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Bastiaan van Diedenhoven, Brian Cairns, Ann M. Fridlind, Kenneth Sinclair, Andrzej Wasilewski
-NASA GISS-
Cynthia A. Randles, Arlindo da Silva-GESTAR/Morgan State University/GSFC –
John Yorks, Steve Platnick, Tom Arnold-GSFC-
Variation of Ice Crystal Size, Shape, and Asymmetry Parameter in Tops of Convective Storm Systems
Observed during SEAC4RS
SEAC4RS science team meeting, Pasadena CA, 2015
Supported by NASA grant #NNX15AD44G (ROSES ACCDAM)
Phase
Cloud optical
thicknessIce crystal effective radius
Ice crystal shape and distortion
Cloud top height
Ice asymmetry parameter
Crystal orientation
RSP eMAS
CPL
RSP eMAS
CPL
RSP eMAS
CPL(thin
clouds)
RSP eMAS
RSP CPL(limite
d)
RSP
RSP CPL
In situ
In situ
In situ (shap
e)
ER-2 remote sensing ice cloud products
Subsampled under ER-2 ground track
Feature Description
ModelGEOS-5 Earth Modeling System with GOCART aerosols coupled to radiation parameterization
Fire Emissions QFED: Daily, NRT, MODIS FRP based
Met. Data Assimilation Full NWP observing system (uses GSI)
Aerosol Data Assimilation
Assimilates 550 nm AOT, Local Displacement Ensembles (LDE), Adaptive Buddy Check
Aerosol Observing System (550 nm
AOT)
MODIS: Aqua & Terra Neural Net Retrievals (NNR)MISR: Bright surfaces only (albedo > 15%)AERONET: Level 2
Resolution ~25 km (0.25° 0.3125° latitude longitude), 72 layers, top ~85 km⨉ ⨉Aerosol Species
Dust (DU), sea-salt (SS), sulfates (SO4), organic and black carbon (OC and BC)
Carbon Species CO2, CO with several geographically tagged tracers
Smoke “Age” Tracers
Provides “age” of un-assimilated biomass burning OC AOT with 1 day time resolution (smoke “age” histogram)
GEOS-5 SEAC4RS Mini-Reanalysis
C. A. Randles1,2, Arlindo da Silva2, Peter R. Colarco3,, Virginie Buchard2,4, Anton Darmenov2, Valentina Aquila3,5, Ed Nowottnick, 3,4, and Ravi Govindaraju2,6
1. GESTAR/Morgan State University, 2. NASA Global Modeling and Assimilation Office, 3. NASA Atmospheric Chemistry and Dynamics Laboratory, 4. GESTAR/USRA, 5. GESTAR/Johns Hopkins University, 6. SSAI
Radiosonde data
64 radiosonde measurements at Houston University, Ellington airfield and Smith point were interpolated on a 500-m altitude grid before averaging.
Cold point tropopause(CPT)
Homo-geneousfreezing Level(HFL)
Level of neutral buoyancy(LNB)
adiabatic parcel
Selection of convective cloud data
GOES imagery RSP COT>5CPL and RSP CTH
RSP retrievalsCloud top height
Multi-angle contrast approach (See poster Kenneth Sinclair)
Excellent agreement with CPL
Cloud phaseStrength of cloudbow feature in polarization:
Liquid indexExcellent agreement with CPLVan Diedenhoven et al., JAS 2012
RSP retrievalsIce shape and asymmetry parameter
Using multi-directional polarization at 865 nmHexagonal columns and plates as proxies for complex
particlesRetrieving:
Aspect ratio of crystal components (AR)Surface roughness/distortion parameter (δ)Asymmetry parameter g consistent with AR and δ
Tested on simulated measurements based on complex ice
Van Diedenhoven et al., AMT 2012; ACP 2013; JGR 2014; 2015 paper in prep.
Cloud optical thickness and effective radius:Nakajima-King (670/865 nm + 1590/2250 nm)Ice model consistent with retrieved g
RSP Optical thickness – Cloud top height
40552 data points in total
Melting level
Homogeneous freezing
level
Level of neutral
buoyancy
Cold point tropopause
Variation of glaciation level
RSPCPL
CPL liquid: depolarization ratio <0.15RSP liquid: Liquid index >0.3 (van Diedenhoven et al., JAS 2012)
Ice clouds with liquid underneath
Multiple scatterin
g
RH: average RH wrt liquid between 900-500 hPa
RSP
RSP
Variation of glaciation level
RSPCPL
Homogeneous freezing level
Melting level
LNB
CPT
CPL liquid: depolarization ratio <0.15RSP liquid: Liquid index >0.3 (van Diedenhoven et al., JAS 2012)
RH: average RH wrt liquid between 900-500 hPa
RSP
RSP
Ice properties vs cloud top height
Using 2.25 μm channel
-20oC
-37oC
-52oC
-52oC
-75oC
-20oC
-37oC
-52oC
-52oC
-75oC
Ice properties vs cloud top height
Cold point tropopause
Homogeneous freezing level
Level of neutral buoyancy
Ice properties vs cloud top height
CPL cold tops in TTL examples 30/
8
11/9
• Outflow of overshooting tops?
• Originating from continental MCS’s (see Lenny Pfister’s talk)
Ice properties vs cloud top height
Ice properties vs cloud top height
CPL depol Ice clouds only
Variation: Land vs Ocean
LandOcean
Variation: Vertical pressure velocity at cloud top
ϖ < -0.08 Pa/sϖ >-0.08 Pa/s
Variation: RH wrt ice (500-100 hPa mean)
RHi > 100%RHi < 100%
RSP oriented ice vs cloud top winds
RSP measurement at ~19:05 UTC on
2 Sept.
Windspeed at cloud top (ws))
Specular reflection
Ice properties vs cloud top height
Tropical storm Ingrid
ConclusionsFrequency of supercooled tops correlates with RH wrt to liquidWith increasing cloud top height
Aspect ratio slightly increasesCrystal distortion slightly increasesAsymmetry parameter decreasesEffective radius increases
Transition seen above level of neutral buoyancyLarger effective radii
at ascending topsover oceanfor supersaturated RH wrt ice
Particle orientation depends on windspeed at cloud top No clear variation found with respect to
Wind shear at multiple levelsOmega at 500 hPa
Back up slides
Difference between SWIR band retrievals
Lidar Penetration depth:H(τ=0.1)-H(τ=3)
Using 2.25 μm channelUsing 1.59 μm channel
2.25 μm channel sees (optically) deeper into cloud
Difference in size from two channels increases with ‘fluffiness’ of top
In situ measured aspect ratios from CPIUm et al., ACP, 2015 TWP-ICESPARTICUSISDAC
RSP vs eMAS and CPL (COT>5)
Preliminary
MODIS+POLDER retrievals at TWP
From van Diedenhoven et al., JGR, 2014
16 Jan.-20 Feb. 2006
COT>5
Solar Flux variations from varying size and shapeOptical thickness =
4SZA= 60o
van Diedenhoven et al., “A flexible parameterization for shortwave optical properties of ice crystals”, JAS, in press
http://www.columbia.edu/~bv2154/parameterization.html
Solar Flux variations from varying size and shapeOptical thickness =
2SZA= 60o
van Diedenhoven et al., “A flexible parameterization for shortwave optical properties of ice crystals”, JAS, in press
http://www.columbia.edu/~bv2154/parameterization.html
“Synergistic ice cloud observations from eMAS and RSP”
Collaborative project with GISS/GSFC/UW
NASA NNX15AD44G (ROSES ACCDAM)
Tasks:Calibration checks &
data evaluationAlgorithm
development Cloud properties and
processes studyCampaigns:
SEAC4RSTC4
CRYSTAL-FACE
Cloud optical thickness and effective radius retrievals
Nakajima-King retrievalsNon-
absorbing band (0.865 μm)
Absorbing band (1.59, 2.25 μm)
Depends on ice model asymmetry parameter (g)
COT and Reff retrievals depend on g of assumed ice model
Ice crystal asymmetry parameterIce crystal asymmetry parameter mainly
depends onShape (aspect ratio)Distortion/microscopical roughness/impurity
Crystal shape from Polarization
Long column
Compact hexagon
Pristine column
Distorted column
Multi-directional polarized reflectance
measurementsconserve
Single scattering features
Polarization contains info about
• Aspect ratio (AR)• Distortionδ(Macke et al.
1996)
Retrieve aspect ratio and distortion to estimate
asymmetry parameter for the use in Reff and COT
retrievals
Simulated data test
van Diedenhoven et al., Atmos. Meas. Tech., 2012; Atmos.
Chem. Phys., 2013
Simulated data: • Complex ice habits
(Yang et al.)• 3 roughness degrees• 20 different size
distributions
Retrieved asymmetry parameter
• Within 5% (0.04)• Mean bias: 0.004• Standard deviation:
0.02
