The effective density of small ice particles obtained from in situ aircraft observations of mid‐latitude cirrus

The effective ice-particle density, parametrized through a mass–dimension relation, is widely used in ice microphysical schemes for weather and climate models. In this study, we use aircraft-based observations in mid-latitude cirrus taken during the Constrain field programme in 2010. The low temperatures and a humidity often close to ice saturation meant that the typical ice particles observed were small (maximum dimension 20–800 µm) and ice water contents were low (0.001–0.05 g m−3). Two new instruments are included in this study: the Small Ice Detector Mark-2 (SID-2) and the deep-cone Nevzorov Total Water Content probe. SID-2 is a new single-particle light-scattering instrument and was used to identify and size small ice particles (10–150 µm). The deep-cone Nevzorov probe is shown to be able to collect small ice masses with sufficient sensitivity. The focus of this article is on the effective density of small ice particles (both pristine ice crystals and small aggregates up to 600 µm maximum dimension). Due to instrument limitations in previous studies, the effective density of small ice particles is questionable. Aircraft flights in six cirrus cases provided ice-particle measurements throughout the depth of the cirrus. The particle size distribution (PSD) was mostly bimodal. The smaller ice-crystal mode dominated the PSD near cloud top and the larger ice-aggregate mode dominated near cloud base. A mass–dimension relation valid for both ice crystals and aggregates was found that provided a best fit to the observations. For small ice particles (less than 70 µm diameter) the density is constant (700 kg m−3), while for larger ice crystals or aggregates the mass–dimension relation is m(D) = 0.0257D2.0. These measurements allow testing of the diagnostic split between ice crystals and aggregates used in the Met Office Unified Model.

[1]  Robert P. d'Entremont,et al.  Inferring Cirrus Size Distributions Through Satellite Remote Sensing and Microphysical Databases , 2010 .

[2]  Robert G. Knollenberg,et al.  The Optical Array: An Alternative to Scattering or Extinction for Airborne Particle Size Determination , 1970 .

[3]  Peter V. Hobbs,et al.  Fall speeds and masses of solid precipitation particles , 1974 .

[4]  P. Field,et al.  Shattering and Particle Interarrival Times Measured by Optical Array Probes in Ice Clouds , 2006 .

[5]  R. Lawson Effects of ice particles shattering on the 2D-S probe , 2011 .

[6]  P. Kaye,et al.  Light scattering by complex ice-analogue crystals , 2006 .

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

[8]  Andrew J. Heymsfield,et al.  A Computational Technique for Increasing the Effective Sampling Volume of the PMS Two-Dimensional Particle Size Spectrometer , 1978 .

[9]  D. Bouniol,et al.  Statistical properties of the normalized ice particle size distribution , 2005 .

[10]  Paul H. Kaye,et al.  Experimental and theoretical light scattering profiles from spherical and nonspherical particles , 1996 .

[11]  P. Francis,et al.  Improved Measurements of the Ice Water Content in Cirrus Using a Total-Water Probe , 1995 .

[12]  A. Korolev,et al.  Evaluation of the Accuracy of PMS Optical Array Probes , 1998 .

[13]  R. Rasmussen,et al.  Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model , 1998 .

[14]  A. Bodas‐Salcedo,et al.  Simulating the equivalent radar reflectivity of cirrus at 94 GHz using an ensemble model of cirrus ice crystals: a test of the Met Office global numerical weather prediction model , 2011 .

[15]  A. Korolev,et al.  The Nevzorov Airborne Hot-Wire LWC-TWC Probe: Principle of Operation and Performance Characteristics , 1998 .

[16]  R. Lawson,et al.  The 2D-S (Stereo) Probe: Design and Preliminary Tests of a New Airborne, High-Speed, High-Resolution Particle Imaging Probe , 2006 .

[17]  Damian R. Wilson,et al.  Use of an explicit model of the microphysics of precipitating stratiform cloud to test a bulk microphysics scheme , 2002 .

[18]  Hugh Swann,et al.  Sensitivity to the representation of precipitating ice in CRM simulations of deep convection , 1998 .

[19]  P. Kaye,et al.  Classifying atmospheric ice crystals by spatial light scattering. , 2008, Optics Letters.

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

[21]  G. Cox Modelling precipitation in frontal rainbands , 2006 .

[22]  Andrew J. Heymsfield,et al.  Improved Representation of Ice Particle Masses Based on Observations in Natural Clouds , 2010 .

[23]  Brad Baker,et al.  Improvement in Determination of Ice Water Content from Two-Dimensional Particle Imagery. Part I: Image-to-Mass Relationships , 2006 .

[24]  J. Walter Strapp,et al.  Laboratory Measurements of the Response of a PMS OAP-2DC , 2001 .

[25]  R. C. Ball,et al.  Universality in snowflake aggregation , 2003 .

[26]  P. Brown Measurements of the Ice Water Content in Cirrus Using an Evaporative Technique , 1993 .

[27]  Andrew J. Heymsfield,et al.  Effective Ice Particle Densities Derived from Aircraft Data , 2004 .

[28]  Robert Wood,et al.  Drizzle in Stratiform Boundary Layer Clouds. Part II: Microphysical Aspects. , 2005 .

[29]  George A. Isaac,et al.  Assessing the Collection Efficiency of Natural Cloud Particles Impacting the Nevzorov Total Water Content Probe , 2006 .

[30]  P. Kaye,et al.  The Ability of the Small Ice Detector (SID-2) to Characterize Cloud Particle and Aerosol Morphologies Obtained during Flights of the FAAM BAe-146 Research Aircraft , 2010 .

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

[32]  P. Field Bimodal ice spectra in frontal clouds , 2000 .

[33]  A. Korolev,et al.  Improved Airborne Hot-Wire Measurements of Ice Water Content in Clouds , 2013 .