Bryan A. Baum 1 Ping Yang 2, Andrew Heymsfield 3 1 NASA Langley Research Center, Hampton, VA 2 Texas A&M University, College Station, TX 3 National Center.

Slides:



Advertisements
Similar presentations
Robin Hogan, Chris Westbrook University of Reading Lin Tian NASA Goddard Space Flight Center Phil Brown Met Office Why it is important that ice particles.
Advertisements

Robin Hogan, Chris Westbrook University of Reading Lin Tian NASA Goddard Space Flight Center Phil Brown Met Office The importance of ice particle shape.
Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.
Enhancement of Satellite-based Precipitation Estimates using the Information from the Proposed Advanced Baseline Imager (ABI) Part II: Drizzle Detection.
Retrieving Cloud Properties for Multilayered Clouds Using Simulated GOES-R Data Fu-Lung Chang 1, Patrick Minnis 2, Bing Lin 2, Rabindra Palikonda 3, Mandana.
1 1. FY08 GOES-R3 Project Proposal Title Page  Title: Investigation of Daytime-Nighttime Inconsistencies in Cloud Optical Parameters  Project Type: Product.
1 START08 Heymsfield/Avallone UTLS PSDs Stratospheric PSDs Population-mean ice crystal densities Crystal shape information (SID-2) Extinction estimates.
3D Radiative Transfer in Cloudy Atmospheres: Diffusion Approximation and Monte Carlo Simulation for Thermal Emission K. N. Liou, Y. Chen, and Y. Gu Department.
Recent Studies in Ice Cloud Models Bryan Baum 1, Ping Yang 2, Andy Heymsfield 3, Carl Schmitt 3, Aaron Bansemer 3, Yu Xie 2, Yong-Xiang Hu 4, Zhibo Zhang.
Analysis of ARM cirrus data and the incorporation of Doppler Fall-velocity measurements in lidar/radar retrievals Outline of lidar/radar procedure. Problem.
Equation for the microwave backscatter cross section of aggregate snowflakes using the Self-Similar Rayleigh- Gans Approximation Robin Hogan ECMWF and.
A 21 F A 21 F Parameterization of Aerosol and Cirrus Cloud Effects on Reflected Sunlight Spectra Measured From Space: Application of the.
ARM Atmospheric Radiation Measurement Program. 2 Improve the performance of general circulation models (GCMs) used for climate research and prediction.
Observational approaches to understanding cloud microphysics.
© Crown copyright Met Office Electromagnetic and light scattering by atmospheric particulates: How well does theory compare against observation? Anthony.
IRCTR - International Research Centre for Telecommunication and Radar ATMOS Ice crystals properties retrieval within ice and mixed-phase clouds using the.
- 1 - A Simple Parameterization For Mid-latitude Cirrus Cloud Ice Particle Size Spectra And Ice Sedimentation Rates David L. Mitchell Desert Research Institute,
University of Wisconsin - Madison (UW) University of Hawaii (UH) Texas A& M (TAMU) University of Colorado at Boulder (CU) University of Alabama in Huntsville.
Photo courtesy of Paul Lawson/J.H. Bain An Overview of Cirrus Cloud Thinning and Determining Its Scientific Feasibility David L. Mitchell Desert Research.
Bryan A. Baum 1, Ping Yang 2, Andrew J. Heymsfield 3, and Sarah Thomas 4 1 NASA Langley Research Center, Hampton, VA 2 Texas A&M University, College Station,
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
Bastiaan van Diedenhoven (Columbia University, NASA GISS) Ann Fridlind, Andrew Ackerman & Brian Cairns (NASA GISS) An investigation of ice crystal sizes.
Summary of Modifications for C6 Ice Cloud Models Bryan Baum, Ping Yang, Andy Heymsfield Microphysical data: a. more field campaigns (ARM, TRMM, SCOUT,
1 Cloud microphysical properties in cirrus during ATTREX Sarah Woods 1, Sara Lance 1, Eric Jensen 2, Nick Krause 1, R. Paul Lawson 1, Bruce Gandrud 1,
University of Wisconsin - Madison (UW) University of Hawaii (UH) Texas A& M (TAMU) University of Colorado at Boulder (CU) University of Alabama in Huntsville.
Rick Russotto Dept. of Atmospheric Sciences, Univ. of Washington With: Tom Ackerman Dale Durran ATTREX Science Team Meeting Boulder, CO October 21, 2014.
FIM Cloud Visualization for SOS and TerraViz Steve Albers.
Page 1© Crown copyright 2004 Cirrus Measurements during the EAQUATE Campaign C. Lee, A.J. Baran, P.N. Francis, M.D. Glew, S.M. Newman and J.P. Taylor.
Trace gas and AOD retrievals from a newly deployed hyper-spectral airborne sun/sky photometer (4STAR) M. Segal-Rosenheimer, C.J. Flynn, J. Redemann, B.
LMD LMD Science Team CALIPSO – March M.Chiriaco, H.Chepfer, V.Noel, A.Delaval, M.Haeffelin Laboratoire de Météorologie Dynamique, IPSL, France P.Yang,
Louie Grasso Daniel Lindsey A TECHNIQUE FOR COMPUTING HYDROMETEOR EFFECITVE RADIUS IN BINS OF A GAMMA DISTRIBUTION M anajit Sengupta INTRODUCTION As part.
Dual-Polarization and Dual-Wavelength Radar Measurements Vivek National Center for Atmospheric Research Boulder, Colorado I.Polarization and dual- wavelength.
Optical properties Satellite observation ? T,H 2 O… From dust microphysical properties to dust hyperspectral infrared remote sensing Clémence Pierangelo.
Evaluating Cloud Microphysics Schemes in the WRF Model Fifth Meeting of the Science Advisory Committee November, 2009 Andrew Molthan transitioning.
The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry.
Characterization of Aerosols using Airborne Lidar, MODIS, and GOCART Data during the TRACE-P (2001) Mission Rich Ferrare 1, Ed Browell 1, Syed Ismail 1,
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Using CALIPSO to Explore the Sensitivity to Cirrus Height in the Infrared.
Improvement of Cold Season Land Precipitation Retrievals Through The Use of Field Campaign Data and High Frequency Microwave Radiative Transfer Model IPWG.
Cloud optical properties: modeling and sensitivity study Ping Yang Texas A&M University May 28,2003 Madison, Wisconsin.
1 CIMSS/SSEC Effort on the Fast IR Cloudy Forward Model Development A Fast Parameterized Single Layer Infrared Cloudy Forward Model Status and Features.
BBHRP Assessment Part 2: Cirrus Radiative Flux Study Using Radar/Lidar/AERI Derived Cloud Properties David Tobin, Lori Borg, David Turner, Robert Holz,
Retrieval of Cloud Phase and Ice Crystal Habit From Satellite Data Sally McFarlane, Roger Marchand*, and Thomas Ackerman Pacific Northwest National Laboratory.
X X X Cloud Variables Top pressure Cloud type Effective radius
Zhibo (zippo) Zhang 03/29/2010 ESSIC
MODIS Collection 6 Ice Model Assessments with POLDER and CALIPSO Ping Yang, Souichiro Hioki, Jiachen Ding Department of Atmospheric Sciences Texas A&M.
Bryan A. Baum, Richard Frey, Robert Holz Space Science and Engineering Center University of Wisconsin-Madison Paul Menzel NOAA Many other colleagues MODIS.
CIMSS Forward Model Capability to Support GOES-R Measurement Simulations Tom Greenwald, Hung-Lung (Allen) Huang, Dave Tobin, Ping Yang*, Leslie Moy, Erik.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Particle Size, Water Path, and Photon Tunneling in Water and Ice Clouds ARM STM Albuquerque Mar Sensitivity of the CAM to Small Ice Crystals.
Cloud Object Analysis and Modeling of Cloud-Aerosol Interactions and Cloud Feedbacks with the Combined CERES and CALIPSO Data Kuan-Man Xu NASA Langley.
UCLA Vector Radiative Transfer Models for Application to Satellite Data Assimilation K. N. Liou, S. C. Ou, Y. Takano and Q. Yue Department of Atmospheric.
- 1 - Satellite Remote Sensing of Small Ice Crystal Concentrations in Cirrus Clouds David L. Mitchell Desert Research Institute, Reno, Nevada Robert P.
1 Recent advances in CALIPSO cloud retrievals: Progress over the last 7 years Looking ahead to the next 30 ISCCP at 30: City College of New York, 23 April.
1 UW MURI Physical Modeling For Processing Hyperspectral Data – Non-UW Co-Is’ Progress Allen Huang, PI Co-Is: Paul L. HIGP, Univ. of Hawaii Ping Y., Univ.
1 Hyperspectral IR Cloudy Fast Forward Model J. E. Davies, X. Wang, E. R. Olson, J. A. Otkin, H-L. Huang, Ping Yang #, Heli Wei #, Jianguo Niu # and David.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
Properties of Tropical Ice Clouds: Analyses Based on Terra/Aqua Measurements P. Yang, G. Hong, K. Meyer, G. North, A. Dessler Texas A&M University B.-C.
The study of cloud and aerosol properties during CalNex using newly developed spectral methods Patrick J. McBride, Samuel LeBlanc, K. Sebastian Schmidt,
Developing Winter Precipitation Algorithm over Land from Satellite Microwave and C3VP Field Campaign Observations Fifth Workshop of the International Precipitation.
What Are the Implications of Optical Closure Using Measurements from the Two Column Aerosol Project? J.D. Fast 1, L.K. Berg 1, E. Kassianov 1, D. Chand.
1 Synthetic Hyperspectral Radiances for Retrieval Algorithm Development J. E. Davies, J. A. Otkin, E. R. Olson, X. Wang, H-L. Huang, Ping Yang # and Jianguo.
Visible vicarious calibration using RTM
A-Train Symposium, April 19-21, 2017, Pasadena, CA
produced by SBDART model for COMS visible calibration
Microwave Remote Sensing
Michael J. Jun Li#, Daniel K. Zhou%, and Timothy J.
Aircraft PSD Studies Using UND Citation Data
A Unified Radiative Transfer Model: Microwave to Infrared
Dual-Aircraft Investigation of the Inner Core of Hurricane Nobert
Sensitivity of idealized squall-line simulations to the level of complexity used in two-moment bulk microphysics schemes. Speaker: Huan Chen Professor:
Presentation transcript:

Bryan A. Baum 1 Ping Yang 2, Andrew Heymsfield 3 1 NASA Langley Research Center, Hampton, VA 2 Texas A&M University, College Station, TX 3 National Center for Atmospheric Research, Boulder, CO Goal: Provide ice cloud bulk scattering models that are developed consistently for suite of multispectral and hyperspectral instruments Hyperspectral/Multispectral Ice Cloud Microphysical and Optical Models 5th Workshop on Hyperspectral Science June 7-9, 2005

Bulk Scattering Models Operational satellite-based cloud retrievals require the ability to simulate realistic ice cloud radiances quickly Simulations require bulk scattering parameters such as single-scattering albedo scattering phase function or asymmetry factor extinction efficiency or scattering/absorption cross sections Scattering parameters in turn are based on the cloud phase (ice, water, or mixed) particle size distribution particle habit distribution wavelength(s) Have libraries of scattering properties from Ping Yang & colleagues

Library of SWIR to Far-IR Scattering Properties 100 to 3250 cm -1 Library of SWIR to Far-IR Scattering Properties 100 to 3250 cm -1 Library of ice particle scattering properties include Hexagonal plates Solid and hollow columns Aggregates Droxtals 3D bullet rosettes 45 size bins ranging from 2 to 9500  m Spectral range: 100 to 3250 cm -1 at 1-cm -1 resolution Properties for each habit/size bin include volume, projected area, maximum dimension, single-scattering albedo, asymmetry factor, and extinction efficiency * Yang, P., H. Wei, H. L Huang, B. A. Baum, Y. X. Hu, M. I. Mishchenko, and Q. Fu, Scattering and absorption property database of various nonspherical ice particles in the infrared and far-infrared spectral region. In press, Applied Optics.

Particle Size Distributions Gamma size distribution* has the form: N(D) = N o D  e - D where D = max diameter N o = intercept  = dispersion  = slope The intercept, slope, and dispersion values are derived for each PSD by matching three moments (specifically, the 1st, 2nd, and 6th moments) Note: when  = 0, the PSD reduces to an exponential distribution *Heymsfield et al., Observations and parameterizations of particle size distributions in deep tropical cirrus and stratiform precipitating clouds: Results from in situ observations in TRMM field campaigns. J. Atmos. Sci., 59, , 2002.

Field Campaign Information Field Campaign LocationInstrumentsNumber of PSDs FIRE-1 (1986)Madison, WI2D-C, 2D-P246 FIRE-II (1991)Coffeyville, KSReplicator22 ARM-IOP (2000)Lamont, OK2D-C, 2D-P, CPI390 TRMM KWAJEX (1999) Kwajalein, Marshall Islands 2D-C, HVPS, CPI418 CRYSTAL-FACE (2002) Off coast of Nicaragua 2D-C, VIPS41 Probe size ranges are: 2D-C,  m; 2D-P,  m; HVPS (High Volume Precipitation Spectrometer), 200–6100  m; CPI (Cloud Particle Imager),  m; Replicator,  m; VIPS (Video Ice Particle Sampler):  m.

Replicator Ice Crystal Profiles from FIRE Cirrus II Campaign

Ice Crystal Profiles From Tropical Cirrus

Particle Size Distributions From Gamma Fits Particle Size Distributions From Gamma Fits Midlatitude cirrus characteristics Size sorting more pronounced Small crystals at cloud top More often find pristine particles Tropical cirrus anvil characteristics Form in an environment having much higher vertical velocities Size sorting is not as well pronounced Large crystals often present at cloud top Crystals may approach cm in size. Habits tend to be more complex Note that CRYSTAL distributions tend to be the narrowest overall

Ice Particle Habit Distribution At this point, we have - ice particle scattering library - wealth of microphysical data for ice clouds (1117 PSDs) Next issue: develop a ice particle habit distribution that makes sense Note: each idealized ice particle has a prescribed volume, and hence mass Compare IWC and D m computed from integrating over the habit and size distributions to those values estimated from techniques developed by Heymsfield and colleagues from analyses of their in situ data

Ice Water Content and Median Mass Diameter

Guidelines 4 size domains defined by particle maximum length Droxtals: used only for smallest particles Aggregates: only for particles > 1000  m Plates: used only for particles of intermediate size Ice Particle Habit Percentages Based on Comparison of Calculated to In-situ D m and IWC Proposed ice particle habit mixture Max length < 60  m 100% droxtals 60  m < Max length < 1000  m 15% bullet rosettes 35% hexagonal plates 50% solid columns 1000  m < Max length < 2500  m 45% solid columns 45% hollow columns 10% aggregates Max length > 2500  m 97% bullet rosettes 3% aggregates

Relationship Between D m and D eff Relationship Between D m and D eff

Relationship Between IWC and D eff Relationship Between IWC and D eff

Small Particle Sensitivity Study Note that CRYSTAL distributions tend to be the narrowest overall For all 41 CRYSTAL size distributions, increase the number of particles with D max < 20  m by a factor of 100 or 1000

For building look-up tables for operational retrievals, want discrete set of models that are evenly spaced in effective diameter Development of Discrete Set of Models

Bulk Scattering Models Available for Multiple Instruments Bulk Scattering Models Available for Multiple Instruments Provide bulk properties (mean and std. dev.) evenly spaced in D eff from 10 to 180  m for asymmetry factorphase function single-scattering albedoextinction efficiency & cross sections IWC D m Models available at for IR Spectral Models (100 to 3250 cm -1 ) MODISAVHRRAATSRMISR VIRSMAS (MODIS Airborne Simulator) ABI (Advanced Baseline Imager)POLDER (Polarization) SEVIRI (Spinning Enhanced Visible InfraRed Imager)