Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

AbstractMeaningful verification and evaluation of convection-allowing models requires approaches that do not rely on point-to-point matches of forecast and observed fields. In this study, one such approach—a beta version of the Method for Object-Based Diagnostic Evaluation (MODE) that incorporates the time dimension [known as MODE time-domain (MODE-TD)]—was applied to 30-h precipitation forecasts from four 4-km grid-spacing members of the 2010 Storm-Scale Ensemble Forecast system with different microphysics parameterizations. Including time in MODE-TD provides information on rainfall system evolution like lifetime, timing of initiation and dissipation, and translation.The simulations depicted the spatial distribution of time-domain precipitation objects across the United States quite well. However, all simulations overpredicted the number of objects, with the Thompson microphysics scheme overpredicting the most and the Morrison method the least. For the smallest smoothing radius and rainfall threshold use...

[1]  James Correia,et al.  Tornado Pathlength Forecasts from 2010 to 2011 Using Ensemble Updraft Helicity , 2013 .

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

[3]  Xuguang Wang,et al.  Hierarchical Cluster Analysis of a Convection-Allowing Ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of the Object-Oriented Cluster Analysis Method for Precipitation Fields , 2011 .

[4]  Xuguang Wang,et al.  Object-Based Evaluation of a Storm-Scale Ensemble during the 2009 NOAA Hazardous Weather Testbed Spring Experiment , 2012 .

[5]  S. J. Weiss,et al.  Some Practical Considerations Regarding Horizontal Resolution in the First Generation of Operational Convection-Allowing NWP , 2008 .

[6]  S. J. Weiss,et al.  An Overview Of the 2010 hAzArdOus weAther testbed experimentAl fOrecAst prOgrAm spring experiment , 2012 .

[7]  Stuart D. Miller Preliminary assessment of timing differences between convective initiation and severe initiation , 2012 .

[8]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[9]  John S. Kain,et al.  Extracting Unique Information from High-Resolution Forecast Models: Monitoring Selected Fields and Phenomena Every Time Step , 2010 .

[10]  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 .

[11]  Valliappa Lakshmanan,et al.  An Efficient , General-Purpose Technique to Identify Storm Cells in Geospatial Images , 2010 .

[12]  Elizabeth E. Ebert,et al.  Toward Better Understanding of the Contiguous Rain Area (CRA) Method for Spatial Forecast Verification , 2009 .

[13]  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 .

[14]  John D. Tuttle,et al.  Inferences of Predictability Associated with Warm Season Precipitation Episodes , 2001 .

[15]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation , 2001 .

[16]  J. McBride,et al.  Verification of precipitation in weather systems: determination of systematic errors , 2000 .

[17]  Fanyou Kong,et al.  Evaluation of CAPS multi-model storm-scale ensemble forecast for the NOAA HWT 2010 Spring Experiment , 2010 .

[18]  Song-You Hong,et al.  Development of an Effective Double-Moment Cloud Microphysics Scheme with Prognostic Cloud Condensation Nuclei (CCN) for Weather and Climate Models , 2010 .

[19]  J. Curry,et al.  A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description , 2005 .

[20]  Jason E. Nachamkin,et al.  Mesoscale Verification Using Meteorological Composites , 2004 .

[21]  S. J. Weiss,et al.  Toward Improved Convection-Allowing Ensembles: Model Physics Sensitivities and Optimizing Probabilistic Guidance with Small Ensemble Membership , 2010 .

[22]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[23]  A. Speranza,et al.  Sensitivity of Precipitation Forecast Skill Scores to Bilinear Interpolation and a Simple Nearest-Neighbor Average Method on High-Resolution Verification Grids , 2003 .

[24]  G. Bryan,et al.  Sensitivity of a Simulated Squall Line to Horizontal Resolution and Parameterization of Microphysics , 2012 .

[25]  Fanyou Kong,et al.  Hierarchical Cluster Analysis of a Convection-Allowing Ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Ensemble Clustering over the Whole Experiment Period , 2011 .

[26]  Tara L. Jensen An Overview of the Objective Evaluation Performed During the Hazardous Weather Testbed (HWT) 2010 Spring Experimen , 2010 .

[27]  B. Brown,et al.  Object-Based Verification of Precipitation Forecasts. Part I: Methodology and Application to Mesoscale Rain Areas , 2006 .

[28]  James Correia,et al.  A Feasibility Study for Probabilistic Convection Initiation Forecasts Based on Explicit , 2012 .

[29]  Barbara G. Brown,et al.  Forecast verification: current status and future directions , 2008 .

[30]  N. Roberts,et al.  Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events , 2008 .

[31]  Neil I. Fox,et al.  An Object-Oriented Multiscale Verification Scheme , 2010 .

[32]  E. Rogers,et al.  The NCEP North American Mesoscale Modeling System : Recent changes and future plans , 2009 .

[33]  Christopher A. Davis,et al.  Corridors of Warm Season Precipitation in the Central United States , 2006 .

[34]  E. Lorenz The predictability of a flow which possesses many scales of motion , 1969 .

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

[36]  Xuguang Wang,et al.  Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble , 2012 .

[37]  B. Brown,et al.  The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the 2005 NSSL/SPC Spring Program , 2009 .

[38]  B. Brown,et al.  Object-Based Verification of Precipitation Forecasts. Part II: Application to Convective Rain Systems , 2006 .

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

[40]  G. Mellor,et al.  Development of a turbulence closure model for geophysical fluid problems , 1982 .

[41]  Ming Hu,et al.  Impact of Configurations of Rapid Intermittent Assimilation of Wsr-88d Radar Data for the 8 May 2003 Oklahoma City Tornadic Thunderstorm Case , 2022 .

[42]  Song‐You Hong,et al.  The WRF Single-Moment 6-Class Microphysics Scheme (WSM6) , 2006 .

[43]  Zaviša I. Janić Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso model , 2001 .

[44]  C. Frei,et al.  SAL—A Novel Quality Measure for the Verification of Quantitative Precipitation Forecasts , 2008 .

[45]  Jidong Gao,et al.  A Three-Dimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars , 2004 .

[46]  W. Gallus,et al.  Neighborhood-Based Verification of Precipitation Forecasts from Convection-Allowing NCAR WRF Model Simulations and the Operational NAM , 2010 .

[47]  Christopher A. Davis,et al.  Initiation of precipitation episodes relative to elevated terrain , 2004 .

[48]  E. Lorenz,et al.  The predictability of a flow which possesses many scales of motion , 1969 .

[49]  Rebecca D. Adams-Selin,et al.  Sensitivity of Bow-Echo Simulation to Microphysical Parameterizations , 2013 .

[50]  Fanyou Kong,et al.  Object-Based Evaluation of the Impact of Horizontal Grid Spacing on Convection-Allowing Forecasts , 2013 .

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

[52]  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 .

[53]  Kevin W. Manning,et al.  Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part I: Description and Sensitivity Analysis , 2004 .

[54]  M. Xue 3B.1 CAPS REALTIME STORM-SCALE ENSEMBLE AND HIGH-RESOLUTION FORECASTS AS PART OF THE NOAA HAZARDOUS WEATHER TESTBED 2007 SPRING EXPERIMENT , 2007 .

[55]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

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

[57]  W. Skamarock,et al.  The Impact of Positive-Definite Moisture Transport on NWP Precipitation Forecasts , 2009 .

[58]  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 .

[59]  William A. Gallus,et al.  The Impact of Large-Scale Forcing on Skill of Simulated Convective Initiation and Upscale Evolution with Convection-Allowing Grid Spacings in the WRF* , 2013 .

[60]  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 .

[61]  James Correia,et al.  Forecasting Tornado Pathlengths Using a Three-Dimensional Object Identification Algorithm Applied to Convection-Allowing Forecasts , 2012 .