Evaluation of Unified Model Microphysics in High-resolution NWP Simulations Using Polarimetric Radar Observations
The UK Met Office Unified Model (UM) is employed by many weather forecasting agencies around the globe. This model is designed to run across spatial and time scales and known to produce skillful predictions for large-scale weather systems. However, the model has only recently begun running operationally at horizontal grid spacings of ∼1.5 km [e.g., at the UK Met Office and the Korea Meteorological Administration (KMA)]. As its microphysics scheme was originally designed and tuned for large-scale precipitation systems, we investigate the performance of UM microphysics to determine potential inherent biases or weaknesses. Two rainfall cases from the KMA forecasting system are considered in this study: a Changma (quasi-stationary) front, and Typhoon Sanba (2012). The UM output is compared to polarimetric radar observations in terms of simulated polarimetric radar variables. Results show that the UM generally underpredicts median reflectivity in stratiform rain, producing high reflectivity cores and precipitation gaps between them. This is partially due to the diagnostic rain intercept parameter formulation used in the one-moment microphysics scheme. Model drop size is generally both underand overpredicted compared to observations. UM frozen hydrometeors favor generic ice (crystals and snow) rather than graupel, which is reasonable for Changma and typhoon cases. The model performed best with the typhoon case in terms of simulated precipitation coverage.
Key wordsUnified Model microphysics polarimetric radar radar simulator numerical weather prediction
全球许多天气预报机构都在使用英国气象局天气与气候统一模式(Met Office Unified Model, 简称MetUM), 它以有效预测大尺度天气系统而闻名, 能够模拟各种时空尺度事件. 然而最近才有预报机构(如: 英国气象局, 韩国气象局)开展该模式在1.5公里水平网格中的业务运行工作. MetUM的微物理方案最早是针对大尺度降水系统设计和调试的, 为评估其在模拟对流性降水过程中固有的潜在偏差或缺点, 本文使用韩国气象局预报系统提供的一次韩国梅雨锋降水和台风“三巴”(2012)降水资料开展数值试验. 通过比较偏振雷达观测资料和MetUM模拟输出的一系列偏振变量值, 发现: MetUM常常低估层状云降水平均反射率, 模拟产生反射率虚假高值中心和破碎的雨带. 这一问题应部分归因于单参数微物理方案中雨粒子截距这一变量采用的诊断公式. 本研究的其他发现有: 模式估计的水滴大小通常都比观测值偏大或偏小; 模式更易模拟出冰晶和雪等常规冰粒子, 较少生成霰粒子, 这对于梅雨锋和台风降水过程模拟是合理的; 从模拟的降水范围看, MetUM在台风个例中效果最好.
摘 要天气与气候统一模式(MetUM) 微物理方案 偏振雷达 雷达模拟器 数值天气预报
This research was supported by a research grant of “Development of a Polarimetric Radar Data Simulator for Local Forecasting Model (II)” by the KMA. Further support was provided by a NOAA Warn-on-Forecast grant (Grant No. NA16OAR4320115) and a National Science Foundation grant (Grant No. AGS-1261776). We also thank the two anonymous reviewers for their help in improving the quality of this manuscript.
- Cintineo, R., J. A. Otkin, M. Xue, and F. Y. Kong, 2014: Evaluating the performance of planetary boundary layer and cloud microphysical parameterization schemes in convectionpermitting ensemble forecasts using synthetic GOES-13 satellite observations. Mon. Wea. Rev., 142, 163–182, https://doi.org/10.1175/MWR-D-13-00143.1. CrossRefGoogle Scholar
- Dawson, D. T., M. Xue, J. A. Milbrandt, and M. K. Yau, 2010: Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Mon. Wea. Rev., 138, 1152–1171, https://doi.org/10.1175/2009MWR2956.1. CrossRefGoogle Scholar
- Dawson, D. T., L. J. Wicker, E. R. Mansell, Y. Jung, and M. Xue, 2013: Low-level polarimetric radar signatures in EnKF analyses and forecasts of the May 8, 2003 Oklahoma City tornadic supercell: Impact of multimoment microphysics and comparisons with observation. Advances in Meteorology, 2013, 818394, https://doi.org/10.1155/2013/818394. CrossRefGoogle Scholar
- Goldenberg, S. B., S. G. Gopalakrishnan, V. Tallapragada, T. Quirino, F. Marks Jr., S. Trahan, X. J. Zhang, and R. Atlas, 2015: The 2012 triply nested, high-resolution operational version of the hurricane weather research and forecasting model (HWRF): Track and intensity forecast verifications. Wea. Forecasting, 30, 710–729, https://doi.org/10.1175/WAF-D-14-00098.1. CrossRefGoogle Scholar
- Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). Korean Meteorological Society, 42, 129–151.Google Scholar
- Jung, Y., G. F. Zhang, and M. Xue, 2008: 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. Google Scholar
- Jung, Y., M. Xue, and M. J. Tong, 2012: Ensemble Kalman filter analyses of the 29–30 May 2004 Oklahoma tornadic thunderstorm using one-and two-moment bulk microphysics schemes, with verification against polarimetric radar data. Mon. Wea. Rev., 140, 1457–1475, https://doi.org/10.1175/MWR-D-11-00032.1. CrossRefGoogle Scholar
- Kim, D.-J., 2015: Center report from KMA-forecasting system operation & research. WGNE-30.Google Scholar
- Morrison, H., J. A. Milbrandt, G. H. Bryan, K. Ikeda, S. A. Tessendorf, and G. Thompson, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part II: Case study comparisons with observations and other schemes. J. Atmos. Sci., 72, 312–339, https://doi.org/10.1175/JAS-D-14-0066.1. Google Scholar
- Nicol, J. C., R. J. Hogan, T. H. M. Stein, K. E. Hanley, P. A. Clark, C. E. Halliwell, H. W. Lean, and R. S. Plant, 2015: Convective updraught evaluation in high-resolution NWP simulations using single-Doppler radar measurements. Quart. J. Ror. Meteor. Soc., 141, 3177–3189, https://doi.org/10.1002/qj.2602. CrossRefGoogle Scholar
- Pan, Y. J., M. Xue, and G. Q. Ge, 2016: Incorporating diagnosed intercept parameters and the graupel category within the ARPS cloud analysis system for the initialization of double-moment microphysics: Testing with a squall line over South China. Mon. Wea. Rev., 144, 371–392, https://doi.org/10.1175/MWR-D-15-0008.1. CrossRefGoogle Scholar
- Park, J.-S., Y. H. Lee, M. Suk, K. Nam, Y. Jung, and J. Ko, 2015a: Evaluation of UM microphysics using dual-polarised radar simulator. Proceedings of the 37th Conference on Radar Meteorology, Norman, OK, American Meteorological Society.Google Scholar
- Putnam, B. J., M. Xue, Y. Jung, G. F. Zhang, and F. Y. Kong, 2017: Simulation of polarimetric radar variables from 2013 CAPS spring experiment storm-scale ensemble forecasts and evaluation of microphysics schemes. Mon. Wea. Rev., 145, 49–73, https://doi.org/10.1175/MWR-D-15-0415.1. CrossRefGoogle Scholar
- Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci., 40, 1185–1206, https://doi.org/10.1175/1520-0469 (1983)040<1185:TMAMSA>2.0.CO;2.Google Scholar
- Straka, J. M., M. S. Gilmore, K. M. Kanak, and E. N. Rasmussen, 2005: A comparison of the conservation of number concentration for the continuous collection and vapor diffusion growth equations using one-and two-moment schemes. J. Appl. Meteor., 44, 1844–1849, https://doi.org/10.1175/JAM2314.1. CrossRefGoogle Scholar
- Tao, W.-K., and J. Simpson, 1993: Goddard cumulus ensemble model. Part I: Model description. TAO, 4, 35–72, https://doi. org/10.3319/TAO.1922.214.171.124(A).Google Scholar
- Van Weverberg, K., N. P. M. Van Lipzig, and L. Delobbe, 2011: The impact of size distribution assumptions in a bulk one-moment microphysics scheme on simulated surface precipitation and storm dynamics during a low-topped supercell case in Belgium. Mon. Wea. Rev., 139, 1131–1147, https://doi.org/10.1175/2010MWR3481.1. CrossRefGoogle Scholar
- Wainwright, C. E., D. T. Dawson II, M. Xue, and G. F. Zhang, 2014: Diagnosing the intercept parameters of the exponential drop size distributions in a single-moment microphysics scheme and impact on supercell storm simulations. J. Appl. Meteor. Climatol., 53, 2072–2090, https://doi.org/10.1175/JAMC-D-13-0251.1. CrossRefGoogle Scholar
- Wilkinson, J. M., A. N. F. Porson, F. J. Bornemann, M. Weeks, P. R. Field, and A. P. Lock, 2013: Improved microphysical parametrization of drizzle and fog for operational forecasting using the Met Office Unified Model. Quart. J. Roy. Meteor. Soc., 139, 488–500, https://doi.org/10.1002/qj.1975. CrossRefGoogle Scholar
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