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![Page 1: The spatial variability of aerosol properties in the vicinity of trade wind cumuli over the Tropical Western Atlantic observed from RICO aircrafts and.](https://reader036.fdocuments.in/reader036/viewer/2022062511/551b3bf8550346dd1a8b5406/html5/thumbnails/1.jpg)
The spatial variability of aerosol properties in the vicinity of trade wind cumuli over the Tropical Western Atlantic observed from
RICO aircrafts and CALIOP
Larry Di Girolamo
Jason Tackett
Marile Colon-Robles
Bob Rauber
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign
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Motivation
Aerosols modify cloud properties through a variety of “indirect effects.”
Many modeling studies into aerosol indirect effects usually prescribe horizontally homogeneous aerosol properties
Courtesy of S. Tripathi
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Motivation
Clouds modify aerosol properties through a variety of chemical and dynamical processes.
The cloud processing and detrainment of aerosols, coupled with humidity haloes, implies that aerosol properties in the near-cloud environment are different than the far cloud environment.
Where’s the observations?
Adapted from Hegg (2001)
Cloud processing
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Where’s the observations?
• Passive satellite sensors have been hampered by 3-D radiative cloud-adjacency effects (e.g. Wen et al. (2007), Yang and Di Girolamo (2008)).
• Aircraft in situ observations have been hampered by inadequate sampling lengths to provide “near-cloud” aerosol spectra.
• Sun-photometers are also subjected 3-D radiative cloud-adjacency effects… but Koren et al. (2007) and Redemann et al. (2009) both observed an AOD increase of ~10 – 13% near cloud.
• Lidar are also hampered by 3-D radiative cloud-adjacency effects when operated during the day… but Su et al. (2008) observed an AOD increase of ~ 8 – 17% near cloud using the HSRL.
Tackett and Di Girolamo (GRL 2009 submitted) using CALIPSO
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CALIOP : Cloud Aerosol LIdar with Orthogonal Polarization
λ = 532 nm and 1064 nm
The CALIOP Instrument
Backscatter : Fraction of radiance scattered in backward direction (km-1sr-1)
Resolution
Wavelengths
Horizontal: 333 m
Vertical: 30 m (λ = 532 nm) 60 m (λ = 1064 nm)
http://www-calipso.larc.nasa.gov/
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CALIOP Data Products
0
22 )(),,~,( drTPrnrQNnr T
Q
r )(rn
P2T
n~
Total Attenuated Backscatter (km-1sr-1)
= radius
= complex index of refraction
= total number concentration
= lidar wavelength
= size distribution
= scattering efficiency
= scattering phase function
= two-way transmittanceTypical values: (km-1sr-1)
Aerosols: 10-3 to 10-2……..Clouds: 0.1 to 1
TN
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CALIOP Data Products
Color Ratio
532
1064
Backscatter at 532 nm (km-1sr-1)
Col
or r
atio
Vaughan et al. (2005)
Typical values:
Clouds: ~1.0
Aerosols: 0.4 – 0.8
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CALIOP Data Products
’’
’(1/3 km)
Cloud Layer Product for cloud masking (1/3 and 5 km)
CALIOP Aerosol Product (5 km) is NOT used
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Region & Time of Interest
~2100 km
~2700 km
~3000 km
RICO: Rain In Cumulus over the Ocean
Focus is on the Caribbean in winter
RICO Field Campaign
Dec. 2004 – Jan. 2005
Courtesy of Google Earth
Focus on nighttime data over ocean
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Methodology
Alti
tude
1) Clouds between 0.5-2.0 km
2) Single layered
3) No clouds above ‘clear air’ profile
4) Horizontal distance to next cloud ≥ 3 km
Criteria:
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Methodology
Alti
tude
1) From cloud top to cloud base altitudes
2) To ½ the distance to the next cloud
Store β':Satellite direction
β'
Dist. from cloud
Total meeting criteria:
26,833 clouds
34,371 cloud edges
Dec. ’06 – Feb. ’07 & Dec. ’07 – Feb. ’08 Dec. ’08 – Feb. ’09
333 m
30 m
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MethodologyA
ltitu
de
Alti
tude
Distance to cloud edge
Averaging strategy
Number of samples:
1 2 3
½ dist. to cloud
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MethodologyA
ltitu
de
Alti
tude
Distance to cloud edge
Averaging strategy
Number of samples:
1 2 3
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MethodologyA
ltitu
de
Alti
tude
Distance to cloud edge
Averaging strategy
Number of samples:
1 2 3
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Total Number of Samples
Distance to cloud edge (km )
Alt
itu
de
(k
m)
0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.5
1.0
1.5
2.0
10
100
1000
10000
100000
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Median backscatter
λ= 532 nm
Distance to cloud edge (km )
Alt
itu
de
(k
m)
0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.5
1.0
1.5
2.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
10-3
(km -1sr-1)
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Normalized median backscatter
λ= 532 nm
Distance to cloud edge (km )
Alt
itu
de
(k
m)
0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.5
1.0
1.5
2.0
0.6
0.8
1.0
1.2
1.4
1.6
1.8
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Integrated median backscatter
€
Δ γ1064 = 42 ± 2%%331532 Δγ
0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.991.0
1.5
2.0
2.5
3.0
Distance to cloud edge (km )
532 nm1,064 nm
10-3
(sr-1)
km
km T dzNnr0.2
5.0),,~,( γ
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Median color ratio
Distance to cloud edge (km )
Alt
itu
de
(k
m)
0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.5
1.0
1.5
2.0
0.45
0.50
0.55
0.60
0.65
0.70
0.75
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Layer averaged median color ratio
%515 Δ
0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.990.50
0.52
0.54
0.56
0.58
Distance to cloud edge (km )
Co
lor
rati
o
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Theory vs. observations
%331532 Δγ
%2421064 Δγ
%515 Δ
When comparing 3 km from cloud edge to ~0.3 km to cloud edge…
How to explain?Observations
0
22 )( drTPrnrQ
km
kmdz
0.2
5.0 γ
What changes in aerosol properties can account for this?
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Theory vs. observations
OPAC "m aritim e clean" aerosol size distribution
0.0001
0.001
0.01
0.1
1
10
100
1000
10000
100000
0.01 0.1 1 10
Radius (μm )
dN
/dr
(cm
-3u
m-1
)
Total
Mode 1
Mode 2
Mode 3
Rj = median radius
σj = standard deviation
Nj = number concentration
Composition [Peter et al, 2008]:
r ≤ 0.2 μm, ammonium sulfate
r > 0.2 μm, sea salt
Log-normal Size distribution [Hess et al, 1998]:
Relative Humidity = 80%
3
1
2
log
/log
2
1exp
10lnlog2)(
j j
j
j
j Rr
r
Nrn
Parameters:
Far From Cloud Aerosol Distribution
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Theory vs. observationsest fit to observations:
0.00001
0.0001
0.001
0.01
0.1
1
10
100
1000
10000
100000
0.01 0.1 1 10
Radius (μm)
dn
(r)/
dr
(cm
-3μ
m-1
)
Far from cloudNear cloud
ΔRj = 34%
Δσj = −2%
ΔNj = −32%
ΔAOD532 = 16%
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Prelim Observations from RICO100 – 200 m vs 1000 – 1100 m based on all RICO flights
PCASP
FSSP
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Prelim Observations from RICO100 – 200 m vs 1000 – 1100 m
0.00001
0.0001
0.001
0.01
0.1
1
10
100
1000
10000
100000
0.01 0.1 1 10
Radius (μm)
dn
(r)/
dr
(cm
-3μ
m-1
)
Far from cloudNear cloud
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Potential ProcessesBest fit to observations: ΔRj = 34%, Δσj = −2%, ΔNj = −32%
Collision-coalescence: Increases Rj and σj, decreases Nj
Hygroscopic growth: Increases Rj and σj, leaving Nj unchanged
Precipitation scavenging: Decreases Rj, σj, and Nj
Other scavenging processes (nucleation, diffusion, impaction):
Increases Rj and σj, decreases Nj
No single process dominates based on observations
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Theory vs. observations
Cloud contamination
Observed increase
(%)
N cloud droplets
(cm-3)
31 ± 3 0.018
42 ± 2 0.011
15 ± 5 0.007Δ
532γΔ
1064γΔ
Cloud contamination alone cannot explain the observations
€
n(r) = Ncr3e−br
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Integrated median backscatter
0.33 0.66 0.99 1.33 1.66 1.99 2.33 2.66 2.991.0
1.5
2.0
2.5
3.0
3.5
4.0
Distance to cloud edge (km )
532 nm, night1,064 nm, night532 nm, day1,064 nm, day
10-3
(sr-1)
Day vs Night
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Conclusion
Systematic increase in backscatter near cloud edge
Layer integrated backscatter increased by ~31% at λ = 532 nm and ~42% at λ = 1064 nm
Layer averaged color ratio increased by ~15%
An increase in aerosol sizes and a decrease in number concentration near cloud edge best explains the observations (ongoing RICO aircraft analysis)
The method and results are amenable for evaluating models
How does lidar backscatter in the vicinity of clouds compare to far from clouds?
Greatest enhancement at cloud base and top
Tackett and Di Girolamo (GRL submitted)