Topic #6: Ice/liquid mass partitioning in mixed phase cloud Co-leaders Greg McFarquhar (in-situ) Johannes Bühl (remote sensing) Participants In-situ: Bundke,

Slides:



Advertisements
Similar presentations
Ewan OConnor, Anthony Illingworth, Robin Hogan and the Cloudnet team Cloudnet.
Advertisements

Ewan OConnor, Robin Hogan, Anthony Illingworth Drizzle comparisons.
Ewan OConnor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat Radar/lidar observations of boundary layer clouds.
Radar/lidar observations of boundary layer clouds
Robin Hogan Department of Meteorology University of Reading Cloud and Climate Studies using the Chilbolton Observatory.
Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.
Modelling radar and lidar multiple scattering Modelling radar and lidar multiple scattering Robin Hogan The CloudSat radar and the Calipso lidar were launched.
Integrated Profiling at the AMF
Simulating cloud-microphysical processes in CRCM5 Ping Du, Éric Girard, Jean-Pierre Blanchet.
Steven Siems 1 and Greg McFarquhar 2 1 Monash University, Melbourne, VIC, Australia 2 University of Illinois, Urbana, IL, USA Steven Siems 1 and Greg McFarquhar.
Equation for the microwave backscatter cross section of aggregate snowflakes using the Self-Similar Rayleigh- Gans Approximation Robin Hogan ECMWF and.
Remote-sensing of the environment (RSE) ATMOS Design of a radar control and signal processor unit for the TARA radar BAP afstudeerproject Yann Dufournet.
1. The problem of mixed-phase clouds All models except DWD underestimate mid-level cloud –Some have separate “radiatively inactive” snow (ECMWF, DWD) –Met.
Remote sensing of Stratocumulus using radar/lidar synergy Ewan O’Connor, Anthony Illingworth & Robin Hogan University of Reading.
Impact of surface interaction and cloud seeding on orographic snowfall A downlooking airborne cloud radar view Bart Geerts University of Wyoming Gabor.
NATS 101 Lecture 13 Precipitation Processes. Supplemental References for Today’s Lecture Danielson, E. W., J. Levin and E. Abrams, 1998: Meteorology.
Millimeter and sub-millimeter observations for Earth cloud hunting Catherine Prigent, LERMA, Observatoire de Paris.
Bredbeck Workshop, 7 – 10 July 2003 Jörg Schulz Meteorological Institute, University of Bonn Harald Czekala RPG Radiometer.
Observational approaches to understanding cloud microphysics.
IRCTR - International Research Centre for Telecommunication and Radar ATMOS Ice crystals properties retrieval within ice and mixed-phase clouds using the.
Observed and modelled long-term water cloud statistics for the Murg Valley Kerstin Ebell, Susanne Crewell, Ulrich Löhnert Institute for Geophysics and.
Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
Remote-sensing of the environment (RSE) ATMOS Analysis of the Composition of Clouds with Extended Polarization Techniques L. Pfitzenmaier, H. Russchenbergs.
Photo courtesy of Paul Lawson/J.H. Bain An Overview of Cirrus Cloud Thinning and Determining Its Scientific Feasibility David L. Mitchell Desert Research.
Characterization of Arctic Mixed-Phase Cloudy Boundary Layers with the Adiabatic Assumption Paquita Zuidema*, Janet Intrieri, Sergey Matrosov, Matthew.
ISCCP at 30. Influence of aerosols on mesoscale convective systems inferred from ISCCP and A-Train datasets Rong Fu & Sudip Chakraborty Jackson School.
Topic 11 New Sensors, Platforms, Analysis Technique and Systems for Measuring Ice Cloud Properties Workshop on Measurement Problems in Ice Clouds Zurich,
NARVAL South Lutz Hirsch, Friedhelm Jansen Sensor Synergy While Radars and Lidars provide excellent spatial resolution but only ambiguous information on.
Matthew Shupe, Ola Persson, Amy Solomon CIRES – Univ. of Colorado & NOAA/ESRL David Turner NOAA/NSSL Dynamical and Microphysical Characteristics and Interactions.
Dual-Polarization and Dual-Wavelength Radar Measurements Vivek National Center for Atmospheric Research Boulder, Colorado I.Polarization and dual- wavelength.
Topic 7: remote sensing of cloud particles and properties; validation etc.
RICO Modeling Studies Group interests RICO data in support of studies.
Lidar+Radar ice cloud remote sensing within CLOUDNET. D.Donovan, G-J Zadelhof (KNMI) and the CLOUDNET team With outside contributions from… Z. Wang (NASA/GSFC)
Group proposal Aerosol, Cloud, and Climate ( EAS 8802) April 24 th, 2006 Does Asian dust play a role as CCN? Gill-Ran Jeong, Lance Giles, Matthew Widlansky.
Robin Hogan Ewan O’Connor The Instrument Synergy/ Target Categorization product.
Matthew Shupe Ola Persson Paul Johnston Duane Hazen Clouds during ASCOS U. of Colorado and NOAA.
Radiometer Physics GmbH
The University of Reading Helen Dacre The Eyjafjallajökull eruption: How well were the volcanic ash clouds predicted? Helen Dacre and Alan Grant Robin.
1 Atmospheric profiling to better understand fog and low level cloud life cycle ARM/EU workshop on algorithms, May 2013 J. Delanoe (LATMOS), JC.
Numerical simulations of optical properties of nonspherical dust aerosols using the T-matrix method Hyung-Jin Choi School.
KNMI 35 GHz Cloud Radar & Cloud Classification* Henk Klein Baltink * Robin Hogan (Univ. of Reading, UK)
The Variation of Observed Ice Cloud Microphysics and Possible Links to the Environment Breakout:
Boundary-layer turbulence, surface processes, and orographic precipitation growth in cold clouds or: The importance of the lower boundary Qun Miao Ningbo.
OVERVIEW OF THE DATA OBTAINED DURING ASTAR 2007 (ARCTIC MIXED-PHASE CLOUDS) & CIRCLE-2 (MID-LATITUDE CIRRUS) Alfons Schwarzenboeck, Guillaume Mioche, Christophe.
Distribution of Liquid Water in Orographic Mixed-Phase Clouds Diana Thatcher Mentor: Linnea Avallone LASP REU 2011.
A study of ice formation by primary nucleation and ice multiplication in shallow precipitating embedded convection T. Choularton 1, I. Crawford 1, C. Dearden.
Ice-Phase Precipitation Remote Sensing Using Combined Passive and Active Microwave Observations Benjamin T. Johnson UMBC/JCET & NASA/GSFC (Code 613.1)
Towards a Characterization of Arctic Mixed-Phase Clouds Matthew D. Shupe a, Pavlos Kollias b, Ed Luke b a Cooperative Institute for Research in Environmental.
INUPIAQ/CLACE 2014 University of Manchester Data availability.
Retrieval of Cloud Phase and Ice Crystal Habit From Satellite Data Sally McFarlane, Roger Marchand*, and Thomas Ackerman Pacific Northwest National Laboratory.
Radiative Influences on Glaciation Time-Scales in Mixed-Phase Clouds Zachary Lebo, Nathanial Johnson, and Jerry Harrington Penn State University Acknowledgements:
Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment, Part II: Multi-layered cloud GCSS.
Horizontal Variability In Microphysical Properties of Mixed-Phase Arctic Clouds David Brown, Michael Poellot – University of North Dakota Clouds are strong.
Particle Size, Water Path, and Photon Tunneling in Water and Ice Clouds ARM STM Albuquerque Mar Sensitivity of the CAM to Small Ice Crystals.
Comparison between aircraft and A-Train observations of midlevel, mixed-phase clouds from CLEX-10/C3VP Curtis Seaman, Yoo-Jeong Noh, Thomas Vonder Haar.
Towards parameterization of cloud drop size distribution for large scale models Wei-Chun Hsieh Athanasios Nenes Image source: NCAR.
An Outline for Global Precipitation Mission Ground Validation: Building on Lessons Learned from TRMM Sandra Yuter and Robert Houze University of Washington.
The Lifecyle of a Springtime Arctic Mixed-Phase Cloudy Boundary Layer observed during SHEBA Paquita Zuidema University of Colorado/ NOAA Environmental.
MODIS, AIRS, and Midlevel Cloud Phase Shaima Nasiri CIMSS/SSEC, UW-Madison Brian Kahn Jet Propulsion Laboratory MURI Hyperspectral Workshop 7-9 June, 2005.
UNIVERSITY OF BASILICATA CNR-IMAA (Consiglio Nazionale delle Ricerche Istituto di Metodologie per l’Analisi Ambientale) Tito Scalo (PZ) Analysis and interpretation.
© Crown copyright Met Office Cloud observations at Cardington Simon Osborne (OBR, Cardington) OBR Conference, 11 th -13 th December 2012.
Simulation of the Arctic Mixed-Phase Clouds
Horizontally Oriented Ice and Precipitation in Maritime Clouds Using CloudSat, CALIOP, and MODIS Observations Alexa Ross Steve Ackerman Robert Holz University.
What are the causes of GCM biases in cloud, aerosol, and radiative properties over the Southern Ocean? How can the representation of different processes.
Group interests RICO data required
RadOn : Retrieval of microphysical and radiative properties of ice clouds from Doppler cloud radar observations J. Delanoë and A. Protat IPSL / CETP.
Radiometer Physics GmbH
Group interests RICO data in support of studies
Presentation transcript:

Topic #6: Ice/liquid mass partitioning in mixed phase cloud Co-leaders Greg McFarquhar (in-situ) Johannes Bühl (remote sensing) Participants In-situ: Bundke, Esposito, Hamilton, Henneberger Johnson, Jourdan, Krämer, Lawson, Meyer, Minikin, Schwarzenboeck, Ulanowski Remote sensing: Alexander, Bieligk, Kommpula, Maahn, Wang Modeling: Flossman, Lohmann

1. General Description of Topic Theme and Objectives of the Topic Working Group From Baumgardner et al. (2012) 1.What is the definition of a mixed-phase cloud? What is minimum ratio of LWC/IWC required to identify cloud as mixed-phase 2.What are spatial scales of mixing between liquid & ice and how do they vary with height & meteorological conditions? 3.How are liquid & ice partitioned with respect to particle sizes (e.g., are all small particles liquid & all large particles ice) 4.How can small particles be distinguished from supercooled droplets & do frozen drops evolve in shape according to condition?

2. Brief Status of Topics 2.1 In-Situ – Review of material from 2010 workshop in attached slides 2.2 Remote Sensing – See following example 2.3 Modeling – Need info

Liquid vs. ice mass partitioning possible by combination of remote sensing and in- situ measurements  Cloud radars (like the Mira36) are most not sensitive to detect falling ice particles  Lidars are best in detecting liquid (sub)layers  Lidar/Radar depolarization and terminal fall speed can be used to further classify detected particles LWC and IWC as products of CLOUDNET  LWC: Scaled adiabatic Method (assisted by Radiometer)  IWC: Parametrization from In-Situ Measurements (Hogan 2006) Remote Sensing – Example Case

LidarRadar Signal Depolarization Fall Velocity

Remote Sensing – Example Case Liquid Water Content [kg/m^3] (CLOUDNET Scaled Adiabatic) Ice Water Content [kg/m^3] (Parametrization of Hogan et. al., 2006)

3. Progress in Last 3 years 3.1 In-Situ – McFarquhar et al. (2013) analysis of CPI images of small particles in mixed- phase clouds – Aerosol effects on mixed-phase clouds during ISDAC (Jackson et al. 2012) – Tethered balloon observations (Lawson et al. 2011; Sikand et al. 2013) – Dependence of vertical profiles on meteorology (e.g., shallow vs. synoptic clouds, Noh et al. 2013) 3.2 Remote Sensing – Dual polarization radar to discriminate phase (Plummer et al. 2010) – Radar doppler spectra (Luke et al. 2010; Verlinde et al. 2013) to detect supercooled water – Liao and Meneghini (2013) dielectric constants computed for oblate and prolate spheroids 3.3 Modeling – Studies of aerosol effects on models of mixed-phase clouds (Morrison et al. 2010; Zubler et al. 2011) – Influence of ice habit on glaciation and evolution (Sulia & Harrington 2011; Avramov and Harrington 2010) – Impact of aerosols on global modeling of mixed clouds (Storelvmo et al. 2011)

4. Remaining Unknowns and Uncertainties 4.1 In-Situ – Discrimination between water/ice for smallest hydrometeors still difficult – Elimination of shattered artifacts from probes still requires care and caution – Fine spatial resolution observations required to determine spatial scale of mixing – Observations in greater range of aerosol concentrations/compositions & meteorology 4.2 Remote Sensing – Definition of “mixed-phase cloud” still unclear – Estimation of IWC/TWC or IWP/TWP should be taken into account 4.3 Modeling