Anderson, J. L., and S. L. Anderson, 1999: A monte carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741−2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2.
Bachmann, K., C. Keil, and M. Weissmann, 2019: Impact of radar data assimilation and orography on predictability of deep convection. Quart. J. Roy. Meteorol. Soc., 145, 117−130, https://doi.org/10.1002/qj.3412.
Bachmann, K., C. Keil, G. C. Craig, M. Weissmann, and C. A. Welzbacher, 2020: Predictability of deep convection in idealized and operational forecasts: Effects of radar data assimilation, orography, and synoptic weather regime. Mon. Wea. Rev., 148, 63−81, https://doi.org/10.1175/MWR-D-19-0045.1.
Bei, N. F., and F. Q. Zhang, 2007: Impacts of initial condition errors on mesoscale predictability of heavy precipitation along the Mei-Yu front of China. Quart. J. Roy. Meteorol. Soc., 133, 83−99, https://doi.org/10.1002/qj.20.
Bei, N. F., and F. Q. Zhang, 2014: Mesoscale predictability of moist baroclinic waves: Variable and scale-dependent error growth. Adv. Atmos. Sci., 31, 995−1008, https://doi.org/10.1007/s00376-014-3191-7.
Bierdel, L., T. Selz, and G. C. Craig, 2017: Theoretical aspects of upscale error growth through the mesoscales: An analytical model. Quart. J. Roy. Meteorol. Soc., 143, 3048−3059, https://doi.org/10.1002/qj.3160.
Caron, J.-F., 2013: Mismatching perturbations at the lateral boundaries in limited-area ensemble forecasting: A case study. Mon. Wea. Rev., 141, 356−374, https://doi.org/10.1175/MWR-D-12-00051.1.
Chen, X., H. L. Yuan, and M. Xue, 2018: Spatial spread-skill relationship in terms of agreement scales for precipitation forecasts in a convection-allowing ensemble. Quart. J. Roy. Meteorol. Soc., 144, 85−98, https://doi.org/10.1002/qj.3186.
Chen, Y., Y. Chen, T. Chen, and H. He, 2016: Characteristics analysis of warm sector rainstorms over the middle lower reaches of the Yangtze River. Meteorol. Mon., 42, 724−731, https://doi.org/10.7519/j.issn.1000-0526.2016.06.008. (in Chinese with English abstract)
Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization (CLIRAD-SW) for atmospheric studies. NASA Tech. Memo, 10460.
Daniels, M. H., K. A. Lundquist, J. D. Mirocha, D. J. Wiersema, and F. K. Chow, 2016: A new vertical grid nesting capability in the Weather Research and Forecasting (WRF) model. Mon. Wea. Rev., 144, 3725−3747, https://doi.org/10.1175/MWR-D-16-0049.1.
Denis, B., J. Côté, and R. Laprise, 2002: Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using the Discrete Cosine Transform (DCT). Mon. Wea. Rev., 130, 1812−1829, https://doi.org/10.1175/1520-0493(2002)130<1812:SDOTDA>2.0.CO;2.
Ding, Y. H., 1993: Study on the Lasting Heavy Rainfalls over the Yangtze-Huaihe River Basin in 1991. China Meteorological Press. (in Chinese)
Done, J. M., G. C. Craig, S. L. Gray, P. A. Clark, and M. E. B. Gray, 2006: Mesoscale simulations of organized convection: Importance of convective equilibrium. Quart. J. Roy. Meteorol. Soc., 132, 737−756, https://doi.org/10.1256/qj.04.84.
Done, J. M., G. C. Craig, S. L. Gray, and P. A. Clark, 2012: Case-to-case variability of predictability of deep convection in a mesoscale model. Quart. J. Roy. Meteorol. Soc., 138, 638−648, https://doi.org/10.1002/qj.943.
Durran, D. R., and M. Gingrich, 2014: Atmospheric predictability: Why butterflies are not of practical importance. J. Atmos. Sci., 71, 2476−2488, https://doi.org/10.1175/JAS-D-14-0007.1.
Durran, D. R., and J. A. Weyn, 2016: Thunderstorms do not get butterflies. Bull. Amer. Meteorol. Soc., 97, 237−243, https://doi.org/10.1175/BAMS-D-15-00070.1.
Flack, D. L. A., R. S. Plant, S. L. Gray, H. W. Lean, C. Keil, and G. C. Craig, 2016: Characterisation of convective regimes over the British Isles. Quart. J. Roy. Meteorol. Soc., 142, 1541−1553, https://doi.org/10.1002/qj.2758.
Flack, D. L. A., S. L. Gray, R. S. Plant, H. W. Lean, and G. C. Craig, 2018: Convective-scale perturbation growth across the spectrum of convective regimes. Mon. Wea. Rev., 146, 387−405, https://doi.org/10.1175/MWR-D-17-0024.1.
Flora, M. L., C. K. Potvin, and L. J. Wicker, 2018: Practical predictability of supercells: Exploring ensemble forecast sensitivity to initial condition spread. Mon. Wea. Rev., 146, 2361−2379, https://doi.org/10.1175/MWR-D-17-0374.1.
Fu, S. M., F. Yu, D. H. Wang, and R. D. Xia, 2013: A comparison of two kinds of eastward-moving mesoscale vortices during the mei-yu period of 2010. Science China Earth Sciences, 56, 282−300, https://doi.org/10.1007/s11430-012-4420-5.
Gasperoni, N. A., M. Xue, R. D. Palmer, and J. D. Gao, 2013: Sensitivity of convective initiation prediction to near-surface moisture when assimilating radar refractivity: Impact tests using OSSEs. J. Atmos. Oceanic Technol., 30, 2281−2302, https://doi.org/10.1175/JTECH-D-12-00038.1.
Grell, G. A., and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693, https://doi.org/10.1029/2002GL015311.
Hagedorn, R., T. M. Hamill, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I: Two-meter temperatures. Mon. Wea. Rev., 136, 2608−2619, https://doi.org/10.1175/2007MWR2410.1.
Hagedorn, R., R. Buizza, T. M. Hamill, M. Leutbecher, and T. N. Palmer, 2012: Comparing TIGGE multimodel forecasts with reforecast-calibrated ECMWF ensemble forecasts. Quart. J. Roy. Meteorol. Soc., 138, 1814−1827, https://doi.org/10.1002/qj.1895.
Hohenegger, C., and C. Schär, 2007: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 4467−4478, https://doi.org/10.1175/2007JAS2143.1.
Hohenegger, C., D. Lüthi, and C. Schär, 2006: Predictability mysteries in cloud-resolving models. Mon. Wea. Rev., 134, 2095−2107, https://doi.org/10.1175/MWR3176.1.
Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). Journal of the Korean Meteorological Society, 42, 129−151.
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318−2341, https://doi.org/10.1175/MWR3199.1.
Johnson, A., and Coauthors, 2014: Multiscale characteristics and evolution of perturbations for warm season convection-allowing precipitation forecasts: Dependence on background flow and method of perturbation. Mon. Wea. Rev., 142, 1053−1073, https://doi.org/10.1175/MWR-D-13-00204.1.
Johnson, A., and X. G. Wang, 2016: A study of multiscale initial condition perturbation methods for convection-permitting ensemble forecasts. Mon. Wea. Rev., 144, 2579−2604, https://doi.org/10.1175/MWR-D-16-0056.1.
Johnson, A., X. G. Wang, J. R. Carley, L. J. Wicker, and C. Karstens, 2015: A comparison of multiscale GSI-based EnKF and 3DVar data assimilation using radar and conventional observations for midlatitude convective-scale precipitation forecasts. Mon. Wea. Rev., 143, 3087−3108, https://doi.org/10.1175/MWR-D-14-00345.1.
Judt, F., 2018: Insights into atmospheric predictability through global convection-permitting model simulations. J. Atmos. Sci., 75, 1477−1497, https://doi.org/10.1175/JAS-D-17-0343.1.
Keil, C., and G. C. Craig, 2011: Regime-dependent forecast uncertainty of convective precipitation. Meteorol. Z., 20, 145−151, https://doi.org/10.1127/0941-2948/2011/0219.
Keil, C., F. Heinlein, and G. C. Craig, 2014: The convective adjustment time-scale as indicator of predictability of convective precipitation. Quart. J. Roy. Meteorol. Soc., 140, 480−490, https://doi.org/10.1002/qj.2143.
Keil, C., F. Baur, K. Bachmann, S. Rasp, L. Schneider, and C. Barthlott, 2019: Relative contribution of soil moisture, boundary-layer and microphysical perturbations on convective predictability in different weather regimes. Quart. J. Roy. Meteorol. Soc., 145, 3102−3115, https://doi.org/10.1002/qj.3607.
Klasa, C., M. Arpagaus, A. Walser, and H. Wernli, 2019: On the time evolution of limited-area ensemble variance: Case studies with the convection-permitting ensemble COSMO-E. J. Atmos. Sci., 76, 11−26, https://doi.org/10.1175/JAS-D-18-0013.1.
Kühnlein, C., C. Keil, G. C. Craig, and C. Gebhardt, 2014: The impact of downscaled initial condition perturbations on convective-scale ensemble forecasts of precipitation. Quart. J. Roy. Meteorol. Soc., 140, 1552−1562, https://doi.org/10.1002/qj.2238.
Lean, H. W., P. A. Clark, M. Dixon, N. M. Roberts, A. Fitch, R. Forbes, and C. Halliwell, 2008: Characteristics of high-resolution versions of the met office unified model for forecasting convection over the United Kingdom. Mon. Wea. Rev., 136, 3408−3424, https://doi.org/10.1175/2008MWR2332.1.
Liu, J. Y., and Z.-M. Tan, 2009: Mesoscale predictability of mei-yu heavy rainfall. Adv. Atmos. Sci., 26, 438−450, https://doi.org/10.1007/s00376-009-0438-9.
Liu, J. Y., Z. M. Tan, and Y. Zhang, 2012: Study of the three types of torrential rains of different formation mechanism during the Meiyu period. Acta Meteorologica Sinica, 70, 452−466, https://doi.org/10.11676/qxxb2012.038.
Lorenz, E. N., 1969: Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci., 26, 636−646, https://doi.org/10.1175/1520-0469(1969)26<636:APARBN>2.0.CO;2.
Luo, Y. L., and Y. R. X. Chen, 2015: Investigation of the predictability and physical mechanisms of an extreme-rainfall-producing mesoscale convective system along the Meiyu front in East China: An ensemble approach. J. Geophys. Res.: Atmos., 120, 10593−10618, https://doi.org/10.1002/2015JD023584.
Luo, Y. L., Y. Gong, and D.-L. Zhang, 2014: Initiation and organizational modes of an extreme-rain-producing mesoscale convective system along a Mei-Yu Front in East China. Mon. Wea. Rev., 142, 203−221, https://doi.org/10.1175/MWR-D-13-00111.1.
Luo, Y. L., W. M. Qian, R. H. Zhang, and D.-L. Zhang, 2013: Gridded hourly precipitation analysis from high-density rain gauge network over the Yangtze-Huai Rivers Basin during the 2007 Mei-Yu season and comparison with CMORPH. Journal of Hydrometeorology, 14, 1243−1258, https://doi.org/10.1175/JHM-D-12-0133.1.
Madaus, L. E., and G. J. Hakim, 2017: Constraining ensemble forecasts of discrete convective initiation with surface observations. Mon. Wea. Rev., 145, 2597−2610, https://doi.org/10.1175/MWR-D-16-0395.1.
Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? The results of two years of real-time numerical weather prediction over the Pacific Northwest Bull. Amer. Meteorol. Soc., 83, 407−430, https://doi.org/10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.
Melhauser, C., and F. Q. Zhang, 2012: Practical and intrinsic predictability of severe and convective weather at the mesoscales. J. Atmos. Sci., 69, 3350−3371, https://doi.org/10.1175/JAS-D-11-0315.1.
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res.: Atmos., 102, 16663−16682, https://doi.org/10.1029/97JD00237.
Nielsen, E. R., and R. S. Schumacher, 2016: Using convection-allowing ensembles to understand the predictability of an extreme rainfall event. Mon. Wea. Rev., 144, 3651−3676, https://doi.org/10.1175/MWR-D-16-0083.1.
Nutter, P., D. Stensrud, and M. Xue, 2004: Effects of coarsely resolved and temporally interpolated lateral boundary conditions on the dispersion of limited-area ensemble forecasts. Mon. Wea. Rev., 132, 2358−2377, https://doi.org/10.1175/1520-0493(2004)132<2358:EOCRAT>2.0.CO;2.
Raynaud, L., and F. Bouttier, 2016: Comparison of initial perturbation methods for ensemble prediction at convective scale. Quart. J. Roy. Meteorol. Soc., 142, 854−866, https://doi.org/10.1002/qj.2686.
Selz, T., and G. C. Craig, 2015: Upscale error growth in a high-resolution simulation of a summertime weather event over Europe. Mon. Wea. Rev., 143, 813−827, https://doi.org/10.1175/MWR-D-14-00140.1.
Selz, T., L. Bierdel, and G. C. Craig, 2019: Estimation of the variability of mesoscale energy spectra with three years of COSMO-DE analyses. J. Atmos. Sci., 76, 627−637, https://doi.org/10.1175/JAS-D-18-0155.1.
Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note-475+ STR.
Snook, N., M. Xue, and Y. Jung, 2015: Multiscale EnKF assimilation of radar and conventional observations and ensemble forecasting for a tornadic mesoscale convective system. Mon. Wea. Rev., 143, 1035−1057, https://doi.org/10.1175/MWR-D-13-00262.1.
Sun, J. H., and F. Q. Zhang, 2012: Impacts of mountain-plains solenoid on diurnal variations of rainfalls along the Mei-Yu front over the East China plains. Mon. Wea. Rev., 140, 379−397, https://doi.org/10.1175/MWR-D-11-00041.1.
Sun, Y. Q., and F. Q. Zhang, 2016: Intrinsic versus Practical limits of atmospheric predictability and the significance of the butterfly effect. J. Atmos. Sci., 73, 1419−1438, https://doi.org/10.1175/JAS-D-15-0142.1.
Surcel, M., I. Zawadzki, and M. K. Yau, 2015: A study on the scale dependence of the predictability of precipitation patterns. J. Atmos. Sci., 72, 216−235, https://doi.org/10.1175/JAS-D-14-0071.1.
Surcel, M., I. Zawadzki, and M. K. Yau, 2016: The case-to-case variability of the predictability of precipitation by a storm-scale ensemble forecasting system. Mon. Wea. Rev., 144, 193−212, https://doi.org/10.1175/MWR-D-15-0232.1.
Surcel, M., I. Zawadzki, M. K. Yau, M. Xue, and F. Y. Kong, 2017: More on the scale dependence of the predictability of precipitation patterns: Extension to the 2009-13 CAPS spring experiment ensemble forecasts. Mon. Wea. Rev., 145, 3625−3646, https://doi.org/10.1175/MWR-D-16-0362.1.
Tan, Z.-M., F. Q. Zhang, R. Rotunno, and C. Snyder, 2004: Mesoscale predictability of moist baroclinic waves: Experiments with parameterized convection. J. Atmos. Sci., 61, 1794−1804, https://doi.org/10.1175/1520-0469(2004)061<1794:MPOMBW>2.0.CO;2.
Tennant, W., 2015: Improving initial condition perturbations for MOGREPS-UK. Quart. J. Roy. Meteorol. Soc., 141, 2324−2336, https://doi.org/10.1002/qj.2524.
Tong, M. J., and M. Xue, 2005: Ensemble Kalman filter assimilation of doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789−1807, https://doi.org/10.1175/MWR2898.1.
Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297−3319, https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2.
Vié, B., O. Nuissier, and V. Ducrocq, 2011: Cloud-resolving ensemble simulations of mediterranean heavy precipitating events: Uncertainty on initial conditions and lateral boundary conditions. Mon. Wea. Rev., 139, 403−423, https://doi.org/10.1175/2010MWR3487.1.
Walser, A., D. Lüthi, and C. Schär, 2004: Predictability of precipitation in a cloud-resolving model. Mon. Wea. Rev., 132, 560−577, https://doi.org/10.1175/1520-0493(2004)132<0560:POPIAC>2.0.CO;2.
Wang, S. Z., M. Xue, A. D. Schenkman, and J. Z. Min, 2013: An iterative ensemble square root filter and tests with simulated radar data for storm-scale data assimilation. Quart. J. Roy. Meteorol. Soc., 139, 1888−1903, https://doi.org/10.1002/qj.2077.
Wang, Y., M. Bellus, J.-F. Geleyn, X. L. Ma, W. H. Tian, and F. Weidle, 2014: A new method for generating initial condition perturbations in a regional ensemble prediction system: Blending. Mon. Wea. Rev., 142, 2043−2059, https://doi.org/10.1175/MWR-D-12-00354.1.
Weyn, J. A., and D. R. Durran, 2017: The dependence of the predictability of mesoscale convective systems on the horizontal scale and amplitude of initial errors in idealized simulations. J. Atmos. Sci., 74, 2191−2210, https://doi.org/10.1175/JAS-D-17-0006.1.
Weyn, J. A., and D. R. Durran, 2019: The scale dependence of initial-condition sensitivities in simulations of convective systems over the southeastern United States. Quart. J. Roy. Meteorol. Soc., 145, 57−74, https://doi.org/10.1002/qj.3367.
Whitaker, J. S., and T. M. Hamill, 2002: Ensemble Data Assimilation without Perturbed Observations. Mon. Wea. Rev., 130, 1913−1924, https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.
Wu, N. G., X. R. Zhuang, J. Z. Min, and Z. Y. Meng, 2020: Practical and intrinsic predictability of a warm-sector torrential rainfall event in the South China monsoon region. J. Geophys. Res.: Atmos., 125, e2019JD031313, https://doi.org/10.1029/2019JD031313.
Yussouf, N., and D. J. Stensrud, 2012: Comparison of single-parameter and multiparameter ensembles for assimilation of radar observations using the ensemble kalman filter. Mon. Wea. Rev., 140, 562−586, https://doi.org/10.1175/MWR-D-10-05074.1.
Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173−1185, https://doi.org/10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2.
Zhang, F., C. Snyder, and J. Z. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 1238−1253, https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.
Zhang, F. Q., A. M. Odins, and J. W. Nielsen-Gammon, 2006: Mesoscale predictability of an extreme warm-season precipitation event. Wea. Forecasting, 21, 149−166, https://doi.org/10.1175/WAF909.1.
Zhang, F. Q., N. F. Bei, R. Rotunno, C. Snyder, and C. C. Epifanio, 2007: Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J. Atmos. Sci., 64, 3579−3594, https://doi.org/10.1175/JAS4028.1.
Zhang, L., J. Z. Min, X. R. Zhuang, and R. S. Schumacher, 2019: General features of extreme rainfall events produced by MCSs over East China during 2016-17. Mon. Wea. Rev., 147, 2693−2714, https://doi.org/10.1175/MWR-D-18-0455.1.
Zhang, M., and D.-L. Zhang, 2012: Subkilometer simulation of a torrential-rain-producing mesoscale convective system in East China. Part I: Model verification and convective organization. Mon. Wea. Rev., 140, 184−201, https://doi.org/10.1175/MWR-D-11-00029.1.
Zhang, X. B., 2019: Multiscale characteristics of different-source perturbations and their interactions for convection-permitting ensemble forecasting during SCMREX. Mon. Wea. Rev., 147, 291−310, https://doi.org/10.1175/MWR-D-18-0218.1.
Zhang, Y. J., F. Q. Zhang, D. J. Stensrud, and Z. Y. Meng, 2016: Intrinsic predictability of the 20 May 2013 Tornadic thunderstorm event in Oklahoma at Storm Scales. Mon. Wea. Rev., 144, 1273−1298, https://doi.org/10.1175/MWR-D-15-0105.1.
Zhao, S. X., L. S. Zhang, and J. H. Sun, 2007: Study of heavy rainfall and related mesoscale systems causing severe flood in Huaihe River basin during the summer of 2007. Climatic and Environmental Research, 12, 713−727, https://doi.org/10.3969/j.issn.1006-9585.2007.06.002. (in Chinese with English abstract)
Zheng, Y. G., M. Xue, B. Li, J. Chen, and Z. Y. Tao, 2016: Spatial characteristics of extreme rainfall over China with hourly through 24-hour accumulation periods based on national-level hourly rain gauge data. Adv. Atmos. Sci., 33, 1218−1232, https://doi.org/10.1007/s00376-016-6128-5.
Zhu, X. Y., and J. J. Zhu, 2004: New generation weather radar network in China. Meteorological Science and Technology, 32, 255−258, https://doi.org/10.3969/j.issn.1671-6345.2004.04.012. (in Chinese with English abstract)
Zhuang, X. R., H. N. Zhu, J. Z. Min, L. Zhang, N. G. Wu, Z. P. Wu, and S. Q. Wang, 2019: Spatial predictability of heavy rainfall events in East China and the application of spatial-based methods of probabilistic forecasting. Atmosphere, 10, 490, https://doi.org/10.3390/atmos10090490.
Zhuang, X. R., N. G. Wu, J. Z. Min, and Y. Xu, 2020: Understanding the predictability within convection-allowing ensemble forecasts in East China: Meteorological sensitivity, forecast error growth and associated precipitation uncertainties across spatial scales. Atmosphere, 11, 234, https://doi.org/10.3390/atmos11030234.
Zimmer, M., G. C. Craig, C. Keil, and H. Wernli, 2011: Classification of precipitation events with a convective response timescale and their forecasting characteristics. Geophys. Res. Lett., 38, L05802, https://doi.org/10.1029/2010GL046199.