Barker, D., and Coauthors, 2012: The weather research and forecasting model's community variational/ensemble data assimilation system: WRFDA. Bull. Amer. Meteor. Soc., 93, 831−843,
Brewster, K. A., M. Hu, M. Xue, and J. Gao, 2005: Efficient assimilation of radar data at high resolution for short range numerical weather prediction. International Symp. on Nowcasting and Very Short Range Forecasting, Toulouse, France, World Weather Res. Prog., CDROM 3. 06.
Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 1013−1043,
Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 2010a: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description and single-observation experiments. Mon. Wea. Rev., 138, 1550−1566,
Buehner, M., P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 2010b: Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-Month experiments with real observations. Mon. Wea. Rev., 138, 1567−1586,
Buehner, M., R. McTaggart-Cowan, and S. Heilliette, 2017: An ensemble Kalman filter for numerical weather prediction based on variational data assimilation: VarEnKF. Mon. Wea. Rev., 145, 617−635,
Chen, Y. D., S. Guo, D. M. Meng, H. L. Wang, D. M. Xu, Y. B. Wang, and J. Wang, 2020: The impact of optimal selected historical forecasting samples on hybrid ensemble-variational data assimilation. Atmospheric Research, 242, 104980,
Courtier, P., J. N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367−1387,
Dowell, D. C., and L. J. Wicker, 2009: Additive noise for storm-scale ensemble data assimilation. J. Atmos. Oceanic Technol., 26, 911−927,
Dowell, D. C., F. Q. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982−2005,<1982:WATRIT>2.0.CO;2.
Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on storm-scale analyses. Mon. Wea. Rev., 139, 272−294,
Gao, J. D., and D. J. Stensrud, 2014: Some observing system simulation experiments with a hybrid 3DEnVAR system for storm-scale radar data assimilation. Mon. Wea. Rev., 142, 3326−3346,
Gao, J. D., C. H. Fu, D. J. Stensrud, and J. S. Kain, 2016: OSSEs for an ensemble 3DVAR data assimilation system with radar observations of convective storms. J. Atmos. Sci., 73, 2403−2426,
Gao, J. D., M. Xue, A. Shapiro, and K. K. Droegemeier, 1999: A variational method for the analysis of three-dimensional wind fields from two Doppler radars. Mon. Wea. Rev., 127, 2128−2142,<2128:AVMFTA>2.0.CO;2.
Gao, J. D., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457−469,<0457:ATVDAM>2.0.CO;2.
Gao, J. D., M. Xue, and D. J. Stensrud, 2013b: The development of a hybrid EnKF-3DVAR algorithm for storm-scale data assimilation. Advances in Meteorology, 512656,
Gao, J. D., and Coauthors, 2013a: A real-time weather-adaptive 3DVAR analysis system for severe weather detections and warnings. Wea. Forecasting, 28, 727−745,
Gao, S. B., J. Z. Min, L. M. Liu, and C. Y. Ren, 2019: The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation. Atmospheric Research, 229, 208−223,
Gao, S. B., J. Z. Sun, J. Z. Min, Y. Zhang, and Z. M. Ying, 2018: A Scheme to assimilate “no rain” observations from Doppler radar. Wea. Forecasting, 33, 71−88,
Ge, G. Q., J. D. Gao, K. Brewster, and M. Xue, 2010: Impacts of beam broadening and earth curvature on storm-scale 3D variational data assimilation of radial velocity with two Doppler radars. J. Atmos. Oceanic Technol., 27, 617−636,
Gustafsson, N., J. Bojarova, and O. Vignes, 2014: A hybrid variational ensemble data assimilation for the HIgh Resolution Limited Area Model (HIRLAM). Nonlinear Processes in Geophysics, 21, 303−323,
Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905−2919,<2905:ahekfv>;2.
Hamill, T. M., J. S. Whitaker, D. T. Kleist, M. Fiorino, and S. G. Benjamin, 2011: Predictions of 2010’s tropical cyclones using the GFS and ensemble-based data assimilation methods. Mon. Wea. Rev., 139, 3243−3247,
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,
Hu, M., M. Xue, J. D. Gao, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699−721,
Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP-A comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183−3203,
Lorenc, A. C., M. Jardak, T. Payne, N. E. Bowler, and M. A. Wlasak, 2017: Computing an ensemble of variational data assimilations using its mean and perturbations. Quart. J. Roy. Meteor. Soc., 143, 798−805,
Mittermaier, M., and N. Roberts, 2010: Intercomparison of spatial forecast verification methods: Identifying skillful spatial scales using the fractions skill score. Wea. Forecasting, 25, 343−354,
Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical- interpolation analysis system. Mon. Wea. Rev., 120, 1747−1763,<1747:TNMCSS>2.0.CO;2.
Putnam, B. J., M. Xue, Y. Jung, N. Snook, and G. F. Zhang, 2014: The analysis and prediction of microphysical states and polarimetric radar variables in a mesoscale convective system using double-moment microphysics, multinetwork radar data, and the ensemble Kalman filter. Mon. Wea. Rev., 142, 141−162,
Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78−97,
Schwartz, C. S., 2016: Improving large-domain convection-allowing forecasts with high-resolution analyses and ensemble data assimilation. Mon. Wea. Rev., 144, 1777−1803,
Schwartz, C. S., and Z. Q. Liu, 2014: Convection-permitting forecasts initialized with continuously cycling limited-area 3DVAR, ensemble kalman filter, and "hybrid" variational-ensemble data assimilation systems. Mon. Wea. Rev., 142, 716−738,
Shen, Y., P. Zhao, Y. Pan, and J. J. Yu, 2014: A high spatiotemporal gauge-satellite merged precipitation analysis over China. J. Geophys. Res., 119, 3063−3075,
Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125, 1870−1884,<1870:PODSMC>2.0.CO;2.
Stensrud, D. J., and J. D. Gao, 2010: Importance of horizontally inhomogeneous environmental initial conditions to ensemble storm-scale radar data assimilation and very short-range forecasts. Mon. Wea. Rev., 138, 1250−1272,
Sun, J. Z., 2005: Initialization and numerical forecasting of a supercell storm observed during STEPS. Mon. Wea. Rev., 133, 793−813,
Sun, J. Z., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642−1661,<1642:DAMRFD>2.0.CO;2.
Sun, J. Z., and N. A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55, 835−852,<0835:DAMRFD>2.0.CO;2.
Sun, J. Z., and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting, 16, 117−132,<0117:RTLLWA>2.0.CO;2.
Sun, J. Z., and H. L. Wang, 2013: Radar data assimilation with WRF 4D-Var. Part II: Comparison with 3D-Var for a squall line over the U.S. great plains. Mon. Wea. Rev., 141, 2245−2264,
Sun, J. Z., H. L. Wang, W. X. Tong, Y. Zhang, C. Y. Lin, and D. M. Xu, 2016: Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting. Mon. Wea. Rev., 144, 149−169,
Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095−5115,
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,
Wang, H., J. Sun, S. Fan, and X. Y. Huang, 2013a: Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events. J. Appl. Meteorol. Climatol., 52, 889−902,
Wang, H. L., J. Z. Sun, X. Zhang, X. Y. Huang, and T. Auligné, 2013b: Radar Data assimilation with WRF 4D-Var. Part I: System development and preliminary testing. Mon. Wea. Rev., 141, 2224−2244,
Wang, H. L., X. Y. Huang, J. Z. Sun, D. M. Xu, S. Y. Fan, J. Q. Zhong, and M. Zhang, 2013c: A comparison between the 3/4DVAR and hybrid ensemble-VAR techniques for radar data Assimilation. Proc. 36th AMS Conf. on Radar Meteorology, Breckenridge, Colorado
Wang, X. G., C. Snyder, and T. M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes. Mon. Wea. Rev., 135, 222−227,
Wang, X. G., D. M. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116−5131,
Wang, X. G., D. M. Barker, C. Snyder, and T. M. Hamill, 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132−5147,
Wang, X. G., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3Dvar-based ensemble-variational hybrid data assimilation for NCEP global forecast system: Single-resolution experiments. Mon. Wea. Rev., 141, 4098−4117,
Wang, Y. M., and X. G. Wang, 2017: Direct assimilation of radar reflectivity without tangent linear and adjoint of the nonlinear observation operator in the GSI-based EnVar system: Methodology and experiment with the 8 may 2003 Oklahoma City tornadic supercell. Mon. Wea. Rev., 145, 1447−1471,
Wang, Y. H., J. D. Gao, P. S. Skinner, K. Knopfmeier, T. Jones, G. Creager, P. L. Heiselman, L. J. Wicker, 2019: Test of a weather-adaptive dual-resolution hybrid warn-on-forecast analysis and forecast system for several severe weather events. Wea. Forecasting, 34, 1807−1827,
Wu, W. S., D. F. Parrish, E. Rogers, and Y. Lin, 2017: Regional ensemble-variational data assimilation using global ensemble forecasts. Wea. Forecasting, 32, 83−96,
Xiao, Q. N., and Coauthors, 2008: Doppler radar data assimilation in KMA’s operational forecasting. Bull. Amer. Meteor. Soc., 89, 39−44,
Xiao, Q. N., Y. H. Kuo, J. Z. Sun, W. C. Lee, E. Lim, Y. R. Guo, and D. M. Barker, 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteorol. Climatol., 44, 768−788,
Xiao, Q. N., Y. H. Kuo, J. Z. Sun, W. C. Lee, D. M. Barker, and E. Lim, 2007: An approach of radar reflectivity data assimilation and its assessment with the Inland QPF of Typhoon Rusa (2002) at landfall. J. Appl. Meteorol. Climatol., 46, 14−22,
Xie, Y., S. Koch, J. McGinley, S. Albers, P. E. Bieringer, M. Wolfson, and M. Chan, 2011: A space-time multiscale analysis system: A sequential variational analysis approach. Mon. Wea. Rev., 139, 1224−1240,
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,<1238:IOIEAO>2.0.CO;2.
Zhang, M., and F. Q. Zhang, 2012: E4DVar: Coupling an ensemble Kalman filter with four-dimensional variational data assimilation in a limited-area weather prediction model. Mon. Wea. Rev., 140, 587−600,