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The automatic recognition and early warning of SL are important objectives in the field of meteorology. By collecting the new-generation weather RADARs (CINRAD\/SA and CINRAD\/SAD) base data during 12 SL weather events occurred in Jiangsu, Shanghai, Shandong, Hebei, and other regions of China from 2019 to 2021, the dataset has a total of 49,920 samples with a window size of 40 km. The 40 km area was labeled by employing manual classification and data augmentation to construct the deep learning dataset with a positive and negative sample ratio of 1:1, of which 80% and 20% are separated as the training and test set, respectively. Based on the echo height of each elevation beam at different distances, three deep learning-based models are trained for SL automatic recognition, which include a near-distance model (M1) trained by the data in nine RADAR elevation angles within 45 km from RADARs, a mid-distance model (M2) by the data in six elevations from 45 to 135 km, and a far-distance model (M3) by the data in three elevations from 135 to 230 km. A confusion matrix and its derived metrics including receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) are introduced as the indicators to evaluate the models by the test dataset. The results indicate that the accuracy of models are over 86% with the hit rates over 87%, the false alarm rates less than 21%, and the critical success indexes (CSI) surpass 78%. All the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.95, so the three models have high hit rates and low false alarm rates for ensuring SL discrimination. Finally, the effectiveness of the models is further demonstrated through two SL events detected with Nanjing, Yancheng and Qingpu RADARs.<\/jats:p>","DOI":"10.3390\/rs15194726","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T03:49:14Z","timestamp":1695786554000},"page":"4726","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["RADAR Echo Recognition of Squall Line Based on Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Peilong","family":"Xie","sequence":"first","affiliation":[{"name":"College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4991-3647","authenticated-orcid":false,"given":"Zhiqun","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"},{"name":"Key Laboratory of Atmosphere Sounding, CMA, Chengdu 610225, China"}]},{"given":"Shujie","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5836-6839","authenticated-orcid":false,"given":"Jiafeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Hanyuan","family":"Tian","sequence":"additional","affiliation":[{"name":"State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"},{"name":"College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Fen","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"},{"name":"Key Laboratory of Transportation Meteorology, CMA, Nanjing 210041, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1175\/1520-0450(1983)022<0297:CAODRD>2.0.CO;2","article-title":"Correlation Analysis of Doppler Radar Data and Retrieval of the Horizontal Wind","volume":"22","author":"Smythe","year":"1983","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/273287a0","article-title":"Three-Dimensional Storm Motion Detection by Conventional Weather Radar","volume":"273","author":"Rinehart","year":"1978","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1175\/JTECH-D-18-0194.1","article-title":"A Method of Radar Echo Extrapolation Based on TREC and Barnes Filter","volume":"36","author":"Zou","year":"2019","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1175\/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2","article-title":"The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm","volume":"13","author":"Johnson","year":"1998","journal-title":"Weather Forecast."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105080","DOI":"10.1016\/j.jastp.2019.105080","article-title":"A Rapid Identification and Warning Method for Severe Weather via Doppler Radar Based on an Improved TITAN Algorithm","volume":"193","author":"Wang","year":"2019","journal-title":"J. Atmos. Sol.-Terr. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18993","DOI":"10.1007\/s11042-021-10689-3","article-title":"An Automatic Identifying Method of the Squall Line Based on Hough Transform","volume":"80","author":"Wang","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Representations by Back-Propagating Errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_10","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_11","unstructured":"LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. (1989, January 27\u201330). Handwritten Digit Recognition with a Back-Propagation Network. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_14","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s13351-019-8162-6","article-title":"Forecasting Different Types of Convective Weather: A Deep Learning Approach","volume":"33","author":"Zhou","year":"2019","journal-title":"J. Meteorol. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3611","DOI":"10.5194\/gmd-16-3611-2023","article-title":"Convective-Gust Nowcasting Based on Radar Reflectivity and a Deep Learning Algorithm","volume":"16","author":"Xiao","year":"2023","journal-title":"Geosci. Model Dev."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guo, S., Sun, N., Pei, Y., and Li, Q. (2023). 3D-UNet-LSTM: A Deep Learning-Based Radar Echo Extrapolation Model for Convective Nowcasting. Remote Sens., 15.","DOI":"10.3390\/rs15061529"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1175\/1520-0493(1993)121<0726:GCOSLO>2.0.CO;2","article-title":"General Characteristics of Squall Lines Observed in TAMEX","volume":"121","author":"Chen","year":"1993","journal-title":"Mon. Weather Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1175\/1520-0434(1998)013<0860:MCSITS>2.0.CO;2","article-title":"Mesoscale Convective Systems in the Southeast United States during 1994\u201395: A Survey","volume":"13","author":"Geerts","year":"1998","journal-title":"Weather Forecast."},{"key":"ref_20","first-page":"252","article-title":"Development of Fuzzy-Logical Two-Step Ground Clutter Detection Algorithm","volume":"65","author":"Liu","year":"2007","journal-title":"Acta Meteorol. Sin."},{"key":"ref_21","first-page":"251","article-title":"Comparison of different attenuation correction methods and their effects on estimated rainfall using x-band dual linear polarimetric radar","volume":"66","author":"Hu","year":"2008","journal-title":"Acta Meteorol. Sin."},{"key":"ref_22","first-page":"147","article-title":"Identification of Non-Precipitation Meteorological Echoes with Doppler Weather Radar","volume":"23","author":"Feng","year":"2012","journal-title":"J. Appl. Meteorol. Sci."},{"key":"ref_23","first-page":"748","article-title":"Ka-Band Millimeter Wave Cloud Radar Data Quality Control","volume":"35","author":"Zheng","year":"2016","journal-title":"J. Infrared Millim. 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