Acknowledgement
이 논문은 2020학년도 경북대학교 국립대학육성사업 지원비에 의하여 연구되었습니다.
References
- Abhilash, S., A. K. Sahai, K. Mohankumar, J. P. George, and S. Das, 2012: Assimilation of Doppler weather radar radial velocity and reflectivity observations in WRF-3DVAR system for short-range forecasting of convective storms. Pure Appl. Geophys., 169, 2047-2070, doi:10.1007/s00024-012-0462-z.
- Albers S. C., J. A. McGinley, D. L. Birkenheuer, and J. R. Smart, 1996: The local analysis and prediction system (LAPS): Analyses of clouds, precipitation, and temperature. Wea. Forecasting, 11, 273-287, doi:10.1175/1520-0434(1996)011<0273:TLAAPS>2.0.CO;2.
- Bae, J.-H., and K.-H. Min, 2022: Forecast characteristics of radar data assimilation based on the scales of precipitation systems. Remote Sens, 14, 605, doi:10.3390/rs14030605.
- Do, P.-N., K.-S. Chung, P.-L. Lin, C.-Y. Ke, and S. M. Ellis, 2022: Assimilating retrieved water vapor and radar data from NCAR S-PolKa: Performance and validation using real cases. Mon. Wea. Rev., 150, 1177-1199, doi:10.1175/MWR-D-21-0292.1.
- 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, doi:10.1175/2010MWR3438.1.
- Gao, J., and D. J. Stensrud, 2012: Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification. J. Atmos. Sci., 69, 1054-1065, doi:10.1175/JAS-D-11-0162.1.
- Gao, J., C. 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, doi:10.1175/JAS-D-15-0311.1.
- Gilmore, M. S., and L. J. Wicker, 1998: The influence of mid-tropospheric dryness on supercell morphology and evolution. Mon. Wea. Rev., 126, 943-958, doi:10.1175/1520-0493(1998)126<0943:TIOMDO>2.0.CO;2.
- Hastuti, M. I., K.-H. Min, and J.-W. Lee, 2023: Improving radar data assimilation forecast using advanced remote sensing data. Remote Sens, 15, 2760, doi:10.3390/rs15112760.
- 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, doi:10.1175/MWR3199.1.
- Hu, M., M. Xue, J. 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, doi:10.1175/MWR3093.1.
- Jimenez, P. A., J. Dudhia, J. F. Gonzalez-Rouco, J. Navarro, J. P. Montavez, and E. Garcia-Bustamante, 2012: A revised scheme for the WRF surface layer formulation. Mon. Wea. Rev., 140, 898-918, doi:10.1175/MWR-D-11-00056.1.
- Johnson, M., Y. Jung, D. Dawson, T. Supinie, M. Xue, J. Park, and Y.-H. Lee, 2018: Evaluation of unified model microphysics in high-resolution NWP simulations using polarimetric radar observations. Adv. Atmos. Sci., 35, 771-784, doi:10.1007/s00376-017-7177-0.
- 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. https://doi.org/10.1175/2007MWR2083.1
- Jung, Y., 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, doi:10.1175/2007MWR2288.1.
- KMA, 2021: Abnormal Climate Report 2020 Korea Meteorological Administration, 212 pp (in Korean).
- Lahoz, B. K. W., and R. Menard, 2010: Data assimilation. Springer-Verlag Berlin Heidelberg. 718 pp.
- Law, K., A. Stuart, and L. Zygalakis, 2015: Data assimilation. Cham, Switzerland, Springer, 242 pp.
- Lee, J.-W., K.-H. Min, Y.-H. Lee, and G. Lee, 2020: X-Net-Based radar data assimilation study over the Seoul metropolitan area. Remote Sens., 12, 893, doi:10.3390/rs12050893.
- Lee, J.-W., K.-H. Min, and K.-S. S. Lim, 2022: Comparing 3DVAR and hybrid radar data assimilation methods for heavy rain forecast. Atmos. Res., 270, 106062 doi:10.1016/j.atmosres.2022.106062.
- Lee, Y.-H., and K.-H. Min, 2019: High-resolution modeling study of an isolated convective storm over Seoul Metropolitan area. Meteor. Atmos. Phys., 131, 1549-1564, doi:10.1007/s00703-019-0657-2.
- Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587-1612, doi:10.1175/2009MWR2968.1.
- Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Appl. Meteor. Climatol., 22, 1065-1092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
- Liu, C., H. Li, M. Xue, Y. Jung, J. Park, L. Chen, R., Kong, and C.-C. Tong, 2022: Use of a reflectivity operator based on double-moment Thompson microphysics for direct assimilation of radar reflectivity in GSI-based hybrid En3DVar. Mon. Wea. Rev., 150, 907-926, doi:10.1175/MWR-D-21-0040.1.
- Min, K.-H., S. Choo, D. Lee, and G. Lee, 2015: Evaluation of WRF cloud microphysics schemes using radar observations. Wea. Forecasting, 30, 1571-1589, doi:10.1175/WAF-D-14-00095.1.
- Parrish, D. F., and J. C. Derber, 1992: The National Meteoro-logical Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763, doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.
- Skamarock, W. C., and Coauthors, 2019: A description of the advanced research WRF model version 4.1, Tech. Rep, 145 pp.
- Smith, Jr. P. L., 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. Climatol., 14, 1156-1165, doi:10.1175/1520-0450(1975)014<1156:RRFCIN>2.0.CO;2.
- Souto, M. J., C. F. Balseiro, V. Perez-Munuzuri, M. Xue, and K. Brewster, 2003: Impact of cloud analysis on numerical weather prediction in the galician region of spain. J. Appl. Meteor. Climatol., 42, 129-140, doi:10.1175/1520-0450(2003)042<0129:IOCAON>2.0.CO;2.
- Sugimoto, S., N. A. Crook, J. Sun, Q. Xiao, and D. M. Barker, 2009: An examination of WRF 3DVAR radar data assimilation on its capability in retrieving unobserved variables and forecasting precipitation through observing system simulation experiments. Mon. Wea. Rev., 137, 4011-4029, doi:10.1175/2009MWR2839.1.
- 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, doi:10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2.
- Sun, J., and H. 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, doi:10.1175/MWR-D-12-00169.1.
- Tewari, M., and Coauthors, 2004: Implementation and verification of the unified Noah land-surface model in the WRF Model. Amer. Meteor. Soc., 14 pp.
- Tsai, C.-C., and K.-S. Chung, 2020: Sensitivities of quantitative precipitation forecasts for Typhoon Soudelor (2015) near landfall to polarimetric radar data assimilation. Remote Sens., 12, 3711, doi:10.3390/rs12223711.
- Wang, H., J. Sun, S. Fan, and X.-Y. Huang, 2013: Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events. J. Appl. Meteor. Climatol., 52, 889-902, doi:10.1175/JAMC-D-12-0120.1.
- Xiao Q., Y.-H. Kuo, J. 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. Meteor. Climatol., 46, 14-22, doi:10.1175/JAM2439.1.
- Xiao Q., and J. Sun, 2007: Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135, 3381-3404, doi:10.1175/MWR3471.1.
- Zheng, Y., K. Alapaty, J. A. Herwehe, A. D. Del Genio, and D. Niyogi, 2016: Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain-Fritsch scheme. Mon. Wea. Rev., 144, 833-860, doi:10.1175/MWR-D-15-0005.1.