Transcript VIIRS Aerosol Optical Depth Algorithm and Products

VIIRS Aerosol Optical Depth
Algorithm and Products
Istvan Laszlo
NOAA
VIIRS Aerosol Science and Operational Users Workshop
November 21-22, 2013
National Center for Weather and Climate Prediction (NCWCP)
College Park, MD
VIIRS Aerosol Cal/Val Team
Name
Organization
Major Task
Kurt F. Brueske
IIS/Raytheon
Code testing support within IDPS
Ashley N. Griffin
PRAXIS, INC/NASA
JAM
Brent Holben
NASA/GSFC
AERONET observations for validation work
Robert Holz
UW/CIMSS
Product validation and science team support
Nai-Yung C. Hsu
NASA/GSFC
Deep-blue algorithm development
Ho-Chun Huang
UMD/CICS
SM algorithm development and validation
Jingfeng Huang
UMD/CICS
AOT Algorithm development and product validation
Edward J. Hyer
NRL
Product validation, assimilation activities
John M. Jackson
NGAS
VIIRS cal/val activities, liaison to SDR team
Shobha Kondragunta
NOAA/NESDIS
Co-lead
Istvan Laszlo
NOAA/NESDIS
Co-lead
Hongqing Liu
IMSG/NOAA
Visualization, algorithm development, validation
Min M. Oo
UW/CIMSS
Cal/Val with collocated MODIS data
Lorraine A. Remer
UMBC
Algorithm development, ATBD, liason to VCM team
Andrew M. Sayer
NASA/GESTAR
Deep-blue algorithm development
Hai Zhang
IMSG/NOAA
Algorithm coding, validation within IDEA
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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Outline
• VIIRS instrument
• Aerosol algorithm
– history
– over-land algorithm
– over-ocean algorithm
• VIIRS vs. MODIS algorithm
• VIIRS aerosol products
• Summary
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VIIRS
Visible Infrared Imaging Radiometer
Suite (VIIRS)
• cross-track scanning radiometer
with ~3000 km swath – full daily
sampling
• 7 years lifetime
• 22 channels (412-12,016 nm)
– 16 of these are M bands with 0.742
x 0.776 km nadir resolution
– aerosol retrieval is from M bands
• high signal-to-noise ratio (SNR):
– M1-M7: ~200-400
– M8-M11: ~10-300
• 2% absolute radiometric accuracy
• single look
• no polarization
Band
name
M1*
M2*
M3*
M4*
M5*
M6
M7*
M8
M9
M10
M11
M12
M13
M14
M15
M16
Wavelength Bandwidth Use in
(nm)
(nm)
algorithm
412
20
L
445
14
L
488
19
L, TL TO
555
21
TO
672
20
L, O, TO
746
15
O
865
39
O, TL
1,240
27
O, TL, TO
1,378
15
TL
1,610
59
O, TL, TO
2,250
47
L, O, TL,
TO
3,700
191
TL
4,050
163
none
8,550
323
none
10,763
989
TL, TO
12,016
864
TT, TO
*dual gain, L: land, O: ocean; T: internal test
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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• Sensor Data Records (SDRs), converted from raw VIIRS data
(RDR), are used in the VIIRS aerosol algorithm
• Processing is on a granule by granule basis
VIIRS (cont)
– VIIRS granule typically consists of 768 x 3200 (along-track by
cross-track) 0.75-km pixels
Example VIIRS granule, 11/02/2013, 19:05 UTC
RGB image
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Aerosol Retrieval – Physical Basis
 The satellite-observed reflectance
(ρtoa) is the sum of atmospheric
(ρatm) and surface components
(ρsrf) .
 The components are the result of
reflection, scattering by
molecules and aerosols and
absorption by aerosols and gases.
 The aerosol portion of the
atmospheric component (aerosol
reflectance) carries information
about aerosol.
 The aerosol reflectance is
determined by the amount and
type (size, shape and chemical
composition) of aerosol.
toa  atm  srf
atm
srf
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VIIRS Aerosol Algorithm (1)
• Separate algorithms used over land and ocean
• Algorithm heritages
– over land: MODIS atmospheric correction
– over ocean: MODIS aerosol retrieval
• The VIIRS aerosol algorithm is similar but NOT
identical to the above MODIS algorithms
• Many years of development work:
– Initial science version is by Raytheon
– Updates and modifications by NGAS
– Current Cal/Val Team is to maintain, evaluate and
improve the algorithm
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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VIIRS Aerosol Algorithm (2)
• AOT and aerosol model is
simultaneously retrieved using
reflected solar radiation in
multiple VIIRS bands and
ancillary data.
• Optimal solution is searched
for that best matches
theoretical and observed
reflectances.
– iteration through increasing
values of AOT and candidate
aerosol models
• Must account for all important
radiative processes
– molecular scattering, aerosol
scattering and absorption, gas
absorption, and surface
reflection.
• Approach in the vector RT
Second Simulation of the
Satellite Signal in the Solar
Spectrum (6S-V1.1)
[Kotchenova and Vermote,
2007] is adopted.
(1)
•
ρR+A, TR+A, SR+A are pre-calculated by 6S and stored in LUT; Tgs are parameterized.
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• Atmospheric correction of reflectances
[Vermote and Kotchenova, 2008]
Over Land Retrieval
– AOT and aerosol model are by-products of
surface reflectance retrieval
• Basis: aerosols change the ratios of
spectral reflectances (spectral contrast)
from those of the surface values.
• AOT and aerosol model are the ones that
provide the best match between ratios of
surface reflectances retrieved in multiple
channels and their expected values.
– Expected ratios are derived empirically by
atmospherically correcting VIIRS TOA Mband reflectances using AERONET AOT (99
sites, ~60,000 matchups).
• Eq. (1) is solved for ρsurf assuming
Lambertian reflection.
• 5 aerosol models [Dubovik et al. 2002]:
–
–
–
–
dust
smoke (high and low absorption)
urban (clean & polluted)
bimodal lognormal size distribution,
function of AOT, spherical particles
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TOA reflectance vs. AOT ()
0.6
M5
M1
M2
0.5 M3
Reflectance
– Surface: green vegetation
– Atmosphere:
• aerosol: urban clean
• Rayleigh: no
• Gas absorption: no
– SZA=0°, VZA=30°, RAZ=20°
=0.0
=0.1
=0.2
=0.5
=1.0
=2.0
0.4
0.3
M11
0.2
0.1
• Normalized spectral reflectance
(“spectral shape”) also changes with
increasing AOT.
0.0
0.5
1.0
1.5
2.0
2.5
Wavelength (m)
0.6
s+a (=0.4)
s+a+m
s+a+m+g
M5
M1
M2
0.5 M3
1.4
1.2
1.0
0.8
M1/M5
M2/M5
M3/M5
M11/M5
0.6
0.4
0.2
TOA reflectance
surface+aerosol reflectance ratio
Over Land Retrieval (2)
• Surface + aerosol reflectance
changes with increasing AOT.
0.4
0.3
M11
0.2
0.1
0.0
0.0
0.5
1.0
AOT
1.5
2.0
0.5
1.0
1.5
2.0
2.5
Wavelength (m)
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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Over Land Retrieval (3)
Location: UMBC (76.71W, 39.26N); Date: 5/17/2013
M7
0.4
Observed reflectance
M8
0.3
0.2
M10
M1
M2
M6
M3
M4
0.1
Aerosol model: urban-polluted
M5 retrieved
M3 retrieved
M3 expected
0.07
Surface reflectance
• AOT is retrieved by marching
through AOTs in LUT until the
retrieved M3 surface
reflectance is close to the one
expected from the retrieved
M5 value.
0.06
0.05
0.04
0.038
M11
M5
0.36
0.03
0.0
M9
0.0
0.1
0.2
0.3
0.4
AOT @550 nm
0.5
1.0
1.5
2.0
2.5
Wavelength (m)
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Surface reflectance, 
0.012
0.008
0.004
0.000
retrieved
expected
M11
0.10
0.08
0.06
M5
model 4


residual    sretrieved     sexpected  
0.04
-0.004

M3
M2
M1
M1
2
0.02
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.5
1.0
AOT @550 nm
0.6
1.5
2.0
2.5
Wavelength (m)
Location: UMBC (76.71W, 39.26N); Date: 5/17/2013
0.56
0.5
0.6
10-2
AOT @550 nm
residual
0.36
0.36
0.3
0.2
-3
10
0.32
0.26
1.26E-04
1.56E-04
0.1
1.29E-04
1.43E-04
-4
10
10
0
1
2
model
3
4
M1
M2
M3
0.4
M4
0.3
M5
M6
M7
0.2
0.1
-5
0.0
Residual
0.4
AOT @550nm
0.5
1.05E-02
(retrieved-expected) M3 surface reflectance
0.12
model 0
model 1
model 2
model 3
model 4
0.016
AOT @550 nm
Over Land Retrieval (4)
0.020
M8
M10
M11
0.0
0.5
1.0
1.5
2.0
2.5
Wavelength (m)
0=dust; 1=smoke-high abs.; 2=smoke-low abs.;
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
3=urban-clean;
4=urban polluted
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Over Ocean Retrieval
• Close adaptation of the MODIS
approach [Tanré et al., 1997]
– search for AOT and aerosol model
that most closely reproduces the
VIIRS-measured TOA reflectance in
multiple bands.
– wind-dependent (speed and
direction) ocean surface reflectance
is calculated analytically.
• Accounts for water-leaving radiance
(Lambertian, fixed pigment
concentration), whitecap (Lambertian,
wind-speed dependent) and specular
reflection (dependent on wind speed
and direction).
– Combines 5 fine mode and 4 coarse
mode models with 0.01 increments
in fine mode fraction (2020 models)
– TOA reflectances in selected M
bands are calculated from Eq. 1 and
compared to observed ones to
retrieve AOT and aerosol model
simultaneously.
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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Over Ocean Retrieval
•
AOT for a given aerosol model is
from matching calculated and
observed M7 TOA reflectances.
Retrieved AOT is used to
calculate TOA reflectances in
other channels.
Location: Arica (70.31W, 18.47S); Date: 5/4/2013
0.30
Observed reflectance
0.25
calculated
observed
calculated @ retrieved AOT550
0.09
M5
0.08
0.07
TOA reflectance
•
M1
M2
0.20
M3
M6
0.06
M7
M7
0.05
0.04
0.03
M8
0.02
M10
0.15
M4
M11
0.10
M5
M6
M7
0.05
0.01
M8
0.00
M10
M11
M9
0.5
1.0
1.5
2.0
2.5
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
AOT@550nm
Wavelength (m)
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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0.09


M6
calculated
observed
0.05
fine mode model index = 0
coarse mode model index = 2
1x10-3
2
Residual
TOA reflectance

cal
obs
   TOA
 
residual   TOA
M7
0.04
8x10-4
6x10-4
residual = 2.3x10-5
AOT @550nm = 0.259
fine mode fraction = 0.6
4x10-4
0.03
M8
2x10-4
0.02
M10
M11
0
0.01
0.5
1.0
1.5
2.0
0
2.5
20
0.45
1x10-3
8x10-4
residual = 2.3x10-5
AOT @550nm = 0.259
fine mode model = 0
coarse mode model = 2
fine mode fraction = 0.6
6x10-4
4x10-4
80
100
Location: Arica (70.31W, 18.47S); Date: 5/4/2013
M1
M2
0.35
0.30
M3
M4
0.25
0.20
M5
M6
M7
0.15
2x10-4
60
0.40
AOT @550nm
1x10-3
40
Fine mode fraction (x100)
Wavelength (m)
Residual
Over Ocean Retrieval (2)
0.07
0.06
1x10-3
fine mode model index = 0
coarse mode model index = 2
fine mode fraction (x100) = 60
M5
0.08
M8
M10
M11
0.10
0
200 400 600 800 10001200 14001600 18002000
Model
0.5
1.0
1.5
2.0
2.5
Wavelength (m)
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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Pixel Selection & Quality Flags
Condition
Invalid SDR data
Cloud Contamination
Sun Glint
Snow/Ice
Fire
Bright Surface
Coastal or Inland Water
Turbid Water
Ephemeral Water
SolZA ≥ 80°
Out of Spec Range
Cloud Adjacency
Cloud Shadow
Cirrus
Soil Dominated
65° ≤ SolZA < 80°
Large Retrieval Residual
Quality Flag
Not
Produced
X
X
X
X
X
X
X
X
X
X
Excluded
Applies to
Degraded
Land
Ocean
X
X
X
X
X
X
X
X
X
X
Detected by
VCM
X
X
X
X
Internal
Tests
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
X
X
X
X
X
X
X
X
X
X
X
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Planned Enhancements
• Replace current fixed surface reflectance
relationships with NDVI-dependent relationships.
• Extend AOT reporting range from 2 to 5.
• Update ocean aerosol models with those from
MODIS algorithm.
• Improve cloud/heavy-aerosol discrimination,
snow/ice detection; add spatial variability
internal test.
• Add Deep-Blue module (Hsu & Sayer) to extend
retrievals over bright surfaces.
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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VIIRS vs. MODIS
VIIRS
Algorithm (general)
Main source of data screening External VCM
Aggregation on
Outputs
Residual calculated as
Absolute difference
Over-ocean algorithm
0.67, 0.74, 0.86, 1.24, 1.61, 2.25
Channel used
µm
MODIS
Internal tests
Inputs
Relative difference
0.55, 0.66, 0.86, 1.24, 1.61, 2.12
µm
Non-lambertian, function of
wind speed and direction
Lambertian, independent on wind
(will change in C6)
Combination of fine and coarse
modes
TOA reflectances
No
Over-land algorithm
0.41, 0.44, 0.48, 0.67, 2.25 µm
Select one from five pre-defined
models
Combination of fine and coarse
modes
TOA reflectances
Yes
Spectral surface reflectance
Constant ratios of 0.41, 0.44,
0.48, 2.25 µm over 0.67 µm
(will depend on NDVI)
Linear relationship between 0.66
and 2.12 µm as a function of NDVI
and scattering angle; constant
linear relationship between 0.47
and 0.66 µm
Match to
Surface reflectances
TOA reflectances
Surface reflection
Aerosol model
Match to
Retrieval over inland water
Channel used
Aerosol model
0.47, 0.66, 2.12 µm
Mix two assigned fine and coarse
mode dominated models
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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VIIRS
MODIS
Products
VIIRS vs. MODIS (cont)
Nominal spatial resolution
Granule size
AOT range
Main product land
Main product ocean
0.75 km (IP)
6 km (EDR)
86 seconds
[0, 2] (will change)
Spectral AOT
Spectral AOT
Ångström exponent
Orbit
10 km (C5)
3 km (C6)
5 minutes
[-0.05, 5]
Spectral AOT
Spectral AOT
Fine mode fraction
Orbit altitude
824 km
690 km
Equator crossing time
13:30 UTC
13:30 UTC (Aqua)
Swath width
3000 km
2300 km
Pixel resolution (nadir)
Pixel resolution (swath edge)
0.75 km
1.5 km
0.5 km
2 km
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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Sensor and Other Inputs
• VIIRS M-band SDRs (reflectances)
– M1-M12, M15, M16, and quality flags
• Solar and view geometry
– zenith and azimuth angles
• VIIRS Cloud Mask (VCM)
– pixel cloud flag (clear/cloudy, probably clear/cloudy), cloud
shadow, land/water, snow/ice, fire, sunglint, heavy aerosol,
volcanic ash
• NCEP GFS data (backup:FNMOC/NAVGEM)
– Water vapor, ozone, surface pressure, winds (speed and
direction)
• Navy Aerosol Analysis and Prediction System (NAAPS)
aerosol data
– used for filling in missing VIIRS IP retrievals
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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VIIRS Aerosol Products (1)
• Aerosol Optical Thickness (AOT)
– for 11 wavelengths (10 M bands + 550 nm)
• APSP (Aerosol Particle Size Parameter)
– Ångström Exponent derived from AOTs at M2 (445 nm) and M5 (672
nm) over land, and M7 (865 nm) and M10 (1610 nm) over ocean
– qualitative measure of particle size
– over-land product is not recommended!
• Suspended Matter (SM)
– classification of aerosol type (dust, smoke, sea salt, volcanic ash) and
smoke concentration
– currently, derived from VIIRS Cloud Mask (volcanic ash) and aerosol
model identified by the aerosol algorithm
• Only day time and over dark land and non-sunglint ocean
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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VIIRS Aerosol Products (2)
At NOAA Comprehensive Large Arraydata Stewardship System (CLASS):
• Intermediate Product (IP)
– 0.75-km pixel
• AOT
• APSP
• AMI (Aerosol Model Information)
– land: single aerosol model
– ocean: indexes of fine and coarse
modes and fine mode fraction
• quality flags
• Environmental Data Record (EDR)
– 6 km aggregated from 8x8 IPs filtered
by quality flags
•
•
•
•
granule with 96 x 400 EDR cells
AOT
APSP
quality flags
– 0.75 km
• SM
At NOAA/NESDIS/STAR
– Gridded 550-nm AOT EDR
• regular equal angle grid: 0.25°x0.25°
(~28x28 km)
• only high quality AOT EDR is used
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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Summary
• Algorithm documents
– Jackson, J., H. Liu, I. Laszlo, S. Kondragunta, L. A. Remer, J.
Huang, H-C. Huang, 2013: Suomi-NPP VIIRS Aerosol Algorithms
and Data Products, J. Geophys. Res. doi: 10.1002/2013JD020449
– ATBD (Draft)
– OAD
• Product documents
– User’s Guide
– Readme files
• VIIRS Aerosol Calibration and Validation website
http://www.star.nesdis.noaa.gov/smcd/emb/viirs_aerosol/i
ndex.php
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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Summary (cont.)
• Product quality:
– Liu, H., L. A. Remer, J. Huang, H-C. Huang, S. Kondragunta, I.
Laszlo, M. Oo, J. M. Jackson, 2013: Preliminary Evaluation of
Suomi-NPP VIIRS Aerosol Optical Thickness, J. Geophys. Res. (in
review)
– Hongqing Liu: VIIRS Aerosol Products, Data Quality, and
Visualization Tools (VIIRS Aerosol Science and Operational Users
Workshop , November 21-22, 2013, College Park, MD)
• Link to CLASS:
– http://www.class.ncdc.noaa.gov/saa/products/welcome
• Link to gridded data:
– http://www.star.nesdis.noaa.gov/smcd/emb/viirs_aerosol/prod
ucts_gridded.php
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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Backup
VIIRS Aerosol Science and Operational Users Workshop, Nov 21-22, 2013, NCWCP, College Park, MD
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AOT Product Timeline
Products go trough various levels of maturity:
Initial
instrument check out;
Tuning cloud mask
parameters
28 Oct 2011
Beta status
2 May 2012
Error
Beta
status
Provisional
status
15 Oct 2012
28 Nov 2012
23 Jan 2013
Red period:
Product is not available to public, or product should not be used.
Blue period:
(Beta)
Product is available to public, but it should be used with caution,
known problems, frequent changes.
Green period:
(Provisional)
Product is available to public; users are encouraged to evaluate.
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