Influences of ice particle
model on ice cloud optical
thickness retrieval
Zhibo (zippo) Zhang
03/29/2010
ESSIC
Outline
• Background
• Importance of ice cloud
• Ice particle model and ice cloud retrieval
• Influence of ice particle model on t retrieval
• Comparison of MODIS and POLDER ice t retrieval
• Influence on our understanding of ice cloud seasonal
variability
• Summary
Ice cloud: fun
Photo from Wiki
Ice cloud: important
ISCCP day-time ice cloud amount
Albedo Effect
Greenhouse
Effect (dominant)
Earth
Ice clouds are important, because
• Cover large portion of the Earth’s surface
• Radiative effects
• Water vapor budget
• Cloud feedbacks
Ice cloud: not well understood
Duane Waliser et al. 2009 JGR
Satellite-base remote sensing
of ice cloud properties
In-situ measurements
Scattering model
microphysics
Ice Particle Model
GCMs
Satellite remote sensing
Ice particle model
• Size distribution
• Shape distribution
• Orientation
• Inhomogeneity & surface roughness
Ice Particle model
Ice particle size
• Size matters
• Cloud life time (e.g., Heymsfield 1972, Jensen et al.1996)
• Cloud reflectance, radiative forcing, heating/cooling rate
(e.g., Ackerman et al. 1988; Jensen et al. 1994 )
• Cloud feedback (e.g., Stephens et al. 1990)
Hard to measure
Shattering of large particles
Number density
Gardiner and Hallett 1985; Gayet et al. 1996
Field et al. 2003;
50 m
m
Particle Size
mm
Earth Observing Laboratory NCAR
Ice particle shape
• Why shape also matters?
wavelength
Aerosol
Ice particle
wavelength
From Bryan Baum
Complicacy of ice particle shape must be
acceptable by scattering models
Capabilities of current
scattering models
Ice particle orientation
Randomly orientated
Horizontally orientated
Images from www.atoptics.co.uk
Ice particle orientation
Horizontally orientated
Image credit: CNES
Inhomogeneity and
surface roughness
Yang et al. 2008 JAMC
Yang et al. 2008 ITGRS
Ice particle model
• Size distribution
Ice Particle model
• Shape distribution
• Orientation
• Inhomogeneity & surface roughness
So many things to consider…
not surprising that ice particle models are usually different from one another
Ice particle models:
MODIS C5
• More than 1000 PSDs
• Complicate habit/shape distribution
• Random orientation
• Homogeneous and smooth
Baum et al. 2005 JAMC
Ice particle model:
MODIS C5
IWC from MODIS C5 ice particle mode is consistent with in situ measurement
Baum et al. 2005 JAMC
Baum et al. 2005 JAMC
Ice particle model:
POLDER
Inhomogeneous Hexagonal Monocrystal
• Constant size (30m)
• One habit only
• Random orientation
• Internal inclusion of air bubbles
Courtesy of
Jerome Riedi
C.-Labonnote et al. 2000 GRL
Scattering signature consistent with POLDER observation
Scattering phase function
Baum05 VS IHM
Comparison of MODIS and
POLDER ice cloud retrieval
Motivation
• How are MODIS and POLDER ice cloud retrievals
different?
• What is the role of ice particle model?
• Any implications for MODIS
climate studies? POLDER
Resolution
20km
• Is it possible to build up1km
a long-term ice cloud
multiple missions?
Cloudproperty
effectivedataset
radius from
Retrieved
Assumed
Ice particle
model
Baum05
Zhang,
Z.et al. 2009: Atmos.
Chem. Phys., 9, 1-15. IHM
(www.atmos-chem-phys.net/9/1/2009/)
Directionality
Single
Up to 16
Case for comparison
Flight track of TC4 mission
Flight track
NASA Langley TC4 team
GOES IR image
Aqua-MODIS granule on July 22, 2007 (UTC 18:45)
Collocation
Collocation of Level-1 radiance data
6km
6km
MODIS 1km pixel
POLDER full resolution pixel
Collocation of Level-2 cloud products
6km
6km
POLDER 20km downscale to 6km
MODIS 1km aggregated to 6km
POLDER full resolution pixel
MODIS t vs POLDER t


Same clouds; different t?
Why?
tPOLDER/tMODIS follows the lognormal distribution
tPOLDER is substantially smaller
than tMODIS
For more than 80% pixels
tPOLDER < tMODIS
For more than 50% pixels
tPOLDER < tMODIS by more than 30%
Main reason for the difference
• Difference in resolution (Plane parallel albedo bias) ✗
• Difference in effective radius treatment
✗
• Difference in ice particle model✔
R ~ (1  g)
 retrieval ~ Robs / (1  g)
 POLDER 1  g Baum05
(From data: 0.68)
~
 0.7126 (0.6827)
 MODIS
1  g IHM
Implications for ice SW CRF
Zonal mean ice optical thickness vs month (2006)
Implications for ice SW CRF
Instantaneous Shortwave CRF (FSW)
Implications for ice SW CRF
Wrong ice particle model
retrieval
Wrong t retrieval
FSW computation
Wrong g used
F SW ~ R ~ (1  g)
“Not so wrong”
FSW
Error cancellation
Ice particle model and seasonal
variation of t retrieval
Difference in g
Difference in higher-order
moment of P11
 cIHM IHM model is used for MODIS retrieval
 cBaum05 Baum05 model is used for MODIS retrieval
Angular signature of ice cloud
reflectance
Satellite
Single-scattering
Multiple-scattering
Angular signature is mainly
determined by single-scattering
MODIS angular sampling
MODIS angular sampling vs season
summer
winter
summer
winter
0
0
s
s
Impact on seasonal variation of t retrieval
Assume IHM to be the truth
summer
winter
Summary
• The t of ice clouds retrieved from POLDER is substantially
smaller than that from MODIS retrieval.
• This difference is mostly attributed to the difference in ice bulk
scattering models used in MODIS and POLDER retrievals
• If a wrong bulk scattering model is used in the retrieval
algorithm, the error in g factor may lead to overestimation or
underestimation of t . However, this error in t retrieval is
largely cancelled in FSW computation by the error in g factor.
• The error in higher-order moment of P11 may lead to artificial
seasonal variation of t and this error can NOT be cancelled in
FSW computation
Questions?