Comparison of the Analyses and Forecasts of a Tornadic Supercell Storm from Assimilating Phased-Array Radar and WSR-88D Observations

AbstractNOAA’s National Severe Storms Laboratory is actively developing phased-array radar (PAR) technology, a potential next-generation weather radar, to replace the current operational WSR-88D radars. One unique feature of PAR is its rapid scanning capability, which is at least 4–5 times faster than the scanning rate of WSR-88D. To explore the impact of such high-frequency PAR observations compared with traditional WSR-88D on severe weather forecasting, several storm-scale data assimilation and forecast experiments are conducted. Reflectivity and radial velocity observations from the 22 May 2011 Ada, Oklahoma, tornadic supercell storm are assimilated over a 45-min period using observations from the experimental PAR located in Norman, Oklahoma, and the operational WSR-88D radar at Oklahoma City, Oklahoma. The radar observations are assimilated into the ARPS model within a heterogeneous mesoscale environment and 1-h ensemble forecasts are generated from analyses every 15 min. With a 30-min assimilation pe...

[1]  Sebastián M. Torres,et al.  Adaptive Range Oversampling to Achieve Faster Scanning on the National Weather Radar Testbed Phased-Array Radar , 2011 .

[2]  Louis J. Wicker,et al.  Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses , 2011 .

[3]  Nicholas E. Graham,et al.  Conditional Probabilities, Relative Operating Characteristics, and Relative Operating Levels , 1999 .

[4]  David J. Stensrud,et al.  Impact of Phased-Array Radar Observations over a Short Assimilation Period: Observing System Simulation Experiments Using an Ensemble Kalman Filter , 2010 .

[5]  Adam J. Clark,et al.  Short-Term Probabilistic Forecasts of the 31 May 2013 Oklahoma Tornado and Flash Flood Event Using a Continuous-Update-Cycle Storm-Scale Ensemble System , 2016 .

[6]  M. Chou,et al.  Technical report series on global modeling and data assimilation. Volume 3: An efficient thermal infrared radiation parameterization for use in general circulation models , 1994 .

[7]  Ming Xue,et al.  A Hybrid MPI–OpenMP Parallel Algorithm and Performance Analysis for an Ensemble Square Root Filter Designed for Multiscale Observations , 2013 .

[8]  Chris Snyder,et al.  A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part II: Short-Range Ensemble Forecasts , 2010 .

[9]  G. Evensen,et al.  Asynchronous data assimilation with the EnKF , 2010 .

[10]  Louis J. Wicker,et al.  Additive Noise for Storm-Scale Ensemble Data Assimilation , 2009 .

[11]  George C. Craig,et al.  The Impact of Data Assimilation Length Scales on Analysis and Prediction of Convective Storms , 2014 .

[12]  Pamela L. Heinselman,et al.  31 May 2013 El Reno Tornadoes: Advantages of Rapid-Scan Phased-Array Radar Data from a Warning Forecaster’s Perspective* , 2015 .

[13]  David J. Stensrud,et al.  The Ensemble Kalman Filter Analyses and Forecasts of the 8 May 2003 Oklahoma City Tornadic Supercell Storm Using Single- and Double-Moment Microphysics Schemes , 2013 .

[14]  M. Yau,et al.  A Multimoment Bulk Microphysics Parameterization. Part IV: Sensitivity Experiments , 2006 .

[15]  Franco Siccardi,et al.  Turbulence Closure Parameterization and Grid Spacing Effects in Simulated Supercell Storms , 2010 .

[16]  Wen-yih Sun,et al.  Diffusion Model for a Convective Layer. Part I: Numerical Simulation of Convective Boundary Layer , 1986 .

[17]  Douglas E. Forsyth,et al.  The National Weather Radar Testbed (Phased-Array) , 2005 .

[18]  M. Xue,et al.  The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications , 2001 .

[19]  Robert R. Hoffman,et al.  Tornado Warning Decisions Using Phased-Array Radar Data , 2015 .

[20]  Pamela L. Heinselman,et al.  Evolution of a Quasi-Linear Convective System Sampled by Phased Array Radar , 2012 .

[21]  C. Snyder,et al.  A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses , 2009 .

[22]  Louis J. Wicker,et al.  Storm-Scale Data Assimilation and Ensemble Forecasts for the 27 April 2011 Severe Weather Outbreak in Alabama , 2015 .

[23]  Ming Xue,et al.  A four‐dimensional asynchronous ensemble square‐root filter (4DEnSRF) algorithm and tests with simulated radar data , 2013 .

[24]  W. Briggs Statistical Methods in the Atmospheric Sciences , 2007 .

[25]  Tianyou Yu,et al.  Multi-scale Analysis and Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell Storm Assimilating Radar and Surface Network Data using EnKF , 2003 .

[26]  Louis J. Wicker,et al.  Impacts of a Storm Merger on the 24 May 2011 El Reno, Oklahoma, Tornadic Supercell , 2015 .

[27]  Rick P. Thomas,et al.  Impacts of Phased-Array Radar Data on Forecaster Performance during Severe Hail and Wind Events , 2015 .

[28]  Y. Wang,et al.  Mesovortices within the 8 May 2009 Bow Echo over the Central United States: Analyses of the Characteristics and Evolution Based on Doppler Radar Observations and a High-Resolution Model Simulation , 2013 .

[29]  Dustan M. Wheatley,et al.  Ensemble Kalman Filter Analyses and Forecasts of a Severe Mesoscale Convective System Using Different Choices of Microphysics Schemes , 2014 .

[30]  Travis M. Smith,et al.  An Automated Method for Depicting Mesocyclone Paths and Intensities , 2013 .

[31]  Louis J. Wicker,et al.  Impact of the Environmental Low-Level Wind Profile on Ensemble Forecasts of the 4 May 2007 Greensburg, Kansas, Tornadic Storm and Associated Mesocyclones , 2012 .

[32]  Ming Xue,et al.  Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System , 2015 .

[33]  Jian Zhang,et al.  National mosaic and multi-sensor QPE (NMQ) system description, results, and future plans , 2011 .

[34]  Stanley G. Benjamin,et al.  CONVECTIVE-SCALE WARN-ON-FORECAST SYSTEM: A vision for 2020 , 2009 .

[35]  Mingjing Tong,et al.  Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability , 2008 .

[36]  Alan K. Betts,et al.  A Composite Mesoscale Cumulonimbus Budget , 1973 .

[37]  H. Pan,et al.  Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model , 1996 .

[38]  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 .

[39]  Louis J. Wicker,et al.  Wind and Temperature Retrievals in the 17 May 1981 Arcadia, Oklahoma, Supercell: Ensemble Kalman Filter Experiments , 2004 .

[40]  R. Vogt,et al.  Agile-Beam Phased Array Radar for Weather Observations , 2007 .

[41]  M. Snyder,et al.  Ensemble storm-scale data assimilation and prediction for severe convective storms [presentation] , 2010 .

[42]  Guifu Zhang,et al.  The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter , 2014 .

[43]  David J. Stensrud,et al.  Importance of Horizontally Inhomogeneous Environmental Initial Conditions to Ensemble Storm-Scale Radar Data Assimilation and Very Short-Range Forecasts , 2010 .

[44]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[45]  Travis M. Smith,et al.  Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar , 2008 .

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

[47]  Sebastián M. Torres,et al.  High-Temporal-Resolution Capabilities of the National Weather Radar Testbed Phased-Array Radar , 2011 .

[48]  David J. Stensrud,et al.  Assimilating Surface Mesonet Observations with the EnKF to Improve Ensemble Forecasts of Convection Initiation on 29 May 2012 , 2015 .

[49]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[50]  Ming Hu,et al.  Numerical Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell and Embedded Tornado Using ARPS with the Assimilation of WSR-88D Data , 2014 .

[51]  John Y. N. Cho,et al.  The Next-Generation Multimission U.S. Surveillance Radar Network , 2007 .

[52]  K. Droegemeier,et al.  The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification , 2000 .

[53]  J. Whitaker,et al.  Evaluating Methods to Account for System Errors in Ensemble Data Assimilation , 2012 .

[54]  Eric C. Bruning,et al.  Lightning Activity in a Hail-Producing Storm Observed with Phased-Array Radar , 2011 .

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

[56]  Corey K. Potvin,et al.  Assessing Ensemble Forecasts of Low-Level Supercell Rotation within an OSSE Framework , 2013 .

[57]  D. Stensrud,et al.  Comparison of Single-Parameter and Multiparameter Ensembles for Assimilation of Radar Observations Using the Ensemble Kalman Filter , 2012 .

[58]  J. Kain,et al.  A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization , 1990 .

[59]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[60]  Daphne LaDue,et al.  Exploring Impacts of Rapid-Scan Radar Data on NWS Warning Decisions , 2012 .

[61]  M. Yau,et al.  A Multimoment Bulk Microphysics Parameterization. Part III: Control Simulation of a Hailstorm , 2006 .

[62]  John S. Kain,et al.  Convective parameterization for mesoscale models : The Kain-Fritsch Scheme , 1993 .

[63]  Corey K. Potvin,et al.  Progress and challenges with Warn-on-Forecast , 2012 .

[64]  Sutherland,et al.  Statewide Monitoring of the Mesoscale Environment: A Technical Update on the Oklahoma Mesonet , 2007 .

[65]  Mingjing Tong,et al.  Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Radar Data and Ensemble Square-root Kalman Filter . Part I : Sensitivity Analysis and Parameter , 2007 .

[66]  Travis M. Smith,et al.  The Warning Decision Support System–Integrated Information , 2007 .

[67]  Mingjing Tong,et al.  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 , 2012 .

[68]  Alan Shapiro,et al.  Sensitivity of Real-Data Simulations of the 3 May 1999 Oklahoma City Tornadic Supercell and Associated Tornadoes to Multimoment Microphysics. Part I: Storm- and Tornado-Scale Numerical Forecasts , 2015 .

[69]  Jidong Gao,et al.  The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation , 2003 .

[70]  M. Chou,et al.  Parameterizations for the Absorption of Solar Radiation by O2 and CO2 with Application to Climate Studies , 1990 .

[71]  M. Xue,et al.  Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar Data , 2011 .

[72]  M. Chou,et al.  A Solar Radiation Model for Use in Climate Studies , 1992 .

[73]  Ming Xue,et al.  Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses using WSR-88D and CASA Radar Data , 2012 .

[74]  Daniel T. Dawson,et al.  Comparison of Evaporation and Cold Pool Development between Single-moment and Multi-moment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms , 2009 .

[75]  Michael D. Eilts,et al.  The Oklahoma Mesonet: A Technical Overview , 1995 .

[76]  Xuan Xin Mesovortices within the 8 May 2009 Bow Echo over the Central United States : Analyses of the Characteristics and Evolution Based on Doppler Radar Observations and a High-Resolution Model Simulation , 2015 .

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

[78]  Jeffrey L. Anderson An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .