bastiaan van diedenhoven, brian cairns, ann m. fridlind, kenneth sinclair, andrzej wasilewski -nasa...

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

bastiaan.vandiedenhoven@nasa.gov

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