Comparison of observed and simulated spatial patterns of ice microphysical processes in tropical oceanic mesoscale convective systems

To equitably compare the spatial pattern of ice microphysical processes produced by three microphysical parameterizations with each other, observations, and theory, simulations of tropical oceanic mesoscale convective systems (MCSs) in the Weather Research and Forecasting (WRF) model were forced to develop the same mesoscale circulations as observations by assimilating radial velocity data from a Doppler radar. The same general layering of microphysical processes was found in observations and simulations with deposition anywhere above the 0°C level, aggregation at and above the 0°C level, melting at and below the 0°C level, and riming near the 0°C level. Thus, this study is consistent with the layered ice microphysical pattern portrayed in previous conceptual models and indicated by dual-polarization radar data. Spatial variability of riming in the simulations suggests that riming in the midlevel inflow is related to convective-scale vertical velocity perturbations. Finally, this study sheds light on limitations of current generally available bulk microphysical parameterizations. In each parameterization, the layers in which aggregation and riming took place were generally too thick and the frequency of riming was generally too high compared to the observations and theory. Additionally, none of the parameterizations produced similar details in every microphysical spatial pattern. Discrepancies in the patterns of microphysical processes between parameterizations likely factor into creating substantial differences in model reflectivity patterns. It is concluded that improved parameterizations of ice-phase microphysics will be essential to obtain reliable, consistent model simulations of tropical oceanic MCSs.

[1]  C. Bretherton,et al.  Cloud-Resolving Model Simulations of KWAJEX: Model Sensitivities and Comparisons with Satellite and Radar Observations , 2007 .

[2]  Robert A. Houze,et al.  Melting and Evaporation of Hydrometeors in Precipitation from the Anvil Clouds of Deep Tropical Convection , 1979 .

[3]  Jerry M. Straka,et al.  Bulk Hydrometeor Classification and Quantification Using Polarimetric Radar Data: Synthesis of Relations , 2000 .

[4]  Devis Tuia,et al.  Hydrometeor classification from polarimetric radar measurements: a clustering approach , 2015 .

[5]  William R. Cotton,et al.  The Sensitivity of a Simulated Extratropical Mesoscale Convective System to Longwave Radiation and Ice-Phase Microphysics , 1988 .

[6]  C. Prigent,et al.  Modeling of passive microwave responses in convective situations using output from mesoscale models: Comparison with TRMM/TMI satellite observations , 2004 .

[7]  M. Lemone,et al.  Cumulonimbus vertical velocity events in GATE. Part I: Diameter, intensity and mass flux , 1980 .

[8]  Alexander V. Ryzhkov,et al.  Cloud Microphysics Retrieval Using S-Band Dual-Polarization Radar Measurements , 1999 .

[9]  R. Stewart,et al.  Mesoscale Circulations Forced by Melting Snow. Part I: Basic Simulations and Dynamics. , 1988 .

[10]  Patrick Minnis,et al.  An Intercomparison of Microphysical Retrieval Algorithms for Upper-Tropospheric Ice Clouds , 2007 .

[11]  Leo J. Donner,et al.  A Cumulus Parameterization Including Mass Fluxes, Convective Vertical Velocities, and Mesoscale Effects: Thermodynamic and Hydrological Aspects in a General Circulation Model , 2001 .

[12]  Tsutomu Takahashi,et al.  Precipitation Mechanisms of Cumulonimbus Clouds at Pohnpei, Micronesia , 1993 .

[13]  Mariko Oue,et al.  A Hydrometeor Classification Method for X-Band Polarimetric Radar: Construction and Validation Focusing on Solid Hydrometeors under Moist Environments , 2015 .

[14]  G. Thompson,et al.  Evaluation of convection‐permitting model simulations of cloud populations associated with the Madden‐Julian Oscillation using data collected during the AMIE/DYNAMO field campaign , 2014 .

[15]  W. Tao,et al.  Reducing the Biases in Simulated Radar Reflectivities from a Bulk Microphysics Scheme: Tropical Convective Systems , 2011 .

[16]  Peter V. Hobbs,et al.  The dimensions and aggregation of ice crystals in natural clouds , 1974 .

[17]  G. Cox,et al.  Aircraft and dual polarization radar observations of hydrometeors in light stratiform precipitation , 2007 .

[18]  S. Braun,et al.  The Transition Zone and Secondary Maximum of Radar Reflectivity behind a Midlatitude Squall Line: Results Retrieved from Doppler Radar Data , 1994 .

[19]  J. Gamache,et al.  Water Budget of a Mesoscale Convective System in the Tropics , 1983 .

[20]  W. Tao,et al.  Modeling Study of a Tropical Squall-Type Convective Line , 1989 .

[21]  D. Churchill,et al.  Effects of Radiation and Turbulence on the Diabatic Heating and Water Budget of the Stratiform Region of a Tropical Cloud Cluster , 1991 .

[22]  S. Esbensen,et al.  A Composite Life Cycle of Nonsquall Mesoscale Convective Systems over the Tropical Ocean. Part I: Kinematic Fields , 1985 .

[23]  A. Korolev,et al.  Small Ice Particles in Tropospheric Clouds: Fact or Artifact? Airborne Icing Instrumentation Evaluation Experiment , 2011 .

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

[25]  C. Bishop,et al.  Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter , 2009 .

[26]  A. Hill,et al.  Diagnosis of systematic differences between multiple parametrizations of warm rain microphysics using a kinematic framework , 2012 .

[27]  U. Blahak,et al.  Comparison of one-moment and two-moment bulk microphysics for high-resolution climate simulations of intense precipitation , 2014 .

[28]  J. Dudhia.  Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[29]  Ice microphysics retrieval in the convective systems of the Indian Ocean during the CINDY–DYNAMO campaign , 2015 .

[30]  R. Houze.  Observed structure of mesoscale convective systems and implications for large-scale heating , 1989 .

[31]  Narayanaswamy Balakrishnan,et al.  Polarimetric signatures in the stratiform region of a mesoscale convective system , 1993 .

[32]  R. Trapp.  Mesoscale Convective Systems , 2013 .

[33]  Fuqing Zhang,et al.  Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study , 2008 .

[34]  M. Bailey,et al.  A Comprehensive Habit Diagram for Atmospheric Ice Crystals: Confirmation from the Laboratory, AIRS II, and Other Field Studies , 2009 .

[35]  Y. Lemaitre,et al.  Polarimetric signatures and hydrometeor classification of West African squall lines , 2010 .

[36]  Robert A. Houze,et al.  Latent heating characteristics of the MJO computed from TRMM Observations , 2015 .

[37]  Edward J. Zipser,et al.  Mesoscale Convective Systems Defined by Their 85-GHz Ice Scattering Signature: Size and Intensity Comparison over Tropical Oceans and Continents , 1996 .

[38]  C. Bretherton,et al.  A New Moist Turbulence Parameterization in the Community Atmosphere Model , 2009 .

[39]  R. Houze,et al.  Precipitation hydrometeor type relative to the mesoscale airflow in mature oceanic deep convection of the Madden‐Julian Oscillation , 2014 .

[40]  Jerry M. Straka,et al.  Polarimetric Signatures above the Melting Layer in Winter Storms: An Observational and Modeling Study , 2013 .

[41]  Edward J. Zipser,et al.  A Census of Precipitation Features in the Tropics Using TRMM: Radar, Ice Scattering, and Lightning Observations , 2000 .

[42]  R. Houze,et al.  Kinematic characteristics of air flowing into and out of precipitating convection over the west Pacific warm pool: An airborne Doppler radar survey , 1999 .

[43]  V. Giraud,et al.  Microphysical characterisation of West African MCS anvils , 2010 .

[44]  Kyoko Ikeda,et al.  Freezing-Level Estimation with Polarimetric Radar , 2004 .

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

[46]  Charles N. Long,et al.  Tracking Pulses of the Madden–Julian Oscillation , 2013 .

[47]  Paul Markowski,et al.  An Investigation of the Goshen County, Wyoming, Tornadic Supercell of 5 June 2009 Using EnKF Assimilation of Mobile Mesonet and Radar Observations Collected during VORTEX2. Part I: Experiment Design and Verification of the EnKF Analyses , 2014 .

[48]  Angela K. Rowe,et al.  Microphysical characteristics of MJO convection over the Indian Ocean during DYNAMO , 2014 .

[49]  R. Hogan,et al.  Properties of embedded convection in warm‐frontal mixed‐phase cloud from aircraft and polarimetric radar , 2002 .

[50]  P. Webster,et al.  Tropical Upper-Tropospheric Extended Clouds: Inferences from Winter MONEX , 1980 .

[51]  Y. M. M. Antar,et al.  Precipitation particle identification with centimeter wavelength dual-polarization radars , 1984 .

[52]  S. Krueger,et al.  Cloud-Resolving Modeling of Deep Convection during KWAJEX. Part I: Comparison to TRMM Satellite and Ground-Based Radar Observations , 2008 .

[53]  Dong‐Kyou Lee,et al.  Retrieval of High-Resolution Wind Fields over the Southern Korean Peninsula Using the Doppler Weather Radar Network , 2009 .

[54]  E. Luke,et al.  The Role of Cloud Microphysics Parameterization in the Simulation of Mesoscale Convective System Clouds and Precipitation in the Tropical Western Pacific , 2013 .

[55]  R. Houze,et al.  Evolution of the Population of Precipitating Convective Systems over the Equatorial Indian Ocean in Active Phases of the Madden–Julian Oscillation , 2013 .

[56]  Wei Huang,et al.  A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results , 2004 .

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

[58]  Franco Molteni,et al.  Simulation of the Madden– Julian Oscillation and its teleconnections in the ECMWF forecast system , 2010 .

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

[60]  T. A. Seliga,et al.  Radar polarimetric backscattering properties of conical Graupel , 1984 .

[61]  G. Caniaux,et al.  A Numerical Study of the Stratiform Region of a Fast-Moving Squall Line. Part I: General Description and Water and Heat Budgets , 1994 .

[62]  A. Clark,et al.  Evaluation of and Suggested Improvements to the WSM6 Microphysics in WRF- ARW Using Synthetic and Observed GOES-13 Imagery , 2014 .

[63]  Rebecca D. Adams-Selin,et al.  Impact of Graupel Parameterization Schemes on Idealized Bow Echo Simulations , 2013 .

[64]  D. Churchill,et al.  Mesoscale Organization and Cloud Microphysics in a Bay of Bengal Depression , 1987 .

[65]  Tsutomu Takahashi,et al.  Videosonde observations of precipitation processes in equatorial cloud clusters. , 1995 .

[66]  Robert G. Fovell,et al.  Numerical Simulation of a Midlatitude Squall Line in Two Dimensions , 1988 .

[67]  Michael M. Bell,et al.  Validation of simulated hurricane drop size distributions using polarimetric radar , 2015 .

[68]  G. Caniaux,et al.  A Numerical Study of the Stratiform Region of a Fast-Moving Squall Line. Part II: Relationship between Mass, Pressure, and Momentum Fields , 1995 .

[69]  H. Uyeda,et al.  Microphysical Properties of Maritime Squall Line Observed on June 2, 2008 in Taiwan , 2012 .

[70]  R. Houze,et al.  The cloud population and onset of the Madden‐Julian Oscillation over the Indian Ocean during DYNAMO‐AMIE , 2013 .

[71]  C. Sui,et al.  Mechanisms of Cloud-radiation interaction in the tropics and midlatitudes , 1996 .

[72]  V. Chandrasekar,et al.  Polarimetric Doppler Weather Radar , 2001 .

[73]  R. Houze.  Mesoscale convective systems , 2004 .

[74]  W. Tao,et al.  The Effect of Melting Processes on the Development of a Tropical and a Midlatitude Squall Line , 1995 .

[75]  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 .

[76]  Robert A. Houze,et al.  Structures of atmospheric precipitation systems: A global survey , 1981 .

[77]  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 .

[78]  Jonathan P. Taylor,et al.  Airborne instruments to measure atmospheric aerosol particles, clouds and radiation: A cook's tour of mature and emerging technology , 2011 .

[79]  Juanzhen Sun,et al.  Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter , 2004 .

[80]  V. Chandrasekar,et al.  A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation , 2014 .

[81]  Joanne Simpson,et al.  Numerical Simulation of a Subtropical Squall Line over the Taiwan Strait , 1991 .

[82]  V. Chandrasekar,et al.  Polarimetric Radar Observations in the Ice Region of Precipitating Clouds at C-Band and X-Band Radar Frequencies , 2013 .

[83]  R. Houze,et al.  The Tropical Dynamical Response to Latent Heating Estimates Derived from the TRMM Precipitation Radar , 2004 .

[84]  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 .

[85]  L. Leung,et al.  Evaluating regional cloud-permitting simulations of the WRF model for the Tropical Warm Pool International Cloud Experiment (TWP-ICE, Darwin 2006) , 2009 .

[86]  Alexander V. Ryzhkov,et al.  The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS , 2009 .

[87]  William S. Olson,et al.  Improving Simulations of Convective Systems from TRMM LBA: Easterly and Westerly Regimes , 2007 .

[88]  M. Satoh,et al.  Evaluation of Precipitating Hydrometeor Parameterizations in a Single-Moment Bulk Microphysics Scheme for Deep Convective Systems over the Tropical Central Pacific , 2014 .

[89]  E. Zipser.  Some Views On “Hot Towers” after 50 Years of Tropical Field Programs and Two Years of TRMM Data , 2003 .

[90]  Fuqing Zhang,et al.  Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part IV: Comparison with 3DVAR in a Month-Long Experiment , 2007 .

[91]  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 .

[92]  J. Dudhia,et al.  Factors Affecting the Evolution of Hurricane Erin (2001) and the Distributions of Hydrometeors: Role of Microphysical Processes , 2006 .

[93]  Harry H. Hendon,et al.  Some Implications of the Mesoscale Circulations in Tropical Cloud Clusters for Large-Scale Dynamics and Climate , 1984 .

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

[95]  Edward J. Zipser,et al.  Cumulonimbus Vertical Velocity Events in GATE. Part II: Synthesis and Model Core Structure , 1980 .