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

AbstractThe quantitative precipitation forecast (QPF) capability of the Variational Doppler Radar Analysis System (VDRAS) is investigated in the Taiwan area, where the complex topography and surrounding oceans pose great challenges to accurate rainfall prediction. Two real cases observed during intensive operation periods (IOPs) 4 and 8 of the 2008 Southwest Monsoon Experiment (SoWMEX) are selected for this study. Experiments are first carried out to explore the sensitivity of the retrieved fields and model forecasts with respect to different background fields. All results after assimilation of the Doppler radar data indicate that the principal kinematic and thermodynamic features recovered by the VDRAS four-dimensional variational data assimilation (4DVAR) technique are rather reasonable. Starting from a background field generated by blending ground-based in situ measurements (radiosonde and surface mesonet station) and reanalysis data over the oceans, VDRAS is capable of capturing the evolution of the m...

[1]  Mingjing Tong,et al.  Ensemble kalman filter assimilation of doppler radar data with a compressible nonhydrostatic model : OSS experiments , 2005 .

[2]  Juanzhen Sun,et al.  Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002 , 2007 .

[3]  Ming-Jen Yang,et al.  Ensemble prediction of rainfall during the 2000–2002 Mei‐Yu seasons: Evaluation over the Taiwan area , 2004 .

[4]  E. Kessler On the distribution and continuity of water substance in atmospheric circulations , 1969 .

[5]  D. Deaven,et al.  Changes to the Operational ''Early'' Eta Analysis / Forecast System at the National Centers for Environmental Prediction , 1996 .

[6]  J. Schaefer The critical success index as an indicator of Warning skill , 1990 .

[7]  Jidong Gao,et al.  A comparison of the radar ray path equations and approximations for use in radar data assimilation , 2006 .

[8]  Qin Xu,et al.  Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction , 2006 .

[9]  Andrew Crook Numerical Simulations Initialized with Radar-Derived Winds. Part I: Simulated Data Experiments , 1994 .

[10]  Alan Shapiro,et al.  Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm , 2002 .

[11]  Luc Fillion,et al.  Short-Term Forecasting of a Midlatitude Convective Storm by the Assimilation of Single-Doppler Radar Observations , 2009 .

[12]  Ming-Jen Yang,et al.  A modeling study of Typhoon Nari (2001) at landfall: 2. Structural changes and terrain-induced asymmetries , 2011 .

[13]  C. Snyder,et al.  Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter , 2003 .

[14]  Juanzhen Sun,et al.  Dynamical and Microphysical Retrieval from Doppler Radar Observations Using a Cloud Model and Its Adjoint. Part I: Model Development and Simulated Data Experiments. , 1997 .

[15]  K. Brewster,et al.  Phase-Correcting Data Assimilation and Application to Storm-Scale Numerical Weather Prediction. Part I: Method Description and Simulation Testing , 2003 .

[16]  Jing-Shan Hong,et al.  Evaluation of the High-Resolution Model Forecasts over the Taiwan Area during GIMEX , 2003 .

[17]  F. Kong A real-time storm-scale ensemble forecast system: 2009 Spring Experiment , 2009 .

[18]  M. Xue,et al.  A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles , 2009 .

[19]  Alan Yates,et al.  THE PENTAGON SHIELD FIELD PROGRAM Toward Critical Infrastructure Protection , 2007 .

[20]  M. Xue,et al.  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 , 2006 .

[21]  Guifu Zhang,et al.  Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables , 2008 .

[22]  Tadashi Tsuyuki,et al.  Assimilation of Precipitation Data to the JMA Mesoscale Model with a Four-dimensional Variational Method and its Impact on Precipitation Forecasts , 2005 .

[23]  Ying-Hwa Kuo,et al.  Assimilation of Doppler Radar Observations with a Regional 3DVAR System: Impact of Doppler Velocities on Forecasts of a Heavy Rainfall Case , 2005 .

[24]  Peter S. Ray,et al.  Initialization of a modeled convective storm using Doppler radar-derived fields , 1992 .

[25]  Ming-Jen Yang,et al.  A Modeling Study of Typhoon Nari (2001) at Landfall. Part I: Topographic Effects , 2008 .

[26]  John D. Tuttle,et al.  Numerical Simulations Initialized with Radar-Derived Winds. Pail II: Forecasts of Three Gust-Front Cases , 1994 .

[27]  Juanzhen Sun,et al.  Real-Time Low-Level Wind and Temperature Analysis Using Single WSR-88D Data , 2001 .

[28]  Mingjing Tong,et al.  An OSSE Framework Based on the Ensemble Square Root Kalman Filter for Evaluating the Impact of Data from Radar Networks on Thunderstorm Analysis and Forecasting , 2006 .

[29]  Juanzhen Sun,et al.  A Frequent-Updating Analysis System Based on Radar, Surface, and Mesoscale Model Data for the Beijing 2008 Forecast Demonstration Project , 2010 .

[30]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[31]  Juanzhen Sun,et al.  Initialization and Numerical Forecasting of a Supercell Storm Observed during STEPS , 2005 .

[32]  Kazuo Saito,et al.  The Operational JMA Nonhydrostatic Mesoscale Model , 2006 .

[33]  S. J. Weiss,et al.  Assessing Advances in the Assimilation of Radar Data and Other Mesoscale Observations within a Collaborative Forecasting-Research Environment , 2010 .

[34]  Jerry M. Straka,et al.  Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part II: Impact of Polarimetric Data on Storm Analysis , 2008 .

[35]  Juanzhen Sun,et al.  Assimilating Radar, Surface, and Profiler Data for the Sydney 2000 Forecast Demonstration Project , 2001 .

[36]  Hajime Nakamura,et al.  Data assimilation of GPS precipitable water vapor into the JMA mesoscale numerical weather prediction model and its impact on rainfall forecasts , 2004 .

[37]  Ming-Jen Yang,et al.  Precipitation Forecast of MM5 in the Taiwan Area during the 1998 Mei-yu Season , 2002 .

[38]  Ming Hu,et al.  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 , 2006 .

[39]  Wen-Chau Lee Overview of SoWMEX/TiMREX , 2009 .

[40]  Ying Zhang,et al.  Analysis and Prediction of a Squall Line Observed during IHOP Using Multiple WSR-88D Observations , 2008 .

[41]  T. Gal-Chen,et al.  A Method for the Initialization of the Anelastic Equations: Implications for Matching Models with Observations , 1978 .

[42]  Y. Kuo,et al.  Topographic Effects on a Wintertime Cold Front in Taiwan , 2006 .

[43]  David Yates,et al.  Prediction of a flash flood in complex terrain. Part I: A comparison of rainfall estimates from radar, and very short range rainfall simulations from a dynamic model and an automated algorithmic system. , 2000 .