[1] GAO S T, YANG S, XUE M, et al. Total deformation and its role in heavy precipitation events associated with deformation-dominant flow patterns[J]. Advances in Atmospheric Sciences, 2008, 25(1): 11-23, https://doi.org/10.1007/s00376-008-0011-y
[2] FU S M, WANG H J, SUN J H, et al. Energy budgets on the interactions between the mean and eddy flows during a persistent heavy rainfall event over the Yangtze River valley in summer 2010[J]. Journal of Meteorological Research, 2016, 30(4): 513-527, https://doi.org/10.1007/s13351-016-5121-3
[3] LUO Y L, SUN J S, LI Y, et al. Science and prediction of heavy rainfall over China: Research progress since the reform and opening-up of new China[J]. Journal of Meteorological Research, 2020, 34(3): 427-459, https://doi.org/10.1007/s13351-020-0006-x
[4] SUN J H, FU S M, QANG H J, et al. Primary characteristics of the extreme heavy rainfall event over Henan in July 2021[J]. Atmospheric Science Letters, 2022, e1131, https://doi.org/10.1002/asl.1131
[5] GAO S T. The instability of the vortex sheet along the shear line[J]. Advances in Atmospheric Sciences, 2000, 17(4): 525-537, https://doi.org/10.1007/s00376-000-0016-7
[6] SHAO A M, QIU C J, LIU L P. Kinematic structure of a heavy rain event from dual-doppler radar observations[J]. Advances in Atmospheric Sciences, 2004, 21(4): 609-616, https://doi.org/10.1007/BF02915728
[7] WANG S Z, YU E T, WANG H J. A simulation study of a heavy rainfall process over the Yangtze River valley using the two-way nesting approach[J]. Advances in Atmospheric Sciences, 2012, 29(4): 731-743, https://doi.org/10.1007/s00376-012-1176-y
[8] KAWASHIMA M, FUJIYOSHI Y. Shear instability wave along a snowband: instability structure, evolution, and energetics derived from dual-Doppler radar data[J]. Journal of the Atmospheric Sciences, 2005, 62(2): 351-370, https://doi.org/10.1175/JAS-3392.1
[9] ZHANG X, YAO X P, MA J L, et al. Climatology of transverse shear lines related to heavy rainfall over the Tibetan Plateau during boreal summer[J]. Journal of Meteorological Research, 2016, 30(6): 915-926, https://doi.org/10.1007/s13351-016-6952-7
[10] LIU Z M, LI G P. Objective identification of the Tibetan Plateau shear line and statistical analysis of its spatiotemporal evolution features[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 2019, 43(1): 13-26, https://doi.org/10.3878/j.issn.1006-9895.1704.17209
[11] MA J L, YAO X P. Statistical analysis of the shear lines and torrential rains over the Yangtze-Huaihe river region during June-July in 1981-2013[J]. Acta Meteorologica Sinica (in Chinese), 2015, 73(5): 883-894, https://doi.org/10.11676/qxxb2015.065
[12] CHEN Y, ZHAI P M. Mechanisms for concurrent low-latitude circulation anomalies responsible for persistent extreme precipitation in the Yangtze River Valley [J]. Climate Dynamics, 2016, 47(3-4): 989-1006, https://doi.org/10.1007/s00382-015-2885-6
[13] CUI L L, SHI J, DU H Q, et al. Characteristics and trends of climatic extremes in China during 1959-2014[J]. Journal of Tropical Meteorology, 2017, 23(4): 368-379, https://doi.org/10.16555/j.1006-8775.2017.04.003
[14] MCGOVERN A, ELMORE K L, GAGNE Ⅱ D J, et al. Using artificial intelligence to improve real-time decision-making for high-impact weather[J]. Bulletin of the American Meteorological Society, 2017, 98(10): 2073-2090, https://doi.org/10.1175/BAMS-D-16-0123.1
[15] REICHSTEIN M, CAMPS-VALLS G, STEVENS B, et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature, 2019, 566: 195-204, https://doi.org/10.1038/s41586-019-0912-1
[16] HAM Y G, KIM J H, LUO J J. Deep learning for multi-year ENSO forecasts[J]. Nature, 2019, 573: 568-572, https://doi.org/10.1038/s41586-019-1559-7
[17] XIA J J, LI H C, KANG Y Y, et al. Machine learning-based weather support for the 2022 Winter Olympics[J]. Advances in Atmospheric Sciences, 2020, 37(9): 927-932, https://doi.org/10.1007/s00376-020-0043-5
[18] LIU J Q, DAI G F, OU X F. An innovative bias-correction approach to CMA-GD hourly quantitative precipitation forecasts[J]. Journal of Tropical Meteorology, 2021, 27(4): 428-436, https://doi.org/10.46267/j.1006-8775.2021.037
[19] LIU J Q, LI Z L, WANG Q Q. Quantitative precipitation forecasting using an improved probability-matching method and its application to a typhoon event[J]. Atmosphere, 2021, 12(10): 1346, https://doi.org/10.3390/atmos12101346
[20] FATHI M, KASHANI M H, JAMEⅡ S M, et al. Correction to: Big data analytics in weather forecasting: A systematic review[J]. Archives of Computational Methods in Engineering, 2022, 29: 733, https://doi.org/10.1007/s11831-021-09630-6
[21] DU M, LI G P, DING C C. The waves of the plateau transverse shear line and its possible connection with the low vortex[J]. Plateau Meteorology (in Chinese), 2018, 37(6): 1605-1615, https://doi.org/10.7522/j.issn.1000-0534.2018.00038
[22] HUANG Y, LI Q, FAN Y, et al. Objective identification of trough lines using gridded wind field data[J]. Atmosphere, 2017, 8(7): 121, https://doi.org/10.3390/atmos8070121
[23] HOU J, GAO T. A method of shear line detection in vector fields based on descriptive statistics of circular data[J]. Multimedia Tools and Applications, 2022, 81(15): 20853-20870, https://doi.org/10.1007/s11042-022-12734-1
[24] RYAN M, SAPUTRO A H, SOPAHELUWAKAN A. Intelligent low-level wind shear alert prediction system based on anemometer sensor network and temporal convolutional network (TCN)[J]. Geographia Technica, 2022, 17(1): 92-103, https://doi.org/10.21163/GT_2022.171.07
[25] XIONG A Y, ZHAO F, WANG Y, et al. Design and implementation of China integrated meteorological information sharing system (CIMISS)[J]. Journal of Applied Meteorological Science (in Chinese), 2015, 264): 500-512, https://doi.org/10.11898/1001-7313.20150412
[26] DEE D P, UPPALA S M, SIMMONS A J, et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(656): 553-597, https://doi.org/10.1002/qj.828
[27] BERRISFORD P, KÅLLBERG P, KOBAYASHI S, et al. Atmospheric conservation properties in ERA-Interim[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(659): 1381-1399, https://doi.org/10.1002/qj.864
[28] ALBERGEL C, DUTRA E, MUNIER S, et al. ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?[J]. Hydrology and Earth System Sciences, 2018, 22(6): 3515-3532. https://doi.org/10.5194/hess-22-3515-2018
[29] HERSBACH H, BELL B, BERRISFORD P, et al. The ERA5 global reanalysis[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730): 1999-2049, https://doi.org/10.1002/qj.3803
[30] CHEN B Y, YU W K, WANG W, et al. A global assessment of precipitable water vapor derived from GNSS zenith tropospheric delays with ERA5, NCEP FNL, and NCEP GFS products[J]. Earth and Space Science, 2021, 8(8): e2021EA001796, https://doi.org/10.1029/2021EA001796
[31] LIU Z Q, SHI C X, ZHOU Z J, et al. CMA global reanalysis (CRA-40): Status and plans [C]//5th International Conference on Reanalysis. Rome: Copernicus Climate Change Service, 2017.
[32] ZHAO B, ZHANG B, SHI C X, et al. Comparison of the global energy cycle between Chinese reanalysis interim and ECMWF reanalysis[J]. Journal of Meteorological Research, 2019, 33(3): 563-575, https://doi.org/10.1007/s13351-019-8129-7
[33] YE M S, YAO X P, ZHANG T, et al. Intercomparison of CRA-Interim precipitation products with ERA5 and JRA-55[J]. Journal of Tropical Meteorology, 2021, 27(2): 136-147, https://doi.org/10.46267/j.1006-8775.2021.013
[34] YU X J, ZHANG L X, ZHOU T J, et al. The Asian subtropical westerly jet stream in CRA-40, ERA5, and CFSR reanalysis data: Comparative assessment[J]. Journal of Meteorological Research, 2021, 35(1): 46-63, https://doi.org/10.1007/s13351-021-0107-1
[35] LI Z L, WAN J. Stability of the nonlinear wave on the horizontal shear-line of wind with the geostrophic momentum approximation[J]. Acta Meteorologica Sinica (in Chinese), 1995, 53(3): 289-298, https://doi.org/10.11676/qxxb1995.034
[36] FISCHER M S, TANG B H, CORBOSIERO K L. A climatological analysis of tropical cyclone rapid intensification in environments of upper-tropospheric troughs[J]. Monthly Weather Review, 2019, 147(10): 3693-3719, https://doi.org/10.1175/MWR-D-19-0013.1
[37] LINDERMAN G C, STEINERBERGER S. Clustering with t-SNE, provably[J]. SIAM Journal on Mathematics of Data Science, 2019, 1(2): 313-332, https://doi.org/10.1137/18M1216134
[38] ROUSSEEUW P J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis[J]. Journal of Computational and Applied Mathematics, 1987, 20: 53-65, https://doi.org/10.1016/0377-0427(87)90125-7
[39] ZHOU H B, GAO J T. Automatic method for determining cluster number based on silhouette coefficient[J]. Advanced Materials Research, 2014, 951: 227-230, https://doi.org/10.4028/www.scientific.net/AMR.951.227
[40] OKUBO A. Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences[J]. Deep Sea Research and Oceanographic Abstracts, 1970, 17(3): 445-454, https://doi.org/10.1016/0011-7471(70)90059-8
[41] WEISS J. The dynamics of enstrophy transfer in two-dimensional hydrodynamics[J]. Physica D: Nonlinear Phenomena, 1991, 48(2-3): 273-294, https://doi.org/10.1016/0167-2789(91)90088-Q
[42] LIU J Q, LI Z L. A case study of a heavy rainstorm in Hunan triggered by the induced cyclone of southwest Vortex[J]. Plateau Meteorology (in Chinese), 2020, 39(2): 311-320, DOI: 10.7522/j.issn.1000-0534.2019.00028