Cloud‐resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts

An intercomparison study of a midlatitude mesoscale squall line is performed using the Weather Research and Forecasting (WRF) model at 1 km horizontal grid spacing with eight different cloud microphysics schemes to investigate processes that contribute to the large variability in simulated cloud and precipitation properties. All simulations tend to produce a wider area of high radar reflectivity (Z_e > 45 dBZ) than observed but a much narrower stratiform area. The magnitude of the virtual potential temperature drop associated with the gust front passage is similar in simulations and observations, while the pressure rise and peak wind speed are smaller than observed, possibly suggesting that simulated cold pools are shallower than observed. Most of the microphysics schemes overestimate vertical velocity and Ze in convective updrafts as compared with observational retrievals. Simulated precipitation rates and updraft velocities have significant variability across the eight schemes, even in this strongly dynamically driven system. Differences in simulated updraft velocity correlate well with differences in simulated buoyancy and low-level vertical perturbation pressure gradient, which appears related to cold pool intensity that is controlled by the evaporation rate. Simulations with stronger updrafts have a more optimal convective state, with stronger cold pools, ambient low-level vertical wind shear, and rear-inflow jets. Updraft velocity variability between schemes is mainly controlled by differences in simulated ice-related processes, which impact the overall latent heating rate, whereas surface rainfall variability increases in no-ice simulations mainly because of scheme differences in collision-coalescence parameterizations.

[1]  Kathrin Wapler,et al.  A limited area model (LAM) intercomparison study of a TWP‐ICE active monsoon mesoscale convective event , 2012 .

[2]  W. Tao,et al.  Sensitivity of a Cloud-Resolving Model to Bulk and Explicit Bin Microphysical Schemes. Part I: Comparisons , 2009 .

[3]  Pavlos Kollias,et al.  Improving representation of convective transport for scale‐aware parameterization: 1. Convection and cloud properties simulated with spectral bin and bulk microphysics , 2015 .

[4]  T. Benjamin.  Gravity currents and related phenomena , 1968, Journal of Fluid Mechanics.

[5]  Fuqing Zhang,et al.  A Modeling Study on the Development of a Bowing Structure and Associated Rear Inflow within a Squall Line over South China , 2012 .

[6]  G. Thompson,et al.  Sensitivity of a simulated midlatitude squall line to parameterization of raindrop breakup , 2012 .

[7]  S. Kreidenweis,et al.  The microphysical contributions to and evolution of latent heating profiles in two MC3E MCSs , 2015 .

[8]  J. Curry,et al.  Confronting Models with Data: The Gewex Cloud Systems Study , 2003 .

[9]  A. Heymsfield,et al.  Investigation of liquid cloud microphysical properties of deep convective systems: 1. Parameterization raindrop size distribution and its application for stratiform rain estimation , 2016 .

[10]  William R. Cotton,et al.  The Impact of Hail Size on Simulated Supercell Storms , 2004 .

[11]  A. Khain.  Notes on state-of-the-art investigations of aerosol effects on precipitation: a critical review , 2009 .

[12]  E. Bigg.  The formation of atmospheric ice crystals by the freezing of droplets , 1953 .

[13]  Y. Hong,et al.  Impacts of Polarimetric Radar Observations on Hydrologic Simulation , 2010 .

[14]  R. Kuligowski,et al.  Assessment of SCaMPR and NEXRAD Q2 Precipitation Estimates Using Oklahoma Mesonet Observations , 2014 .

[15]  Peter T. May,et al.  Mass-Flux Characteristics of Tropical Cumulus Clouds from Wind Profiler Observations at Darwin, Australia , 2015 .

[16]  M. Yau,et al.  A Multimoment Bulk Microphysics Parameterization. Part II: A Proposed Three-Moment Closure and Scheme Description , 2005 .

[17]  R. Houze.  Chapter 7 - Basic Cumulus Dynamics☆ , 2014 .

[18]  W. Tao,et al.  Sensitivity of a Cloud-Resolving Model to Bulk and Explicit Bin Microphysical Schemes. Part II: Cloud Microphysics and Storm Dynamics Interactions , 2009 .

[19]  M. Weisman.  The Role of Convectively Generated Rear-Inflow Jets in the Evolution of Long-Lived Mesoconvective Systems , 1992 .

[20]  Adrian M. Tompkins,et al.  Organization of Tropical Convection in Low Vertical Wind Shears: The Role of Cold Pools , 2001 .

[21]  L. Donner,et al.  Nucleation processes in deep convection simulated by a cloud-system-resolving model with double-moment bulk microphysics , 2007 .

[22]  Michele M. Rienecker,et al.  Precipitation intensity and variation during MC3E: A numerical modeling study , 2013 .

[23]  A. Ryzhkov,et al.  Polarimetric radar and aircraft observations of saggy bright bands during MC3E , 2016 .

[24]  John S. Kain,et al.  The Kain–Fritsch Convective Parameterization: An Update , 2004 .

[25]  W. Cotton,et al.  New primary ice-nucleation parameterizations in an explicit cloud model , 1992 .

[26]  Jimmy W. Voyles,et al.  The Arm Climate Research Facility: A Review of Structure and Capabilities , 2013 .

[27]  Witold F. Krajewski,et al.  Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting , 2000 .

[28]  J. Wyngaard,et al.  Resolution Requirements for the Simulation of Deep Moist Convection , 2003 .

[29]  W. Cotton,et al.  Parameterization and Impact of Ice initiation Processes Relevant to Numerical Model Simulations of Cirrus Clouds. , 1994 .

[30]  W. Grabowski.  Extracting microphysical impacts in large-eddy simulations of shallow convection , 2014 .

[31]  Yang Tian,et al.  Mechanisms for convection triggering by cold pools , 2015, 1511.02815.

[32]  G. Thompson,et al.  Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes , 2009 .

[33]  H. Morrison,et al.  Effects of Horizontal and Vertical Grid Spacing on Mixing in Simulated Squall Lines and Implications for Convective Strength and Structure , 2015 .

[34]  Xiaoqing Wu,et al.  A Comparison of TWP-ICE Observational Data with Cloud-Resolving Model Results , 2012 .

[35]  Jerry M. Straka,et al.  A Summary of Convective-Core Vertical Velocity Properties Using ARM UHF Wind Profilers in Oklahoma , 2013 .

[36]  J. Milbrandt,et al.  Predicting the Snow-to-Liquid Ratio of Surface Precipitation Using a Bulk Microphysics Scheme , 2012 .

[37]  Jiwen Fan,et al.  Improving bulk microphysics parameterizations in simulations of aerosol effects , 2013 .

[38]  Yunyan Zhang,et al.  Interactions between cumulus convection and its environment as revealed by the MC3E sounding array , 2014 .

[39]  W. Collins,et al.  Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models , 2008 .

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

[41]  S. G. Bigler,et al.  History of Operational Use of Weather Radar by U.S. Weather Services. Part II: Development of Operational Doppler Weather Radars , 1998 .

[42]  M. Baldauf,et al.  Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities , 2011 .

[43]  Eric C. Bruning,et al.  Simulated Electrification of a Small Thunderstorm with Two-Moment Bulk Microphysics , 2010 .

[44]  G. Bryan,et al.  A Multimodel Assessment of RKW Theory’s Relevance to Squall-Line Characteristics , 2006 .

[45]  Di Wu,et al.  High‐resolution NU‐WRF simulations of a deep convective‐precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations , 2016, Journal of geophysical research. Atmospheres : JGR.

[46]  F. Guichard,et al.  A gcss model intercomparison for a tropical squall line observed during toga‐coare. I: Cloud‐resolving models , 2000 .

[47]  S. G. Bigler,et al.  History of Operational Use of Weather Radar by U.S. Weather Services. Part I: The Pre-NEXRAD Era , 1998 .

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

[49]  Jiwen Fan,et al.  Evaluation of cloud‐resolving and limited area model intercomparison simulations using TWP‐ICE observations: 1. Deep convective updraft properties , 2014 .

[50]  H. Morrison,et al.  Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part III: Introduction of Multiple Free Categories , 2016 .

[51]  N Bharadwaj,et al.  THE MIDLATITUDE CONTINENTAL CONVECTIVE CLOUDS EXPERIMENT (MC3E). , 2016, Bulletin of the American Meteorological Society.

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

[53]  Renee A. McPherson,et al.  The Value of Routine Site Visits in Managing and Maintaining Quality Data from the Oklahoma Mesonet , 2006 .

[54]  G. Thompson,et al.  Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization , 2008 .

[55]  A. Ryzhkov,et al.  Estimation of Rainfall Based on the Results of Polarimetric Echo Classification , 2007 .

[56]  W. Tao,et al.  Sensitivity of a Cloud-Resolving Model to the Bulk and Explicit Bin Microphysical Schemes. Part 1; Validations with a PRE-STORM Case , 2004 .

[57]  Gregory Thompson,et al.  Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part II: Case Study Comparisons with Observations and Other Schemes , 2015 .

[58]  Yuan Wang,et al.  Long-term impacts of aerosols on precipitation and lightning over the Pearl River Delta megacity area in China , 2011 .

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

[60]  B. Ferrier,et al.  A Double-Moment Multiple-Phase Four-Class Bulk Ice Scheme. Part I: Description , 1994 .

[61]  T. Takemi.  Environmental stability control of the intensity of squall lines under low‐level shear conditions , 2007 .

[62]  Zhanqing Li,et al.  Potential aerosol indirect effects on atmospheric circulation and radiative forcing through deep convection , 2012, Geophysical Research Letters.

[63]  A. Arakawa.  The Cumulus Parameterization Problem: Past, Present, and Future , 2004 .

[64]  Zhanqing Li,et al.  Microphysical effects determine macrophysical response for aerosol impacts on deep convective clouds , 2013, Proceedings of the National Academy of Sciences.

[65]  P. Kollias,et al.  Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E , 2016 .

[66]  C. Williams,et al.  Evaluation of cloud‐resolving and limited area model intercomparison simulations using TWP‐ICE observations: 2. Precipitation microphysics , 2014 .

[67]  Wojciech W. Grabowski,et al.  Untangling Microphysical Impacts on Deep Convection Applying a Novel Modeling Methodology , 2015 .

[68]  John H. Seinfeld,et al.  Theoretical basis for convective invigoration due to increased aerosol concentration , 2011 .

[69]  R. Rauber,et al.  Numerical Simulation of the Effects of Varying Ice Crystal Nucleation Rates and Aggregation Processes on Orographic Snowfall , 1986 .

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

[71]  K. D. Beheng,et al.  Representation of microphysical processes in cloud‐resolving models: Spectral (bin) microphysics versus bulk parameterization , 2015 .

[72]  S. Kreidenweis,et al.  Aerosol effects on the anvil characteristics of mesoscale convective systems , 2016 .

[73]  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 .

[74]  Y. Wang,et al.  Implementation of a two‐moment bulk microphysics scheme to the WRF model to investigate aerosol‐cloud interaction , 2008 .

[75]  S. McFarlane,et al.  Evaluation of Cloud-Resolving Model Intercomparison Simulations Using TWP-ICE Observations: Precipitation and Cloud Structure , 2011 .

[76]  H. Morrison,et al.  Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests , 2015 .

[77]  Jian Zhang,et al.  Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaics: Examples of Convective Storms and Stratiform Rain Echoes , 2005 .

[78]  Guang J. Zhang,et al.  Evaluating convective parameterization closures using cloud‐resolving model simulation of tropical deep convection , 2015 .

[79]  H. Morrison,et al.  Modeling Condensation in Deep Convection , 2017 .

[80]  Adam Theisen,et al.  Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign , 2012 .

[81]  J. Dudhia,et al.  A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes , 2006 .

[82]  J. Klett,et al.  Microphysics of Clouds and Precipitation , 1978, Nature.

[83]  W. Cooper,et al.  Ice Initiation in Natural Clouds , 1986 .

[84]  Pengfei Zhang,et al.  Derivation of Aerosol Profiles for MC3E Convection Studies and Use in Simulations of the 20 May Squall Line Case , 2017 .

[85]  Minghua Zhang,et al.  An intercomparison of cloud‐resolving models with the atmospheric radiation measurement summer 1997 intensive observation period data , 2002 .

[86]  Pavlos Kollias,et al.  On polarimetric radar signatures of deep convection for model evaluation: columns of specific differential phase observed during MC3E. , 2016, Monthly weather review.

[87]  Alain Protat,et al.  Convective cloud vertical velocity and mass‐flux characteristics from radar wind profiler observations during GoAmazon2014/5 , 2016 .

[88]  R. McTaggart-Cowan,et al.  Sedimentation-Induced Errors in Bulk Microphysics Schemes , 2010 .

[89]  R. Rotunno,et al.  A Theory for Strong, Long-Lived Squall Lines , 1988 .

[90]  M. Yau,et al.  A Multimoment Bulk Microphysics Parameterization. Part I: Analysis of the Role of the Spectral Shape Parameter , 2005 .

[91]  Morris L. Weisman,et al.  “A Theory for Strong Long-Lived Squall Lines” Revisited , 2004 .