Severe convective weather can lead to a variety of disasters, but they are still difficult to be pre-warned and forecasted in the meteorological operation. This study generates a model based on the light gradient boosting machine (LightGBM) algorithm using C-band radar echo products and ground observations, to identify and classify three major types of severe convective weather ( i.e. , hail, short-term heavy rain (STHR), convective gust (CG)). The model evaluations show the LightGBM model performs well in the training set (2011-2017) and the testing set (2018) with the overall false identification ratio (FIR) of only 4.9% and 7.0%, respectively. Furthermore, the average probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR) for the three types of severe convective weather in two sample sets are over 85%, 65% and lower than 30%, respectively. The LightGBM model and the storm cell identification and tracking (SCIT) product are then used to forecast the severe convective weather 15 - 60 minutes in advance. The average POD, CSI and FAR for the forecasts of the three types of severe convective weather are 57.4%, 54.7% and 38.4%, respectively, which are significantly higher than those of the manual work. Among the three types of severe convective weather, the STHR has the highest POD and CSI and the lowest FAR, while the skill scores for the hail and CG are similar. Therefore, the LightGBM model constructed in this paper is able to identify, classify and forecast the three major types of severe convective weather automatically with relatively high accuracy, and has a broad application prospect in the future automatic meteorological operation.
KeywordsSevere Convective WeatherMachine LearningLightGBMEarly Warning and Forecast
Doswell, C.A. (2015) Severe Convective Storms in the European Societal Context. Atmospheric Research, 158-159, 210-215. https://doi.org/10.1016/j.atmosres.2014.08.007
Yu, X. and Zheng, Y. (2020) Advances in Severe Convection Research and Operation in China. Journal of Meteorological Research, 34, 189-217. https://doi.org/10.1007/s13351-020-9875-2
Zhou, K., Zheng, Y., Li, B., Dong, W. and Zhang, X. (2019) Forecasting Different Types of Convective Weather: A Deep Learning Approach. Journal of Meteorological Research, 33, 797-809. https://doi.org/10.1007/s13351-019-8162-6
Han, L., Sun, J.Z. and Zhang, W. (2019) Convolutional Neural Network for Convective Storm Nowcasting Using 3-D Doppler Weather Radar Data. IEEE Transactions on Geoscience and Remote Sensing, 58, 1487-1495. https://doi.org/10.1109/TGRS.2019.2948070
Prudden, R., Adams, S., Kangin, D., Robinson, N., Ravuri, S., Mohamed, S. and Arribas, A. (2020) A Review of Radar-Based Nowcasting of Precipitation and Applicable Machine Learning Techniques. arXiv:2005.04988.
Aydin, K. and Giridhar, V. (1992) C-Band Dual-Polarization Radar Observables in Rain. Journal of Atmospheric and Oceanic Technology, 9, 383-390. https://doi.org/10.1175/1520-0426(1992)009%3C0383:CBDPRO%3E2.0.CO;2
Féral, L., Sauvageot, H. and Soula, S. (2003) Hail Detection Using S- and C-Band Radar Reflectivity Difference. Journal of Atmospheric and Oceanic Technology, 20, 233-248. https://doi.org/10.1175/1520-0426(2003)020%3C0233:HDUSAC%3E2.0.CO;2
Seed, A.W. (2003) A Dynamic and Spatial Scaling Approach to Advection Forecasting. Journal of Applied Meteorology and Climatology, 42, 381-388. https://doi.org/10.1175/1520-0450(2003)042%3C0381:ADASSA%3E2.0.CO;2
Fox, N.I. and Wikle, C.K. (2005) A Bayesian Quantitative Precipitation Nowcast Scheme. Weather and Forecasting, 20, 264-275. https://doi.org/10.1175/WAF845.1
Mecikalski, J.R., Williams, J.K., Jewett, C.P., Ahijevych, D., LeRoy, A. and Walker, J.R. (2015) Probabilistic 0-1-h Convective Initiation Nowcasts That Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data. Journal of Applied Meteorology and Climatology, 54, 1039-1059. https://doi.org/10.1175/JAMC-D-14-0129.1
McGovern, A., Elmore, K.L., Gagne, D.J., Haupt, S.E., Karstens, C.D., Lagerquist, R., Smith, T. and Williams, J.K. (2017) Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. Bulletin of the American Meteorological Society, 98, 2073-2090. https://doi.org/10.1175/BAMS-D-16-0123.1
Czernecki, B., Taszarek, M., Marosz, M., Półrolniczak, M., Kolendowicz, L., Wyszogrodzki, A. and Szturc, J. (2019) Application of Machine Learning to Large hail Prediction—The Importance of Radar Reflectivity, Lightning Occurrence and Convective Parameters Derived from ERA5. Atmospheric Research, 227, 249-262. https://doi.org/10.1016/j.atmosres.2019.05.010
Lagerquist, R., McGovern, A. and Smith, T. (2017) Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind. Weather and Forecasting, 32, 2175-2193. https://doi.org/10.1175/WAF-D-17-0038.1
Łoś, M., Smolak, K., Guerova, G. and Rohm, W. (2020) GNSS-Based Machine Learning Storm Nowcasting. Remote Sensing, 12, Article No. 2356. https://doi.org/10.3390/rs12162536
Mostajabi, A., Finney, D.L., Rubinstein, M. and Rachidi, F. (2019) Nowcasting Lightning Occurrence from Commonly Available Meteorological Parameters Using Machine Learning Techniques. npj Climate and Atmospheric Science, 2, Article No. 41. https://doi.org/10.1038/s41612-019-0098-0
Williams, J.K. (2014) Using Random Forests to Diagnose Aviation Turbulence. Machine Learning, 95, 51-70. https://doi.org/10.1007/s10994-013-5346-7
Wiesmeier, M., Barthold, F., Blank, B. and Kögel-Knabner, I. (2011) Digital Mapping of Soil Organic Matter Stocks Using Random Forest Modeling in a Semi-Arid steppe Ecosystem. Plant and Soil, 340, 7-24. https://doi.org/10.1007/s11104-010-0425-z
Gao, X., Luo, H., Wang, Q., Zhao, F., Ye, L. and Zhang, Y. (2019) A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM. Sensors, 19, Article No. 947. https://doi.org/10.3390/s19040947
Chen, C., Zhang, Q., Ma, Q. and Yu, B. (2019) LightGBM-PPI: Predicting Protein-Protein Interactions through LightGBM with Multi-Information Fusion. Chemometrics and Intelligent Laboratory Systems, 191, 54-64. https://doi.org/10.1016/j.chemolab.2019.06.003
Ma, X., Sha, J., Wang, D., Yu, Y., Yang, Q. and Niu, X. (2018) Study on a Prediction of P2P Network Loan Default Based on the Machine Learning LightGBM and XGboost Algorithms According to Different High Dimensional Data Cleaning. Electronic Commerce Research and Applications, 31, 24-39. https://doi.org/10.1016/j.elerap.2018.08.002
Jiang, M., Liu, J., Zhang, L. and Liu, C. (2020) An Improved Stacking Framework for Stock Index Prediction by Leveraging Tree-Based Ensemble Models and Deep Learning Algorithms. Physica A: Statistical Mechanics and its Applications, 541, Article ID: 122272. https://doi.org/10.1016/j.physa.2019.122272
Sun, X., Liu, M. and Sima, Z. (2020) A Novel Cryptocurrency Price Trend Forecasting Model Based on LightGBM. Finance Research Letters, 32, Article ID: 101084. https://doi.org/10.1016/j.frl.2018.12.032
Zhang, C., Wu, M., Chen, J., Chen, K., Zhang, C., Xie, C., Huang, B. and He, Z. (2019) Weather Visibility Prediction Based on Multimodal Fusion. IEEE Access, 7, 74776-74786. https://doi.org/10.1109/ACCESS.2019.2920865
Zhang, Y., Zhang, R., Ma, Q., Wang, Y., Wang, Q., Huang, Z. and Huang, L. (2020) A Feature Selection and Multi-Model Fusion-Based Approach of Predicting Air Quality. ISA Transactions, 100, 210-220. https://doi.org/10.1016/j.isatra.2019.11.023
Ju, Y., Sun, G., Chen, Q., Zhang, M., Zhu, H. and Rehman, M.U. (2019) A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting. IEEE Access, 7, 28309-28318. https://doi.org/10.1109/ACCESS.2019.2901920
Fan, J., Ma, X., Wu, L., Zhang, F., Yu, X. and Zeng, W. (2019) Light Gradient Boosting Machine: An Efficient Soft Computing Model for Estimating Daily Reference Evapotranspiration with Local and External Meteorological Data. Agricultural Water Management, 225, Article ID: 105758. https://doi.org/10.1016/j.agwat.2019.105758
Gagne, D.J., McGovern, A., Haupt, S.E., Sobash, R.A., Williams, J.K. and Xue, M. (2017) Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles. Weather and Forecasting, 32, 1819-1840. https://doi.org/10.1175/WAF-D-17-0010.1
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. and, Liu, T.-Y. (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, December 2017, 3149-3157.
Yasser, K. and Hemayed, E. (2017) Novelty Detection for Location Prediction Problems Using Boosting Trees. 2017 International Conference on Computational Science and Its Applications, Trieste, 3-6 July 2017, 173-182. https://doi.org/10.1007/978-3-319-62395-5_13
Friedman, J.H. (2001) Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29, 1189-1232. https://doi.org/10.1214/aos/1013203450
Kaltenboeck, R. and Ryzhkov, A. (2013) Comparison of Polarimetric Signatures of Hail at S and C Bands for Different Hail Sizes. Atmospheric Research, 123, 323-336. https://doi.org/10.1016/j.atmosres.2012.05.013
Marzano, F.S., Scaranari, D., Montopoli, M. and Vulpiani, G. (2008) Supervised Classification and Estimation of Hydrometeors from C-Band Dual-Polarized Radars: A Bayesian Approach. IEEE Transactions on Geoscience and Remote Sensing, 46, 85-98. https://doi.org/10.1109/TGRS.2007.906476
Johnson, J.T., Mackeen, P.L., Witt, A., Mitchell, E.D., Stumpf, G.J., Eilts, M.D. and Thomas, K.W. (1998) The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm. Weather and Forecasting, 13, 263-276. https://doi.org/10.1175/1520-0434(1998)013%3C0263:TSCIAT%3E2.0.CO;2