[1] BOWLER N E. Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model[J]. Tellus A, 2006, 58(5): 538-548, https://doi.org/10.1111/j.1600-0870.2006.00197.x
[2] SCHWARTZ C S, ROMINE G S, SMITH K R, et al. Characterizing and optimizing precipitation forecasts from a convection permitting ensemble initialized by a mesoscale ensemble Kalman filter[J]. Weather and Forecasting, 2014, 29: 1295-1318. https://doi.org/10.1175/WAF-D-13-00145.1
[3] ZHANG X B. A GRAPES-based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance[J]. Quarterly Journal of the Royal Meteorological Society, 2018, 144(711): 478-498, https://doi.org/10.1002/qj.3220.
[4] ZHANG X B. Application of a convection-permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX[J]. Quarterly Journal of the Royal Meteorological Society, 2018, 144(717): 2842-2862, https://doi.org/10.1002/qj.3414.
[5] HUANG X Y, LI H, ZHAO H S, et al. Objective approach for rainstorm based on dual-factor feature extraction and generalized regression neural network[J]. Natural Hazards, 2020, 104:1987-2002, https://doi.org/10.1007/s11069-020-04258-4.
[6] MAEJIMA Yasumitsu, MIYOSHI Takemasa, KUNII Masaru, et al. Impact of dense and frequent surface observations on 1-minute-update severe rainstorm prediction:a simulation study[J]. Journal of the Meteorological Society of Japan, 2019, 97(1):253-273, https://doi.org/10.2151/jmsj.2019-014.
[7] ZHANG H L, PU Z X. Beating the uncertainties:ensemble forecasting and ensemble-based data assimilation in modern numerical weather prediction[J]. Advances in Meteorology, 2010, 432160, https://doi.org/10.1155/2010/432160.
[8] QIAN W, DU J, AI Y. A review: anomaly-based versus fullfield-based weather analysis and forecasting[J]. Bulletin of the American Meteorological Society, 2021, 102(4): E849-E870, https://doi.org/10.1175/BAMS-D-19-0297.1.
[9] KOBER K, CRAIG G C, KEIL C, et al. Blending a probabilistic nowcasting method with a high-resolution numerical weather prediction ensemble for convective precipitation forecasts[J]. Quarterly Journal of the Royal Meteorological Society, 2012, 138(664):755-768, https://doi.org/10.1002/qj.939.
[10] LI J H, GAO Y D, WAN Q L. Sample optimization of ensemble forecast to simulate a tropical cyclone using the observed track[J]. Atmosphere-Ocean, 2018, 56(3): 112-128, https://doi.org/10.1080/07055900.2018.1500881.
[11] LI J H, ZHANG Z Y, WAN Q L, et al. Study of eleven tropical cyclones simulated by sample optimization of an ensemble forecast based on the observed track[J]. Atmosphere-Ocean, 2020, 58(3): 157-172, https://doi.org/10.1080/07055900.2020.1770053.
[12] LI J H, WAN Q L, XU D S, et al. An initialization scheme for weak tropical cyclones in the South China Sea[J]. Journal of Meteorological Research, 2021, 35(2): 358-370, https://doi.org/10.1007/s13351-021-0069-3.
[13] LI J H, ZHANG Z Y, LIU L, et al. The simulation of five tropical cyclones by sample optimization of ensemble forecasting based on the observed track and intensity[J]. Advances in Atmospheric Sciences, 2021, 38(10): 1763-1777, https://doi.org/10.1007/s00376-021-0353-2.
[14] LI J H, WAN Q L, GAO Y D, et al. The effect of sample optimization on the ensemble Kalman filter in forecasting typhoon Rammasun (2014)[J]. Journal of Tropical Meteorology, 2018, 24(4):433-447, https://doi.org/10.16555/j.1006-8775.2018.04.003.
[15] ZHANG X B. Case dependence of multiscale interactions between multisource perturbations for convectionpermitting ensemble forecasting during SCMREX[J]. Monthly Weather Review, 2021, 149: 1853-1871, https://doi.org/10.1175/MWR-D-20-0316.1.
[16] ZHANG X B. Impacts of different perturbation methods on multiscale interactions between multisource perturbations for convection-permitting ensemble forecasting during SCMREX[J]. Quarter Journal of the Royal Meteorological Society 2021, (in press), https://doi.org/10.1002/qj.4160.
[17] ZHANG X B, LUO Y L, WAN Q L, et al. Impact of assimilating wind profiling radar observations on convection-permitting quantitative precipitation forecasts during SCMREX[J]. Weather and Forecasting, 2016, 31 (4): 1271-1292, https://doi.org/10.1175/WAF-D-15-0156.1.
[18] BAO X H, LUO Y L, SUN J, et al. Assimilating Doppler radar observations with an ensemble Kalman filter for convection-permitting prediction of convective development in a heavy rainfall event during the presummer rainy season of south China[J]. Science China Earth Sciences, 2017, 60: 1866-1885, https://doi.org/10.1007/s11430-017-9076-9.
[19] ZHANG X B. Multiscale characteristics of differentsource perturbations and their interactions for convectionpermitting ensemble forecasting during SCMREX[J]. Monthly Weather Review, 2019, 147(1): 291-310, https://doi.org/10.1175/MWR-D-18-0218.1.
[20] NIESEN E R, SCHUMACHER R S. Using convectionallowing ensembles to understand the predictability of an extreme rainfall event[J]. Monthly Weather Review, 2016, 144(10): 3651-3676, https://doi.org/10.1175/MWRD-16-0083.1
[21] MELHAUSER C, ZHANG F Q. Practical and intrinsic predictability of severe and convective weather at the mesoscales[J]. Journal of the Atmospheric Sciences, 2012, 69(11): 3350-3371, https://doi.org/10.1175/JAS-D-11-0315.1.
[22] ZHANG Y J, ZHANG F Q, STENSRUD D J, et al. Intrinsic predictability of the 20 May 2013 tornadic thunderstorm event in Oklahoma at storm scales[J]. Monthly Weather Review, 2016, 144(4): 1273-1298, https://doi.org/10.1175/MWR-D-15-0105.1.
[23] WU M, LUO Y. Mesoscale observational analysis of lifting mechanism of a warm-sector convective system producing the maximal daily precipitation in China mainland during pre-summer rainy season of 2015[J]. Journal of Meteorological Research, 2016, 30(5): 719-736. https://doi.org/10.1007/s13351-016-6089-8.
[24] SCHUMACHER R. Ensemble-based analysis of factors leading to the development of a multiday warm-season heavy rain event[J]. Monthly Weather Review, 2011, 139 (9): 3016-3035. https://doi.org/10.1175/MWR-D-10-05022.1.
[25] ZHANG F, MENG Z. Impact of synoptic-scale factors on rainfall forecast in different stages of a persistent heavy rainfall event in south China[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(7): 3574-3593, https://doi.org/10.1002/2017JD028155.
[26] CHEN G T J. Observational aspects of the Mei-Yu phenomena in subtropical China[J]. Journal of the Meteorological Society of Japan, 1983, 61(2): 306-312, https://doi.org/10.2151/jmsj1965.61.2_306.
[27] CHEN X, ZHAO K, XUE M. Spatial and temporal characteristics of warm season convection over Pearl River Delta region, China, based on 3 years of operational radar data[J]. Journal of Geophysical Research: Atmospheres, 2014, 119(22): 12447-12465. https://doi.org/10.1002/2014JD021965.
[28] DU Y, CHEN G X. Heavy rainfall associated with double low-level jets over southern China, Part Ⅰ: ensemblebased analysis[J]. Monthly Weather Review, 2018, 146 (11): 3827-3844, https://doi.org/10.1175/MWR-D-18-0101.1.
[29] CHEN X, ZHANG F, ZHAO K. Influence of monsoonal wind speed and moisture content on intensity and diurnal variations of the Mei-Yu season coastal rainfall over South China[J]. Journal of the Atmospheric Sciences, 2017, 74(9): 2835-2856, https://doi.org/10.1175/JAS-D-17-0081.1.
[30] WANG H, LUO Y L, JOU B J. Initiation, maintenance, and properties of convection in an extreme rainfall event during SCMREX: observational analysis[J]. Journal of Geophysical Research: Atmospheres, 2014, 119(23): 13206-13232, https://doi.org/10.1002/2014JD022339.
[31] LIANG Z, LIU Y, YIN J, et al. A case study of the effects of a synoptic situation on the motion and development of warm-sector mesoscale convective systems over south China[J], Asia-Pacific Journal of Atmospheric Sciences, 2019, 55: 255-268, https://doi.org/10.1007/s13143-018-0063-6.
[32] HONG S, DUDHIA J, CHEN S. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation[J]. Monthly Weather Review, 2004, 132(1): 103-120, https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2. doi: 10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
[33] NOH Y, CHEON W G, HONG S Y, et al. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data[J]. Boundary-Layer Meteorology, 2003, 107: 401-427, https://doi.org/10.1023/A:1022146015946.
[34] GRELL G A, DEVENYI D. A generalized approach to parameterizing convection combining ensemble and data assimilation techniques[J]. Geophysical Research Letters, 2002, 29(14): 381-384, https://doi.org/10.1029/2002GL015311.
[35] BARKER D M, HUANG W, GUO Y R, et al. A threedimensional variational data assimilation system for MM5: Implementation and initial results[J]. Monthly Weather Review, 2004, 132(4): 897-914, https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2. doi: 10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2
[36] MENG Z, ZHANG F. Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation, Part Ⅲ: Comparison with 3DVAR in a real-data case study[J]. Monthly Weather Review, 2008, 136: 522-540, https://doi.org/10.1175/2007MWR2106.1.
[37] MENG Z, ZHANG F. Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation, Part Ⅳ: Performance over a warm-season month of June 2003 [J]. Monthly Weather Review, 2008, 136(2): 3671-3682, https://doi.org/10.1175/2008MWR2270.1.
[38] ZHU L, WAN Q L, SHEN X, et al. Prediction and predictability of high-impact Western Pacific landfalling tropical cyclone Vicente (2012) through convectionpermitting ensemble assimilation of Doppler radar velocity[J]. Monthly Weather Review, 2016, 144(1): 21- 43, https://doi.org/10.1175/MWR-D-14-00403.1.
[39] ZHANG F, SNYDER C, SUN J. Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman Filter[J]. Monthly Weather Review, 2004, 132(5): 1238-1253, https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2. doi: 10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2