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.

Slides:



Advertisements
Similar presentations
Proposed new uses for the Ceilometer Network
Advertisements

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.
Boundary Layer Clouds & Sea Spray Steve Siems, Yi (Vivian) Huang, Luke Hande, Mike Manton & Thom Chubb.
Enhancement of Satellite-based Precipitation Estimates using the Information from the Proposed Advanced Baseline Imager (ABI) Part II: Drizzle Detection.
A Methodology for Simultaneous Retrieval of Ice and Liquid Water Cloud Properties O. Sourdeval 1, L. C.-Labonnote 2, A. J. Baran 3, G. Brogniez 2 1 – Institute.
Upgrades to the MODIS near-IR Water Vapor Algorithm and Cirrus Reflectance Algorithm For Collection 6 Bo-Cai Gao & Rong-Rong Li Remote Sensing Division,
3D Radiative Transfer in Cloudy Atmospheres: Diffusion Approximation and Monte Carlo Simulation for Thermal Emission K. N. Liou, Y. Chen, and Y. Gu Department.
1 ASIC-3 Workshop March 2006 Climate Quality Observations from Satellite Lidar Dave Winker, NASA LaRC, Hampton, VA 28 April ‘06.
Earth System Science Teachers of the Deaf Workshop, August 2004 S.O.A.R. High Earth Observing Satellites.
1 An initial CALIPSO cloud climatology ISCCP Anniversary, July 2008, New York Dave Winker NASA LaRC.
10 June 2004 NOAA CALIPSO Meeting Camp Springs, MD CALIPSO Overview Presented by Jim Yoe Status – D. Winker Potential Applications – D. Emmitt, C. Barnet,
On average TES exhibits a small positive bias in the middle and lower troposphere of less than 15% and a larger negative bias of up to 30% in the upper.
Surface Skin Temperatures Observed from IR and Microwave Satellite Measurements Catherine Prigent, CNRS, LERMA, Observatoire de Paris, France Filipe Aires,
Millimeter and sub-millimeter observations for Earth cloud hunting Catherine Prigent, LERMA, Observatoire de Paris.
Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.
Reflected Solar Radiative Kernels And Applications Zhonghai Jin Constantine Loukachine Bruce Wielicki Xu Liu SSAI, Inc. / NASA Langley research Center.
1 CALIPSO Status and Plans Dave Winker Winds Working Group, June 2009, Wintergreen, VA.
Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison Benevento, June 2007.
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 Heymsfield 3 1 NASA Langley Research Center, Hampton, VA 2 Texas A&M University, College Station, TX 3 National Center.
Bastiaan van Diedenhoven (Columbia University, NASA GISS) Ann Fridlind, Andrew Ackerman & Brian Cairns (NASA GISS) An investigation of ice crystal sizes.
FIM Cloud Visualization for SOS and TerraViz Steve Albers.
Gerd-Jan van Zadelhoff & Dave Donovan Comparing ice-cloud microphysical properties using Cloudnet & ARM data.
Characterization of Arctic Mixed-Phase Cloudy Boundary Layers with the Adiabatic Assumption Paquita Zuidema*, Janet Intrieri, Sergey Matrosov, Matthew.
Orbit Characteristics and View Angle Effects on the Global Cloud Field
Introduction Invisible clouds in this study mean super-thin clouds which cannot be detected by MODIS but are classified as clouds by CALIPSO. These sub-visual.
AGU 2002 Fall Meeting NASA Langley Research Center / Atmospheric Sciences Validation of GOES-8 Derived Cloud Properties Over the Southeastern Pacific J.
LMD LMD Science Team CALIPSO – March M.Chiriaco, H.Chepfer, V.Noel, A.Delaval, M.Haeffelin Laboratoire de Météorologie Dynamique, IPSL, France P.Yang,
Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro,
Cloud Top Properties Bryan A. Baum NASA Langley Research Center Paul Menzel NOAA Richard Frey, Hong Zhang CIMSS University of Wisconsin-Madison MODIS Science.
1 CALIPSO: Validation activities and requirements Dave Winker NASA LaRC GALION, WMO Geneva, September 2010.
11 Ice Cover and Sea and Lake Ice Concentration with GOES-R ABI Presented by Yinghui Liu Presented by Yinghui Liu 1 Team Members: Yinghui Liu, Jeffrey.
1 Optimal Channel Selection. 2 Redundancy “Information Content” vs. “On the diagnosis of the strength of the measurements in an observing system through.
Andrew Heidinger and Michael Pavolonis
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,
Topic 7: remote sensing of cloud particles and properties; validation etc.
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.
Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato 1, Fred G. Rose 2, Xu Liu 1, Martin Mlynczak 1, and Bruce A. Wielicki 1 1 NASA.
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.
Investigations of Artifacts in the ISCCP Datasets William B. Rossow July 2006.
Cloud optical properties: modeling and sensitivity study Ping Yang Texas A&M University May 28,2003 Madison, Wisconsin.
ISCCP Calibration 25 th Anniversary Symposium July 23, 2008 NASA GISS Christopher L. Bishop Columbia University New York, New York.
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.
TOMS Ozone Retrieval Sensitivity to Assumption of Lambertian Cloud Surface Part 1. Scattering Phase Function Xiong Liu, 1 Mike Newchurch, 1,2 Robert Loughman.
X X X Cloud Variables Top pressure Cloud type Effective radius
Zhibo (zippo) Zhang 03/29/2010 ESSIC
Challenges and Strategies for Combined Active/Passive Precipitation Retrievals S. Joseph Munchak 1, W. S. Olson 1,2, M. Grecu 1,3 1: NASA Goddard Space.
Initial Analysis of the Pixel-Level Uncertainties in Global MODIS Cloud Optical Thickness and Effective Particle Size Retrievals Steven Platnick 1, Robert.
Visible optical depth,  Optically thicker clouds correlate with colder tops Ship tracks Note, retrievals done on cloudy pixels which are spatially uniform.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
Ball Aerospace & Technologies Corporation -
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 CALIPSO VALIDATION and DATA QUALITY IMPROVEMENT EECLAT T0, J. Pelon.
Interannual Variability and Decadal Change of Solar Reflectance Spectra Zhonghai Jin Costy Loukachine Bruce Wielicki (NASA Langley research Center / SSAI,
Retrieval of desert dust aerosols vertical profiles from IASI measurements in the TIR atmospheric window Sophie Vandenbussche, Svetlana Kochenova, Ann-Carine.
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,
Aerosol properties in a cloudy world (from MODIS and CALIOP) Alexander Marshak (GSFC) Bob Cahalan (GSFC), Tamas Varnai (UMBC), Guoyong Wen, Weidong Yang.
Dave Winker1 and Helene Chepfer2
Horizontally Oriented Ice and Precipitation in Maritime Clouds Using CloudSat, CALIOP, and MODIS Observations Alexa Ross Steve Ackerman Robert Holz University.
Relationships inferred from AIRS-CALIPSO synergy
Cloud Property Retrievals over the Arctic from the NASA A-Train Satellites Aqua, CloudSat and CALIPSO Douglas Spangenberg1, Patrick Minnis2, Michele L.
Need for TEMPO-ABI Synergy
Mike Pavolonis (NOAA/NESDIS/STAR)
Cloud trends from GOME, SCIAMACHY and OMI
Presentation transcript:

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 2013 Dave Winker 1, Anne Garnier 2, Andy Heymsfield 4, Jacques Pelon 3, and Melody Avery 1 1) NASA Langley Research Center, Hampton, VA 2) SSAI, Hampton, VA 3) LATMOS, U. Pierre et Marie Curie, Paris 4) NCAR, Boulder, CO

2 Three co-aligned instruments: CALIOP: polarization lidar - 70-meter footprint - 1/3 km footprint spacing IIR: Imaging IR radiometer – 8.6, 10.5, 12.0 um – 1 km footprint, 60 km swath WFC: Wide-Field Camera Launch: 28 April 2006

R m, radiance at  m, measured R ref, reference radiance at  m, measured or computed R Tcloud, blackbody radiance from cloud equivalent altitude Garnier et al, JAMC, 2012 IIR: effective optical depth retrieval

CALIOP used to constrain IR retrieval From CALIOP: - scene type: R ref (clear or low opaque) - eff cloud height: R Tcloud 4

Scattering effects small at 12  m  vis ~ 2 x 12  m ODeff 3 habits, De=  m 12  m absorption OD and visible OD are closely related –Nearly independent of particle size and shape for De > 20  m Garnier et al, JAMC, 2012

IIR eff OD vs. CALIOP vis OD  CALIOP ‘constrained’ OD from direct transmittance measurement CALIOP constrained OD/IIR OD eff = 2.0 +/-10% in agreement with expectations and sensitivity studies. Expected Single-layer semi-transparent clouds, tops > 7km, randomly oriented ice, global ocean, day. 6 Expected  CALIOP ‘unconstrained’ OD Sensitivity of IIR retrievals: good agreement with CALIOP down to ODs smaller than 0.05

CALIOP retrieval performance should not be dependent on underlying surface IIR retrieval uncertainties somewhat larger over land than ocean LANDOCEAN 7

Single-layered cloud, altitude > 7km, T° < 233K, sea, all latitudes, Day+Night January 2011 Then  Ice water path IWP =  ice /3) x (2xOD eff _12) x D e Basic approach (Parol, 1991):  eff 12/10 = OD eff _12 / OD eff _10  eff 12/08= OD eff _12/ OD eff _08... and lookup tables De and IWP Garnier et al, JAMC, 2012

Next Topic: IWC from lidar extinction 9

Recap Basis of CALIOP Version 3 IWC: IWC = 119  1.22 Heymsfield, Winker, and van Zadelhoff, 2005 ( = HWZ) 10

CALIOP, CPR-RO, 2C-ICE (Jan 2008) 11

Frequency of cloud detection Symbols in each panel show the 20% level. Fraction of clouds detected only by CALIOP Fraction of clouds detected by both CALIOP and CPR Heymsfield et al. (JGR, submitted) 12

Revisit the HWZ parameterization Motivation to update HWZ parameterization: –New in situ measurements, especially in cold clouds SCOUT, CRYSTAL-FACE and Pre-AVE 13

Expanded in situ Dataset High-temperature data (0 to -60 C) Low-temperature data ( - 50 to -90 C) Heymsfield et al. (JGR, submitted)

Improved IWC Parameterization HWZ: IWC = 119  1.22 New: IWC = a(T)  b(T) - New data < - 60 C from recent campaigns - In situ data corrected for particle shattering effects - New temperature-dependent parameterization provides better fit to in situ measurements HWZf(T)f(De) 0 to -90 C0.42 ± ± ± to -20 C0.38 ± ± ± to -60 C0.43 ± ± ± to -90 C1.73 ± ± ± 0.28 At warm temperatures, CALIOP Version 3 IWC too low by ~ factor of 2 IWC fit / IWC meas 15

Now: Two independent IWC retrievals IWC from IIR De and IWP, using CALIOP cloud thickness and from parameterized CALIOP extinction retrieval median V3 CALIOP/IIR ratio ~ 1.7 Agreement will improve with new CALIOP IWC parameterization Single-layered semi-transparent clouds, cloud top > 7km, randomly oriented ice, global ocean, day. 16

17 A few thoughts Can do a few things better using active and passive together than with either alone Colocated lidar and IR observations (same line of sight) eliminates uncertainties from view angle differences, spatial mismatches Comparison of independent retrievals is essential for evaluating uncertainties

18 END