the operational modis aerosol products - nasa€¦ · the global aerosol mod08_d3 daily level 3 1...
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The Operational MODISAerosol Products
L.A. Remer, Y. J. Kaufman, D. Tanré
And the MODIS aerosol team:D.A. Chu, C. Ichoku, R. Kleidman, I. Koren, R. Levy,
R-R. Li, J.V. Martins, S. Mattoo
We’ve been extremely productive!
RGB AOT
FLUX Fraction fine Rong-Rong Li
CaliforniaWildfires
Oct. 26, 2003
From Terra-MODIS
MOD04
The global aerosol
MOD08_D3
Daily Level 31 degree data
October 26, 2003
Aerosol Optical Thickness
Fine mode fraction
Paul Hubankshttp//:modis-atmos.gsfc.nasa.gov
MOD08_M3
Monthly meanglobal data
1 degree grid
Oct. 2003
Optical Thickness
Fine mode fraction
Pixel CountsPaul Hubankshttp//:modis-atmos.gsfc.nasa.gov
Deriving aerosol properties over land and ocean
But, before we can begin to derive aerosol...
We need to find the right pixels.
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Cloud Masking
2.00
1.00
0.50
0
Figure 7. MODIS image from SouthAmerica, Sept. 14, 2002 showingheavy smoke in northwest corner.Bottom images show τ 0.66 retrievals.Bottom left uses operational cloudmask that prevents retrievals in heavy smoke. Bottom right usesspatial variability cloud mask thatmasks the cloud but allow retrievalsfor τ 0.66 > 1.50.
Spatial variability with anassociated test using
1.38 µm to find smooth cirrusis proving to be the best
method to separate aerosol from clouds over both
land and ocean.Martins et al. (2002)
Heavy Smoke
Example from the AmazonStandard Cloud mask Martins Cloud mask
Snow Masking
Li et al. (2005)
(ρ0.86- ρ1.24)/(ρ0.86 + ρ1.24)>0.01And T11 < 285K then SNOW
Sediment Masking
Li et al. (2003)
Spatial variabilityCloud maskInternal spectralSnow maskSediment mask
ocean land
now soon now soon
X X
X
X
Now = Collection 004Soon = Collection 005
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cloud
watersnow
10 km
cloud
400 total- 56 water________
344- 24 snow________
320- 55 cloud_______
265-116 “bright”________
149 “good”
Discard brightest 50%and darkest 20% of the 149 good pixels.
44 pixelsRemer et al. (2004)
How to derive aerosol products from satellite(in 3 easy steps…)
1. Create a Look-Up Table with expected aerosol properties
2. Estimate surface reflectance (to separate signal fromthe atmosphere from signal from the ground).
3. Match the satellite-observed reflectances to the outputof the Look-Up Table
Urban/Industrial(0.96)
“smoke”- moderateabsorption (0.90)
Highly absorbing (0.85)Seasonally urban/industrial (0.96)
Highly absorbing (0.85)Seasonally moderate (0.90)
HighlyAbsorbing(0.85)
3 non-dust modelsplus dust
Set by geography andseason
Models are dynamic f(τ)
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Step 1: Create a LUT with expected aerosol properties… LAND
Step 2: Estimate surface reflectance LAND
CurrentAssume ρs
2.1 = ρm2.1
Assume ρs0.47 = 0.25ρs
2.1Assume ρs
0.66 = 0.50ρs2.1
PlannedAssume ρs
0.47 ~ 0.35ρs2.1
Assume ρs0.66 ~ 0.60ρs
2.1
Step 3: Match the satellite-observed reflectances to the outputof the Look-Up Table LAND
Current:
Individual channel retrievals:0.47 µm and 0.66 µm
Fine model ratio = η =f(ρο
0.66/ρο0.47)
Remer et al. (2004)
Planned:
True inversion: 3 pieces of information =ρm
0.47 ρm0.66 ρm
2.1
will yield three quantities =τ 0.47, η, ρs
2.1
and for a given aerosol modelthe spectral dependencewill automatically yieldτ 0.55 and τ 0.66
No longer assumeρs
2.1 = ρm2.1
The Ocean Algorithm
Choice of 4 fine modesand 5 coarse modes
And 5 coarse modes
In order to minimize(ρmeas - ρLUT) over 6 wavelengths
0.0001
0.001
0.01
0.4 0.5 0.6 0.8 1 2
dry smoke reff
=0.10 µm
urban reff
=0.20 µm
wet reff
=0.25 µm
salt reff
=1 µm
dust reff
=1 µm
dust reff
=2.5 µm
Aer
osol
refl
ecta
nce
wavelength (µm)Remer et al. (2004)
+ ��� ����
oceanlandboth
AERONET sites
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
O CEAN660 nm
N = 2052
100 points50 points25 points15 points
MO
DIS
AO
T (
660
nm)
AERONET AOT (660 nm)
y = 0.008 + 0.95 x R = 0.92
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
LAND660 nm
N = 5906
300 points150 points75 points32 points
MO
DIS
AO
T (
660
nm)
AERONET AOT (660 nm)
y = 0.059 + 0.70 x R = 0.68
66% of MODIS aerosol retrievalsover ocean fall within expected uncertainty
71% of MODIS aerosol retrievalsover land fall within expected uncertainty
MODIS aerosol validation 2000-2002
ocean
land
Remer et al. (2004)
Ichoku et al. (2004)3 year validation data set Ichoku et al. (2004)3 year validation data set
Global Mean (MODIS minus AERONET) AOT550 difference
-0.2
-0.1
0
0.1
0.2
0.3
0.4
5 10 15 20 25 30 35 40 45 50 55 60 65
0 5 10 15 20 25 30 35 40 45 50 55 60Sensor Zenith Angle Bin Ranges (degrees)
Mea
n (M
OD
IS -
AE
RO
NE
T) A
OT5
50
Land_T003 Ocean_T003Land_T004 Ocean_T004Land_A003 Ocean_A003
AERONET Fine FractionO’Neill’s method from sun data
MO
DIS
Fin
e Fr
actio
n
Individualretrievals
Monthly means
Dubovik inversion
Over Ocean: Validating Size ParametersNon-sphericity of dust causes MODIS to under estimate size
(over estimate fine fraction.)
Remer et al. (2004) Kleidman et al. (2005)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6
16 quadrants of the Earth
Anmyon, KoreaE Asia
Capo Verde
Europe
WASHINGTONIndia
Pacific Ocean
S Africa
Aerosol optical thickness
more dust
more pollution
baselineoceanic aerosol
Yoram Kaufman
Monthly mean fine fraction 2001
τ ~ 0.2
τ ~ 0.1
The switch from Side Bto Side A electronics inJune 2001, creates a smallcalibration shift that affectsaerosol size parametersbut not optical thickness.
The effect is magnified whenwhen τ is low.
Applications
Direct and indirect radiative effects and forcing, Aerosol effects on clouds,
Air quality,Quantitative estimate of dust transport over ocean,
Estimates of biomass burning emissions at fire sources
Semi-direct Forcing(Heavy smoke in the Amazon
kills the clouds)
Koren et al. (2004) in Science
Clouds only (Solar)
Clouds, IR
Smoke only
Total – Semi direct effect
July 2002
AOT < 0.2
AOT > 0.2
Water Cloud fraction: AOT < 0.2
Water Cloud fraction: AOT > 0.2
Koren, Kaufman, Rosenfeld, Remer, Rudich
But over the Atlantic aerosols appear to increase cloudiness!
Future plans1. Dust nonsphericity2. True inversion for land retrievals3. Include polarization over land4. Evaluating and updating aerosol models and surface
assumptions.5. Better masking
Collection 0051. Spectral snow mask2. Land Cloud Mask3. Remove Flux products4. Corrected mistakes in the land retrieval over bright surfaces
Collection 005 and Beyond….
Acknowledgements
MODIS Aerosol Team: D.A. Chu, C. Ichoku, R. Kleidman, I. Koren, R. Levy, R-R. Li, J.V. Martins, S. Mattoo, Z. Ahmad
AERONET: B. Holben, O. Dubovik, T. Eck, D. Giles, I. Slutsker, A. Smirnov
AERONET PIs: (SIMBIOS) C. McLain, G. Fergion, C. Pietras
MODIS Atmospheres: M. King, P. Menzel , B-C. Gao, S. Ackerman, R. Frey , M. Gray,L. Gumley, P. Hubanks, R. Hucek, E. Moody, W. Ridgway, K. Strabala
MODIS Land/ Univ. of Maryland: E. Vermote
NOAA/NESDIS/ORA: A. Ignatov, X. Zhao