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Title: A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E

Abstract

Abstract. Mass–dimension (mD) relationships determining bulk microphysical properties such as total water content (TWC) and radar reflectivity factor (Z) from particle size distributions are used in both numerical models and remote sensing retrievals. The a and b coefficients representing m=aDb relationships, however, can vary significantly depending on meteorological conditions, particle habits, the definition of particle maximum dimension, the probes used to obtain the data, techniques used to process the cloud probe data, and other unknown reasons. Thus, considering a range of a,b coefficients may be more applicable for use in numerical models and remote sensing retrievals. Microphysical data collected by two-dimensional optical array probes (OAPs) installed on the University of North Dakota (UND) Citation aircraft during the Mid-latitude Continental Convective Clouds Experiment (MC3E) were used in conjunction with TWC data from a Nevzorov probe and ground-based S-band radar data to determine a and b using a technique that minimizes the chi-square difference between the TWC and Z derived from the OAPs and those directly measured by a TWC probe and radar. All a and b values within a specified tolerance were regarded as equally plausible solutions. Of the 16 near-constant-temperature flight legs analyzed during the 25 April, 20 May, and 23 May 2011 events,more » the derived surfaces of solutions on the first 2 days where the aircraft-sampled stratiform cloud had a larger range in a and b for lower temperature environments that correspond to less variability in N(D), TWC, and Z for a flight leg. Because different regions of the storm were sampled on 23 May, differences in the variability in N(D), TWC, and Z influenced the distribution of chi-square values in the (a,b) phase space and the specified tolerance in a way that yielded 2.8 times fewer plausible solutions compared to the flight legs on the other dates. These findings show the importance of representing the variability in a,b coefficients for numerical modeling and remote sensing studies, rather than assuming fixed values, as well as the need to further explore how these surfaces depend on environmental conditions in clouds containing ice hydrometeors.« less

Authors:
ORCiD logo; ; ; ; ; ORCiD logo;
Publication Date:
Research Org.:
Univ. of Illinois at Urbana-Champaign, IL (United States); University Corporation for Atmospheric Research, Boulder, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1502267
Alternate Identifier(s):
OSTI ID: 1612069
Grant/Contract Number:  
SC0016476; SC0014065
Resource Type:
Published Article
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Name: Atmospheric Chemistry and Physics (Online) Journal Volume: 19 Journal Issue: 6; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Country of Publication:
Germany
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences

Citation Formats

Finlon, Joseph A., McFarquhar, Greg M., Nesbitt, Stephen W., Rauber, Robert M., Morrison, Hugh, Wu, Wei, and Zhang, Pengfei. A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E. Germany: N. p., 2019. Web. doi:10.5194/acp-19-3621-2019.
Finlon, Joseph A., McFarquhar, Greg M., Nesbitt, Stephen W., Rauber, Robert M., Morrison, Hugh, Wu, Wei, & Zhang, Pengfei. A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E. Germany. doi:10.5194/acp-19-3621-2019.
Finlon, Joseph A., McFarquhar, Greg M., Nesbitt, Stephen W., Rauber, Robert M., Morrison, Hugh, Wu, Wei, and Zhang, Pengfei. Thu . "A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E". Germany. doi:10.5194/acp-19-3621-2019.
@article{osti_1502267,
title = {A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E},
author = {Finlon, Joseph A. and McFarquhar, Greg M. and Nesbitt, Stephen W. and Rauber, Robert M. and Morrison, Hugh and Wu, Wei and Zhang, Pengfei},
abstractNote = {Abstract. Mass–dimension (m–D) relationships determining bulk microphysical properties such as total water content (TWC) and radar reflectivity factor (Z) from particle size distributions are used in both numerical models and remote sensing retrievals. The a and b coefficients representing m=aDb relationships, however, can vary significantly depending on meteorological conditions, particle habits, the definition of particle maximum dimension, the probes used to obtain the data, techniques used to process the cloud probe data, and other unknown reasons. Thus, considering a range of a,b coefficients may be more applicable for use in numerical models and remote sensing retrievals. Microphysical data collected by two-dimensional optical array probes (OAPs) installed on the University of North Dakota (UND) Citation aircraft during the Mid-latitude Continental Convective Clouds Experiment (MC3E) were used in conjunction with TWC data from a Nevzorov probe and ground-based S-band radar data to determine a and b using a technique that minimizes the chi-square difference between the TWC and Z derived from the OAPs and those directly measured by a TWC probe and radar. All a and b values within a specified tolerance were regarded as equally plausible solutions. Of the 16 near-constant-temperature flight legs analyzed during the 25 April, 20 May, and 23 May 2011 events, the derived surfaces of solutions on the first 2 days where the aircraft-sampled stratiform cloud had a larger range in a and b for lower temperature environments that correspond to less variability in N(D), TWC, and Z for a flight leg. Because different regions of the storm were sampled on 23 May, differences in the variability in N(D), TWC, and Z influenced the distribution of chi-square values in the (a,b) phase space and the specified tolerance in a way that yielded 2.8 times fewer plausible solutions compared to the flight legs on the other dates. These findings show the importance of representing the variability in a,b coefficients for numerical modeling and remote sensing studies, rather than assuming fixed values, as well as the need to further explore how these surfaces depend on environmental conditions in clouds containing ice hydrometeors.},
doi = {10.5194/acp-19-3621-2019},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 6,
volume = 19,
place = {Germany},
year = {2019},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.5194/acp-19-3621-2019

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Cited by: 4 works
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Works referenced in this record:

Improved Representation of Ice Particle Masses Based on Observations in Natural Clouds
journal, October 2010

  • Heymsfield, Andrew J.; Schmitt, Carl; Bansemer, Aaron
  • Journal of the Atmospheric Sciences, Vol. 67, Issue 10
  • DOI: 10.1175/2010JAS3507.1

Comparison of Ice-Phase Microphysical Parameterization Schemes Using Numerical Simulations of Tropical Convection
journal, July 1991

  • McCumber, Michale; Tao, Wei-Kuo; Simpson, Joanne
  • Journal of Applied Meteorology, Vol. 30, Issue 7
  • DOI: 10.1175/1520-0450-30.7.985

Ice Particle Mass–Dimensional Relationship Retrieval and Uncertainty Evaluation Using the Optimal Estimation Methodology Applied to the MACPEX Data
journal, March 2017

  • Xu, Zhuocan; Mace, Gerald G.
  • Journal of Applied Meteorology and Climatology, Vol. 56, Issue 3
  • DOI: 10.1175/JAMC-D-16-0222.1

A Comparison of X-Band Polarization Parameters with In Situ Microphysical Measurements in the Comma Head of Two Winter Cyclones
journal, December 2016

  • Finlon, Joseph A.; McFarquhar, Greg M.; Rauber, Robert M.
  • Journal of Applied Meteorology and Climatology, Vol. 55, Issue 12
  • DOI: 10.1175/JAMC-D-16-0059.1

Assessment of Radar Signal Attenuation Caused by the Melting Hydrometeor Layer
journal, April 2008


The effective density of small ice particles obtained from in situ aircraft observations of mid-latitude cirrus : Effective Density of Small Ice Particles
journal, November 2012

  • Cotton, R. J.; Field, P. R.; Ulanowski, Z.
  • Quarterly Journal of the Royal Meteorological Society, Vol. 139, Issue 676
  • DOI: 10.1002/qj.2058

Refinements to Ice Particle Mass Dimensional and Terminal Velocity Relationships for Ice Clouds. Part I: Temperature Dependence
journal, April 2007

  • Heymsfield, Andrew J.; Bansemer, Aaron; Twohy, Cynthia H.
  • Journal of the Atmospheric Sciences, Vol. 64, Issue 4
  • DOI: 10.1175/JAS3890.1

Modeling Backscatter Properties of Snowfall at Millimeter Wavelengths
journal, May 2007

  • Matrosov, Sergey Y.
  • Journal of the Atmospheric Sciences, Vol. 64, Issue 5
  • DOI: 10.1175/JAS3904.1

Small Ice Particles in Tropospheric Clouds: Fact or Artifact? Airborne Icing Instrumentation Evaluation Experiment
journal, August 2011

  • Korolev, A. V.; Emery, E. F.; Strapp, J. W.
  • Bulletin of the American Meteorological Society, Vol. 92, Issue 8
  • DOI: 10.1175/2010BAMS3141.1

Investigation of ice cloud microphysical properties of DCSs using aircraft in situ measurements during MC3E over the ARM SGP site: Microphysical properties of DCS
journal, April 2015

  • Wang, Jingyu; Dong, Xiquan; Xi, Baike
  • Journal of Geophysical Research: Atmospheres, Vol. 120, Issue 8
  • DOI: 10.1002/2014JD022795

The Microphysics of Ice and Precipitation Development in Tropical Cumulus Clouds
journal, June 2015

  • Lawson, R. Paul; Woods, Sarah; Morrison, Hugh
  • Journal of the Atmospheric Sciences, Vol. 72, Issue 6
  • DOI: 10.1175/JAS-D-14-0274.1

Constraining mass–diameter relations from hydrometeor images and cloud radar reflectivities in tropical continental and oceanic convective anvils
journal, January 2014

  • Fontaine, E.; Schwarzenboeck, A.; Delanoë, J.
  • Atmospheric Chemistry and Physics, Vol. 14, Issue 20
  • DOI: 10.5194/acp-14-11367-2014

The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language
journal, July 2016

  • Helmus, Jonathan J.; Collis, Scott M.
  • Journal of Open Research Software, Vol. 4
  • DOI: 10.5334/jors.119

Radar Scattering from Ice Aggregates Using the Horizontally Aligned Oblate Spheroid Approximation
journal, March 2012

  • Hogan, Robin J.; Tian, Lin; Brown, Philip R. A.
  • Journal of Applied Meteorology and Climatology, Vol. 51, Issue 3
  • DOI: 10.1175/JAMC-D-11-074.1

Partial Beam Blockage Correction Using Polarimetric Radar Measurements
journal, May 2013

  • Zhang, P.; Zrnić, D.; Ryzhkov, A.
  • Journal of Atmospheric and Oceanic Technology, Vol. 30, Issue 5
  • DOI: 10.1175/JTECH-D-12-00075.1

On the Impacts of Different Definitions of Maximum Dimension for Nonspherical Particles Recorded by 2D Imaging Probes
journal, May 2016

  • Wu, Wei; McFarquhar, Greg M.
  • Journal of Atmospheric and Oceanic Technology, Vol. 33, Issue 5
  • DOI: 10.1175/JTECH-D-15-0177.1

Developing and bounding ice particle mass- and area-dimension expressions for use in atmospheric models and remote sensing
journal, January 2016


Backscatter characteristics of nonspherical ice crystals: Assessing the potential of polarimetric radar measurements
journal, December 1999

  • Lemke, Henriette M.; Quante, Markus
  • Journal of Geophysical Research: Atmospheres, Vol. 104, Issue D24
  • DOI: 10.1029/1999JD900490

Modification and Tests of Particle Probe Tips to Mitigate Effects of Ice Shattering
journal, April 2013

  • Korolev, Alexei; Emery, Edward; Creelman, Kirk
  • Journal of Atmospheric and Oceanic Technology, Vol. 30, Issue 4
  • DOI: 10.1175/JTECH-D-12-00142.1

Improved Airborne Hot-Wire Measurements of Ice Water Content in Clouds
journal, September 2013

  • Korolev, A.; Strapp, J. W.; Isaac, G. A.
  • Journal of Atmospheric and Oceanic Technology, Vol. 30, Issue 9
  • DOI: 10.1175/JTECH-D-13-00007.1

The Mass-Dimensional Properties of Cirrus Clouds During TC4: TC4 Mass-Dimensional Properties
journal, October 2017

  • Mascio, Jeana; Xu, Zhuocan; Mace, Gerald G.
  • Journal of Geophysical Research: Atmospheres, Vol. 122, Issue 19
  • DOI: 10.1002/2017JD026787

Shattering and Particle Interarrival Times Measured by Optical Array Probes in Ice Clouds
journal, October 2006

  • Field, P. R.; Heymsfield, A. J.; Bansemer, A.
  • Journal of Atmospheric and Oceanic Technology, Vol. 23, Issue 10
  • DOI: 10.1175/JTECH1922.1

Snow Studies. Part II: Average Relationship between Mass of Snowflakes and Their Terminal Fall Velocity
journal, October 2010

  • Szyrmer, Wanda; Zawadzki, Isztar
  • Journal of the Atmospheric Sciences, Vol. 67, Issue 10
  • DOI: 10.1175/2010JAS3390.1

Ice Cloud Particle Size Distributions and Pressure-Dependent Terminal Velocities from In Situ Observations at Temperatures from 0° to −86°C
journal, December 2013

  • Heymsfield, Andrew J.; Schmitt, Carl; Bansemer, Aaron
  • Journal of the Atmospheric Sciences, Vol. 70, Issue 12
  • DOI: 10.1175/JAS-D-12-0124.1

Ice Crystal Sizes in High Ice Water Content Clouds. Part I: On the Computation of Median Mass Diameter from In Situ Measurements
journal, November 2016

  • Leroy, D.; Fontaine, E.; Schwarzenboeck, A.
  • Journal of Atmospheric and Oceanic Technology, Vol. 33, Issue 11
  • DOI: 10.1175/JTECH-D-15-0151.1

Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 1. Observations
journal, January 2007

  • McFarquhar, Greg M.; Zhang, Gong; Poellot, Michael R.
  • Journal of Geophysical Research, Vol. 112, Issue D24
  • DOI: 10.1029/2007JD008633

The Midlatitude Continental Convective Clouds Experiment (MC3E)
journal, September 2016

  • Jensen, M. P.; Petersen, W. A.; Bansemer, A.
  • Bulletin of the American Meteorological Society, Vol. 97, Issue 9
  • DOI: 10.1175/BAMS-D-14-00228.1

The Dimensional Characteristics of Ice Crystal Aggregates from Fractal Geometry
journal, May 2010

  • Schmitt, C. G.; Heymsfield, A. J.
  • Journal of the Atmospheric Sciences, Vol. 67, Issue 5
  • DOI: 10.1175/2009JAS3187.1

The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer and In Situ Observations
journal, March 2016

  • Olson, William S.; Tian, Lin; Grecu, Mircea
  • Journal of Applied Meteorology and Climatology, Vol. 55, Issue 3
  • DOI: 10.1175/JAMC-D-15-0131.1

Developing and Evaluating Ice Cloud Parameterizations for Forward Modeling of Radar Moments Using in situ Aircraft Observations
journal, May 2015

  • Maahn, Maximilian; Löhnert, Ulrich; Kollias, Pavlos
  • Journal of Atmospheric and Oceanic Technology, Vol. 32, Issue 5
  • DOI: 10.1175/JTECH-D-14-00112.1

An Assessment of the Impact of Antishattering Tips and Artifact Removal Techniques on Cloud Ice Size Distributions Measured by the 2D Cloud Probe
journal, December 2014

  • Jackson, Robert C.; McFarquhar, Greg M.; Stith, Jeff
  • Journal of Atmospheric and Oceanic Technology, Vol. 31, Issue 12
  • DOI: 10.1175/JTECH-D-13-00239.1

Assessment of the performance of the inter-arrival time algorithm to identify ice shattering artifacts in cloud particle probe measurements
journal, January 2015


Correction of Radar Reflectivity and Differential Reflectivity for Rain Attenuation at X Band. Part I: Theoretical and Empirical Basis
journal, November 2005

  • Park, S-G.; Bringi, V. N.; Chandrasekar, V.
  • Journal of Atmospheric and Oceanic Technology, Vol. 22, Issue 11
  • DOI: 10.1175/JTECH1803.1

Vertical Variability of Cloud Hydrometeors in the Stratiform Region of Mesoscale Convective Systems and Bow Echoes
journal, October 2007

  • McFarquhar, Greg M.; Timlin, Michael S.; Rauber, Robert M.
  • Monthly Weather Review, Vol. 135, Issue 10
  • DOI: 10.1175/MWR3444.1

Processing of Ice Cloud In Situ Data Collected by Bulk Water, Scattering, and Imaging Probes: Fundamentals, Uncertainties, and Efforts toward Consistency
journal, January 2017


Ice in Clouds Experiment—Layer Clouds. Part I: Ice Growth Rates Derived from Lenticular Wave Cloud Penetrations
journal, November 2011

  • Heymsfield, Andrew J.; Field, Paul R.; Bailey, Matt
  • Journal of the Atmospheric Sciences, Vol. 68, Issue 11
  • DOI: 10.1175/JAS-D-11-025.1

The Retrieval of Ice Water Content from Radar Reflectivity Factor and Temperature and Its Use in Evaluating a Mesoscale Model
journal, February 2006

  • Hogan, Robin J.; Mittermaier, Marion P.; Illingworth, Anthony J.
  • Journal of Applied Meteorology and Climatology, Vol. 45, Issue 2
  • DOI: 10.1175/JAM2340.1

Fall speeds and masses of solid precipitation particles
journal, May 1974

  • Locatelli, John D.; Hobbs, Peter V.
  • Journal of Geophysical Research, Vol. 79, Issue 15
  • DOI: 10.1029/JC079i015p02185

Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds
journal, January 2010

  • Delanoë, Julien; Hogan, Robin J.
  • Journal of Geophysical Research, Vol. 115
  • DOI: 10.1029/2009JD012346