參考文獻 |
Barnes, S., 1964: A technique for maximizing details in numerical map analysis. J. Appl. Meteor., 3, 395–409.
Chung, K.-S., I. Zawadzki, M. K. Yau, and L. Fillion, 2009: Short term forecasting of a midlatitude convective storm by the assimilation of single–Doppler radar observations. Mon. Wea. Rev., 137, 4115–4135.
Crook, N. A., 1994: Numerical simulations initialized with radarderived winds. Part I: Simulated data experiments. Mon. Wea. Rev., 122, 1189–1203.
——, and J. D. Tuttle, 1994: Numerical simulations initialized with radar-derived winds. Part II: Forecasts of three gust-front cases. Mon. Wea. Rev., 122, 1204–1217.
——, and J. Sun, 2002: Assimilating radar, surface, and profiler data for the Sydney 2000 forecast demonstration project, J. Atmos. Oceanic. Technol., 19, 888-898.
Dudhia, J., 1989: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model, J. Atmos. Sci., 46, 3077–3107.
Fabry, F. and J. Sun, 2010: For How Long Should What Data Be Assimilated for the Mesoscale Forecasting of Convection and Why? Part I: On the Propagation of Initial Condition Errors and Their Implications for Data Assimilation. Mon. Wea. Rev., 138, 242-255.
Gal-Chen, Tzvi, 1978: A Method for the Initialization of the Anelastic Equations: Implications for Matching Models with Observations. Mon. Wea. Rev., 106, 587–606.
Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation, Mon. Wea. Rev., 132, 103–120.
Hu, M., M. Xue, J. Gao, and K. Brewster, 2006a: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675–698.
——,——, ——, and ——, 2006b: 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.
Kessler, E., 1969: On the Distribution and Continuity of Water Substancein Atmospheric Circulation. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.
Klemp, Joseph B., Robert B. Wilhelmson, 1978: The Simulation of Three- Dimensional Convective Storm Dynamics. J. Atmos. Sci., 35, 1070–1096.
Jung, Y., G. Zhang, and M. Xue, 2008a: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables. Mon. Wea. Rev., 136, 2228–2245.
——, M. Xue, G. Zhang, and J. M. Straka, 2008b: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part II: Impact of polarimetric data on storm analysis. Mon. Wea. Rev., 136, 2246–2260.
Li, Y., X. Wang and M. Xue, 2012: Assimilation of radar radial velocity data with the WRF ensemble-3DVAR hybrid system for the prediction of hurricane Ike (2008) . Mon. Wea. Rev. , in press.
Lin, Ying, Peter S. Ray, Kenneth W. Johnson, 1993: Initialization of a Modeled Convective Storm Using Doppler Radar–derived Fields. Mon. Wea. Rev., 121, 2757–2775.
Miller, M. J., and R. P. Pearce, 1974: A three-dimentional primitive equation model of cumulonimbus convection. Quart. J. Roy. Meteor. Soc., 100, 133–154.
Pan, X., X. Tian, X. Li, Z. Xie, A. Shao, and C. Lu (2012), Assimilating Doppler radar radial velocity and reflectivity observations in the weather research and forecasting model by a proper orthogonal-decomposition-based ensemble, three-dimensional variational assimilation method, J. Geophys. Res., 117, D17113, doi:10.1029/2012JD017684.
Rogers, E., T. L. Black, D. G. Deaver, G. J. DiMego, Q. Zhao, M. Baldwin, N. W. Junker, and Y. Lin, 1996: Changes to the operational ‘‘early’’Eta analysis/forecast systemat theNational Centers for Environmental Prediction. Wea. Forecasting, 11, 391–412.
Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570–575.
Smith, P. L., Jr., C. G. Myers, and H. D. Orville, 1975: Radar reflectivity factor calculations in numerical cloud models using bulk parameterization of precipitation processes. J. Appl. Meteor., 14, 1156–1165.
Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663–1677.
Sun, J., 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.
——, and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using WSR-88D data, Wea. Forecasting, 16, 117-132.
——, 2005: Initialization and Numerical Forecasting of a Supercell Storm Observed during STEPS, Mon. Wea. Rev., 133, 793–813.
——, and Y. Zhang, 2008: Analysis and Prediction of a Squall Line Observed during IHOP Using Multiple WSR-88D Observations, Mon. Wea. Rev., 136, 2364–2388.
——, M. Chen and Y. Wang, 2010 : Frequent-updating Analysis System Based on Radar, Surface, and Mesoscale Model Data for the Beijing 2008 Forecast Demonstration Project, Wea. Forecasting, 25, 1715-1735.
Sun J, Wang H. 2012: 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. doi:10.1175/MWR-D-12-00169.1, in press.
Tai, S.-L., Y.-C. Liou, J. Sun, S.-F. Chang, and M.-C. Kuo, 2011: Precipitation forecasting using doppler radar data, a cloud model with adjoint, and the weather research and forecasting model: real case studies during sowmex in taiwan. Wea. Forecasting, 26, 975–992.
Takuya K, Kuroda T, Seko H, Saito K. 2011. A cloud-resolving 4DVAR assimilation experiment for a local heavy rainfall event in the Tokyo metropolitan area. Mon. Wea. Rev., 139: 1911–1931.
Tong, M., 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.
Tripoli, G. J., and W. R. Cotton, 1981: The use of ice-liquid water potential temperature as a thermodynamic variable in deep atmospheric models. Mon. Wea. Rev., 109, 1094–1102.
Wang H, Sun J, Zhang X, Huang X, Auligne T. 2013. Radar data assimilation with WRF 4D-Var: Part I. System development and preliminary testing. Mon. Wea. Rev. doi:10.1175/MWR-D-12- 00168.1, in press.
Weisman, M. L. and J.B. Klemp, 1982: The Dependence of Numerically Simulated Convective Storms on Vertical Wind Shear and Buoyancy. Mon. Wea. Rev., 110, 504-520.
——, and R. Rotunno, 2004: “Theory for Strong Long-Lived Squall Lines” Revisited. J. Atmos. Sci., 61, 361-382.
Weygandt, S. S., A. Shapiro, and K. K. Droegemeier, 2002: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part II: Thermodynamic retrieval and numerical prediction. Mon. Wea. Rev., 130, 454–476.
Wu, Bing, Johannes Verlinde, Juanzhen Sun, 2000: Dynamical and Microphysical Retrievals from Doppler Radar Observations of a Deep Convective Cloud. J. Atmos. Sci., 57, 262–283.
Xiao, Q., and J. Sun, 2007: Multiple radar data assimilation and short-range QPF of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135, 3381–3404.
——, Y.-H. Kuo, J. Sun, W.-C. Lee, E. Lim, Y. 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. Meteor., 44, 768–788.
Xue, M., M. Tong, and K. K. Droegemeier, 2006: An OSSE framework based on the ensemble square rootKalman filter for evaluating impact of data fromradar networks on thunderstorm analysis and forecast. J. Atmos. Oceanic Technol., 23, 46–66.
Zhao, Q., J. Cook, Q. Xu, and P. R. Harasti, 2006: Using radar wind observations to improve mesoscale numerical weather prediction. Wea. Forecasting, 21, 502–522. |