaceborne and ground radar reflectivity data using a neural network-based approach. Adv. Atmos. Sci., 35, 346–359. doi:  10.1007/s00376-017-6334-9.

    Kuang, Q. M., X. B. Yang, W. S. Zhang, et al., 2016: Spatiotemporal modeling and implementation for radar-based rainfall estimation. IEEE Geosci. Remote Sens. Lett., 13, 1601–1605. doi:  10.1109/LGRS.2016.2597170.

    Kusiak, A., X. P. Wei, A. Prakash, et al., 2013: Modeling and prediction of rainfall using radar reflectivity data: A data-mining approach. IEEE Trans. Geosci. Remote Sens., 51, 2337–2342. doi:  10.1109/TGRS.2012.2210429.

    Li, J., A. D. Heap, A. Potter, et al., 2011: Application of machine learning methods to spatial interpolation of environmental variables. Environ. Model. Softw., 26, 1647–1659. doi:  10.1016/j.envsoft.2011.07.004.

    Luo, G., and Z. Yang, 2018: The application of ECG cancellation in diaphragmatic electromyographic by using stationary wavelet transform. Biomed. Eng. Lett., 8, 259–266. doi:  10.1007/s13534-018-0064-5.

    Mortazavi, S. H., and S. M. Shahrtash, 2008. Comparing denoising performance of DWT, WPT, SWT and DT-CWT for Partial Discharge signals. 2008 43rd International Universities Power Engineering Conference, Padova, Italy, 1–4 September, IEEE, 1–6, doi:  10.1109/UPEC.2008.4651625.

    Quek, S. T., Q. Wang, L. Zhang, et al., 2001: Sensitivity analysis of crack detection in beams by wavelet technique. Int. J. Mech. Sci., 43, 2899–2910. doi:  10.1016/S0020-7403(01)00064-9.

    Ramli, S., S. H. A. Bakar, and W. Tahir, 2011: Radar hydrology: New Z/R relationships for Klang River Basin, Malaysia based on rainfall classification. 2011 IEEE Colloquium on Humanities, Science and Engineering, Penang, Malaysia, 5–6 December, IEEE, 537–541. doi:  10.1109/CHUSER.2011.6163790.

    Ryde, J. W., 1946: The attenuation of centimetre radio waves and the echo intensities resulting from atmospheric phenomena. J. Inst. Elec. Eng., 93, 101–103. doi:  10.1049/ji-3a-1.1946.0029.

    Sehad, M., M. Lazri, and S. Ameur, 2017: Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery. Adv. Space Res., 59, 1381–1394. doi:  10.1016/j.asr.2016.11.042.

    Seo, D. J., 1998: Real-time estimation of rainfall fields using radar rainfall and rain gage data. J. Hydrol., 208, 37–52. doi:  10.1016/S0022-1694(98)00141-3.

    Tang, Y. Q., X. B. Yang, W. S. Zhang, et al., 2018: Radar and rain gauge merging-based precipitation estimation via geographical–temporal attention continuous conditional random field. IEEE. Trans. Geosci. Remote Sens., 56, 5558–5571. doi:  10.1109/TGRS.2018.2819802.

    Thorndahl, S., J. E. Nielsen, and M. R. Rasmussen, 2014: Bias adjustment and advection interpolation of long-term high resolution radar rainfall series. J. Hydrol., 508, 214–226. doi:  10.1016/j.jhydrol.2013.10.056.

    Villarini, G., P. V. Mandapaka, W. F. Krajewski, et al., 2008: Rainfall and sampling uncertainties: A rain gauge perspective. J. Geophys. Res. Atmos., 113, D11102. doi:  10.1029/2007JD009214.

    Xiao, R. R., and V. Chandrasekar, 1997: Development of a neural network based algorithm for rainfall estimation from radar observations. IEEE Trans. Geosci. Remote Sens., 35, 160–171. doi:  10.1109/36.551944.