Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations

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• 作者：XueXing Qiu ; FuQing Zhang
• 关键词：EnKF ; Doppler radar data ; Local extreme rain ; Predictability
• 刊名：Science China Earth Sciences
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：59
• 期：3
• 页码：518-532
• 全文大小：10,504 KB
• 参考文献：Aksoy A, Dowell D C, Snyder C. 2009. A multi-case comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon Weather Rev, 137: 1805–1824
  Aksoy A, Dowell D C, Snyder C. 2010. A multi-case comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: Short-range ensemble forecasts. Mon Weather Rev, 138: 1273–1292
  Barker D M, Huang W, Guo Y R, Bourgeois A J, Xiao Q N. 2004. A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon Weather Rev, 132: 897–914CrossRef
  Bei N F, Zhang F Q. 2007. Mesoscale predictability of the torrential rainfall along the mei-yu front of China. Q J R Meteorol Soc, 133: 83–99CrossRef
  Dowell D C, Zhang F Q, Wicker L J, Snyder C, Crook A. 2004. Wind and thermodynamic retrievals in the 17 May 1981Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon Weather Rev, 132: 1982–2005CrossRef
  Hong S Y, Dudhia J, Chen S H. 2004. A revised approach to ice-microphysical processes for the bulk parameterization of cloud and precipitation. Mon Weather Rev, 132: 103–120CrossRef
  Lan W R, Zhu J, Xue M, Gao J D, Lei T. 2010a. Storm-scale ensemble Kalman filter data assimilation experiments using simulated Doppler radar data. Part I: Perfect model tests (in Chinese). Chin J Atmos Sci, 34: 640–652
  Lan W R, Zhu J, Xue M, Lei T, Gao J D. 2010b. Storm-scale ensemble Kalman filter data assimilation experiments using simulated Doppler radar data. Part II: Imperfect model tests (in Chinese). Chin J Atmos Sci, 34: 737–753
  Liu J Y, Tan Z M. 2009. Mesoscale predictability of Mei-yu heavy rainfall. Adv Atmos Sci, 26: 438–450CrossRef
  Lorenz. 1969. Atmospheric predictability as revealed by naturally occurring analogues. J Atmos Sci, 26: 636–646CrossRef
  Melhauser C, Zhang F Q. 2012. Practical and intrin-sic predictability of severe and convective weather at the mesoscales. J Atmos Sci, 69: 3350–3371CrossRef
  Meng Z Y, Zhang F Q. 2007. Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect- model experiments. Mon Weather Rev, 135: 1403–1423
  Meng Z Y, Zhang F Q. 2008a. Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon Weather Rev, 136: 522–540CrossRef
  Meng Z Y, Zhang F Q. 2008b. Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Performance over a warm-season month of Jun. 2003. Mon Weather Rev, 136: 3671–3682CrossRef
  Min J Z, Chen J, Wang S Z, Bao Y S. 2011. The tests of WRF-EnSRF data assimilation system and its applications in storm-scale events (in Chinese). Chin J Meteorol Sci, 31: 135–144
  Noh Y, Cheon W G, Hong S Y. 2003. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound-Layer Meteor, 107: 401–427CrossRef
  Schumacher R S, Johnson R H. 2005. Organization and environmental properties of extreme-rain-producing mesoscale convective systems. Mon Weather Rev, 133: 961–976CrossRef
  Schumacher R S, Johnson R H. 2006. Characteristics of U.S. extreme rain events during 1999–2003. Weather Forecast, 21: 69–85CrossRef
  Sippel J A, Braun S A, Shie C L. 2011. Environmental influences on the strength of Tropical Storm Debby (2006). J Atmos Sci, 68: 2557–2581CrossRef
  Sippel J A, Zhang F Q. 2008. A probabilistic analysis of the dynamics and predictability of tropical cyclogenesis. J Atmos Sci, 65: 3440–3459CrossRef
  Sippel J A, Zhang F Q. 2010. Factors affecting the predictability of Hurricane Humberto (2007). J Atmos Sci, 67: 1759–1778CrossRef
  Skamarock W C, Klemp J B, Dudhia J, Gill D O, Barker D M, Duda M G, Huang X Y, Wang W, Powers J R. 2008. A description of the Advanced Research WRF version 3. NCAR Tech Note 4751STR, 113
  Snyder C, Zhang F Q. 2003. Tests of an ensemble Kalman filter for convective-scale data assimilation. Mon Weather Rev, 131: 1663–1677CrossRef
  Tippett M K, Anderson J L, Bishop C H, Hamill T M, Whitaker J S. 2003. Ensemble square root filters. Mon Weather Rev, 131: 1485–1490CrossRef
  Tong M, Xue M. 2005. Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon Weather Rev, 133: 1789–1807CrossRef
  Xu X Y, Liu L P, Zheng G G. 2006. Numerical Experiment of Assimilation of Doppler Radar Data with an Ensemble Kalman Filter (in Chinese). Chin J Atmos Sci, 30: 712–728
  Xue M, Tong M, Droegemeier K K. 2006. An OSSE framework based on the ensemble square-root Kalman filter for evaluating impact of data from radar networks on thunderstorm analysis and forecast. J Atmos Ocean Technol, 23: 46–66CrossRef
  Weng Y H, Zhang F. 2012. Assimilating Airborne Doppler Radar Observations with an Ensemble Kalman Filter for Convection-Permitting Hurricane Initialization and Prediction: Katrina (2005). Mon Weather Rev, 140: 841–859CrossRef
  Whitaker J S, Hamill T M. 2002. Ensemble data assimilation without perturbed observations. Mon Weather Rev, 130: 1913–1924CrossRef
  Zhang F Q, Snyder C, Rotunno R. 2004. Tests of an ensemble Kalman filter for convective-scale data assimilation: Impact of initial estimate and observations. Mon Weather Rev, 132: 1238–1253CrossRef
  Zhang F Q, Odins A M, Nielsen-Gammon J W. 2006a. Mesoscale predictability of an extreme warm-season precipitation event. Weather Forecast, 21: 149–166CrossRef
  Zhang F Q, Meng Z Y. Aksoy A. 2006b. Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect-model experiments. Mon Weather Rev, 134: 722–736
  Zhang F Q, Weng Y H, Sippel A J, Meng Z Y. 2009a. Convection- permitting hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter: Humberto (2007). Mon Weather Rev, 137: 2105–2125CrossRef
  Zhang F Q, Sippel J A. 2009b. Effects of moist convection on hurricane predictability. J Atmos Sci, 66: 1944–1961CrossRef
  Zhang F Q, Weng Y H. 2015. Modernizing the prediction of hurricane intensity and associated hazards: A five-year real-time forecast experiment concluded by superstorm Sandy (2012). Bull Amer Meteorol Soc, 96: 25–33CrossRef
  Zhang X L, Yu R, Du M Y. 2014. Evolution pattern of short-time intense precipitation-producing systems associated with Meiyu front (in Chinese). Chin J Atmos Sci, 38: 770–781
  Zhu B L, Lin W T, Zhang Y. 2009. Impact s of initial perturbations on prediction of a heavy rain in South China (in Chinese). Chin J Atmos Sci, 33: 1333–1347
  
• 作者单位：XueXing Qiu (1) (2)
  FuQing Zhang (2)
  
  1. Anhui Meteorological Observatory, Hefei, 230031, China
  2. Department of Meteorology, Pennsylvania State University, University Park, PA, 16802, USA
  
• 刊物主题：Earth Sciences, general;
• 出版者：Springer Berlin Heidelberg
• ISSN：1869-1897

文摘

Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.