GOESR Algorithm Working Group Aerosol Atmospheric Chemistry and Air Quality AAA Application Team 1 Presentations Suspended MatterAerosol Optical Depth Algorithm Istvan Laszlo STAR Aerosol Detection Algorithm ID: 810794
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Slide1
GOES-R ABI Aerosol Algorithms
GOES-R Algorithm Working Group Aerosol, Atmospheric Chemistry and Air Quality (AAA) Application Team
1
Slide2Presentations
Suspended Matter/Aerosol Optical Depth Algorithm – Istvan
Laszlo, STAR
Aerosol Detection Algorithm –
Shobha Kondragunta, STARProving Ground and User Interaction – Shobha Kondragunta, STAR
2
Slide33
3
3
Suspended Matter (SM)
Aerosol Optical Depth (
AOD
)
Presented by
Istvan Laszlo
With contributions from Mi Zhou,
Pubu
Ciren
, and
Hongqing
Liu
Slide44
4
4
The aerosol portion of the atmospheric radiation (
aerosol reflectance
) observed by satellites is determined by the amount and type (size, shape and chemical composition) of aerosol.
Over dark surfaces, aerosol reflectance increases with increasing amount of aerosol (as measured by AOD)
→
Used for estimating AOD
The spectral dependence of aerosol reflectance is a function of aerosol type.
→ Used for estimating aerosol type (model)
SM/
AOD
Retrieval:
Physical Basis
model 1: urban; model 2: smoke; top: 0.64
μ
m, bottom: AOD=0.4;
solar zenith angle = 40
o;
view zenith angle = 40
o;
relative azimuth = 180
o
Slide55
5
5
SM/
AOD
Algorithm:
Features of the
GOES-R/
ABI
SM/
AOD
algorithm:
Based on the MODIS/VIIRS heritagesSeparate algorithms for land and waterUses multiple channels to estimate AOD and aerosol typeAdvantages
A lot of ground work has already been done with MODIS Has been tested in an operational environmentPotential synergy with MODIS/VIIRS aerosol productEstimates aerosol typeDisadvantages
Sensitive to radiometric error (multi-channel retrieval)No retrievals over bright surface (sun-glint, bare soil, desert)Dependence on aerosol model assumptions
Over land, uses Lambertian surface model and spectral regression with large variance for surface albedo, which can lead to large AOD error for not dark enough surface
Slide66
6
6
Aerosol retrieval is accomplished by comparing observed spectral reflectances with calculated ones.
AOD and aerosol model corresponding to calculated reflectances best matching observed ones are selected as solutions.
SM/
AOD
Retrieval:
Illustration of Methodology
TOA reflectance in blue band
TOA reflectance in red band
aerosol model 1
aerosol model 2
Residual 1
Residual 2
Retrieved AOD550
Observation
Illustration of aerosol retrieval concept
AOD and model is from the “minimum” residual between observed and calculated spectral reflectances.
Residual 2 < Residual 1, so retrieved AOD
≈ 1.0 and aerosol model is model 2.
Slide77
7
7
SM/
AOD
Algorithm Input
Sensor Input
ABI Band
Wavelength
Range
(μm)
Central
Wavelength
(μm)
Central
Wavenumber
(cm-1)
Sub-satellite
IGFOV
(km)
Sample Use
1
0.45-0.49
0.47
21277
1
AOD Land
2
0.59-0.69
0.64
15625
0.5
AOD Land and Ocean
3
0.846-0.885
0.865
11561
1
AOD Ocean
4
1.371-1.386
1.378
7257
2
5
1.58-1.64
1.61
6211
1
AOD Ocean
6
2.225 - 2.275
2.25
4444
2
AOD Land and Ocean
Estimate land surface reflectance
7
3.80-4.00
3.90
2564285.77-6.66.191616296.75-7.156.9514392107.24-7.447.3413622118.3-8.78.511762129.42-9.89.61104121310.1-10.610.3596621410.8-11.611.289321511.8-12.812.381321613.0-13.613.37522
land only
both land and ocean
ocean only
Slide88
SM/
AOD
Mathematical Description
Calculation of TOA Reflectance
TOA reflectance
atmospheric contribution
surface contribution
The satellite-observed reflectance (
ρ
toa
) is approximated as the sum of atmospheric (
ρ
atm
) and surface components (
ρ
surf
)
Calculated reflectances account for transmission and absorption of radiation in the atmosphere and reflection at the surface.
Atmospheric reflectances and transmittances are pre-calculated using the 6S RTM (
Vermote et al
., 1997) and stored in LUT for speed.
Surface reflectance of ocean is calculated; that over land is retrieved. → Separate algorithms for aerosol retrieval over ocean and land.
Slide99
9
9
SM/AOD Mathematical Description
Atmospheric Contribution
9
gas transmittance
atmosphere LUT
Calculation of atmospheric reflectance term
ρ
R+A
: reflectance due to molecules (R) and aerosol (A) together – calculated with 6S RTM and stored in LUT
ρ
R
: reflectance due to molecules – calculated in the code following 6S;
P
0
and
P
are standard and actual pressures, respectively
T
: gas transmittance (parameterized)
O
3
, O
2
, CO
2
, N
2
O, CH
4
molecules, aerosol, H
2
O
top of atmosphere
bottom of atmosphere
Slide1010
SM/
AOD
Mathematical Description
Surface Contribution
gas transmittance
atmosphere LUT
land and ocean reflectances
Calculation of surface reflectance term
Total (direct+diffuse) downward and upward transmittance
T
R+A
and spherical albedo
S
R+A
of molecular and aerosol atmosphere are calculated with 6S RTM and stored in LUT
Slide1111
SM/
AOD
Mathematical Description
Ocean Surface Reflectance
11
11
Water reflection includes three components:
Water-leaving radiance (
Lambertian
)
Whitecap (
Lambertian
)
Sunglint
(bi-directional)
Whitecap effective reflectance
Wind speed (m/s)
ρ
wc
corresponds
to constant chlorophyll concentration (0.4 mg m
-3
)
ABI
Channel (wavelength in µm)
1 (0.47)
0.2200
0.0148
2 (0.64)
0.2200
0.0013
3 (0.865)
0.1983
0.0
5 (1.61)
0.1195
0.0
6 (2.25)
0.0475
0.0
Slide1212
Formulation follows 6S RTM
Cox and Munk (1954) ocean model
Constant salinity (34.3 ppt)
Fixed westerly wind direction
12
12
SM/
AOD
Mathematical Description
Ocean Surface Reflectance
Term 2
Term 3
Term 4
Term 5
Sunglint
Term 1
calculated
Sunglint LUT
All , , and from atmosphere LUT
13
13
SM/AOD Mathematical Description
Land Surface Reflectance
a
Lambertian reflection is assumed.
Surface reflectances at 0.47 (ρ
0.47
) and 0.64 μm (ρ
0.64
) are estimated from those at 2.25 μm (ρ
2.25
).
Use
NDVI to separate vegetation- and soil-based surface types (VIIRS approach)
For vegetation-based surface
For soil-based surface
Surface reflectances in the visible and NIR ABI channels
Mid-IR NDVI
Slide1414
SM/AOD Mathematical Description
Selection of Dark Pixel
Land – select pixels with low SWIR reflectance:
0.01 ≤
ρ
2
.25
μ
m
≤ 0.25
Ocean – avoid areas effected by glint:glint angle θg > 40oθg
is the angle between the viewing direction θv
and the direction of specular reflection θs
:
θ
g
= cos
-1
( cos
θ
s
cos
θ
v
+
sin
θ
s
sin
θ
v
cos
Φ
)
θ
s
θ
s
θ
v
θ
g
Φ
Z
Slide1515
15
15
SM/
AOD
Mathematical Description
Aerosol Models
LAND
: Four aerosol models: dust, smoke, urban, generic (
MODIS
C5,
Levy et al
., 2007)
Single scattering albedo and asymmetry parameter as a function of wavelength for the four land aerosol models
WATER
: Four fine mode and five coarse mode aerosol models (MODIS C5)
Single scattering albedo and asymmetry parameter as a function of wavelength for the fine (left) and coarse mode (right) models over ocean.
Slide1616
16
16
calculate TOA reflectance at 0.47
µ
m
match 0.47um observation ?
calculate residual at 0.64
µ
m
Each aerosol
model
Lookup Table
Satellite & Ancillary Data
Increase AOD at 550nm
retrieved
AOD
Select the aerosol model and AOD with the minimum residual as the “best” solution
Retrieve
ρ
2.25
,
AOD and aerosol model simultaneously by matching the observed TOA reflectance of the reference channel 0.47
µ
m and calculate the corresponding residuals at 0.64
µm
for each of the four aerosol models
where residual is calculated as:
SM/AOD Mathematical Description
SM/AOD Retrieval over Land
Y
N
Slide1717
SM/
AOD
Mathematical Description
SM/AOD Retrieval over Ocean
17
17
TOA reflectance is assumed to be a linear combination fine and coarse mode aerosols
Retrieve AOD and fine mode weight for each combination of candidate fine and coarse aerosol models.
For each fine &
Coarse model
combination
match 0.87
μ
m obs.?
Change fine mode weight
calculate TOA reflectance in ABI channel
calculate residuals in channels 2, 5 & 6
Lookup Table
Satellite & Ancillary Data
Increase AOD at 550nm
retrieved
AOD
Minimum residual?
residual
retrieved
AOD &
Weight &
residual
Select the AOD and combination of fine and coarse modes with minimum residual as the “best” solution.
where residual is calculated as:
Y
N
Slide1818
18
18
SM/
AOD
Mathematical Description
Size Parameter and SM
The Ångström exponent (
α
) is used as proxy for particle size:
Large/small values of Ångström exponent indicate small/large particles, respectively.
The Ångström exponent is calculated from AODs and two pairs of wavelengths (MODIS heritage):
SM: The retrieved AOD is scaled into column integrated suspended matter in units of µg/cm
2
using a mass extinction coefficient (cm
2
/µg) computed for the aerosol models identified by the ABI algorithm.
SM/
AOD Algorithm VerificationComparison with
MODIS
/Terra
19
ABI AOD
MODIS-ABI AOD
MODIS/Terra aerosol reflectances are used; 03/15/2012
Slide2020
20
20
SM/
AOD
Algorithm Verification
Comparison with
AERONET
Retrievals are from
MODIS
Terra and Aqua from 2000-2009
All available
AERONET
stationsAOD at 550
nm
Same overall performance of MODIS
and
ABI
over land
Slightly smaller overall
ABI
bias over water
Land
Water
Slide2121
Aerosol Detection
(Smoke & Dust)
Presented by
Shobha
Kondragunta
With contributions from
Pubu
Ciren
22
Aerosol Detection
Sensor Inputs
Future
GOES
Imager
(ABI)
Band
Nominal
Wavelength
Range
(μm)
Nominal Central
Wavelength
(μm)
Nominal Central
Wavenumber
(cm-1)
Nominal
sub-satellite
IGFOV
(km)
Sample Use
1
0.45-0.49
0.47
21277
1
Dust/Smoke
2
0.59-0.69
0.64
15625
0.5
Dust/Smoke
3
0.846-0.885
0.865
11561
1
Dust/Smoke
4
1.371-1.386
1.378
7257
2
Dust
5
1.58-1.64
1.61
6211
1
Dust/Smoke
6
2.225 - 2.275
2.25
4444
2
Smoke
7
3.80-4.003.9025642Dust/Smoke85.77-6.66.191616296.75-7.156.9514392107.24-7.447.3413622118.3-8.78.511762129.42-9.89.61104121310.1-10.610.3596621410.8-11.611.28932Dust/Smoke1511.8-12.812.38132Dust/Smoke1613.0-13.613.37522
Input for both
Dust and smoke
Input for smoke
Input for dust
Slide23Physical Basis of the Algorithm
Aerosols, surface, and clouds have different spectral and spatial characteristics
Aerosol and surface signals can be separated through analysis of spectral differences in BTs and
reflectances
Cloud mask information is passed on by the cloud algorithm but internal tests for additional cloud screening and snow/ice have been implementedThresholds based on simulations and observations from existing satellite instruments.
23
Slide24Physical Basis of the Algorithm
24
Clear Sky
Thin Dust
Thick Dust
Slide2525
Spectral (wavelength dependent) thresholds can separate thick smoke, light smoke, and clear sky conditions
Physical Basis of the Algorithm
Heavy smoke
clear
smoke
Clear Regime
Smoke
Regime
Thick Smoke
Regime
Slide2626
Aerosol Detection –
Example
Global Smoke/Dust Flags
(May 26, 2008)
smoke dust
ABI smoke/dust detection algorithm is tested by using MODIS as proxy data
Biomass
burning
Saharan
desert dust
Mongolia
desert dust
Slide27Routine Validation Tools
Product validation: using CALIPSO Vertical Feature Mask (VFM) as truth data (retrospective analysis not near real time. Data downloaded from NASA/
LaRC
)
Tools (IDL)Generates match-up dataset between ADP and VFM along CALIPSO track, spatially (5 by 5 km) and temporally (coincident) Visualizing vertical distribution of VFM and horizontal distribution of both ADP and VFM
Generating statistics matrix
27
Slide2828
”Deep-Dive”
Validation Tools
Percentage of Pixels (%)
Slide29Proving Ground and User Interaction
Shobha
Kondragunta
With contributions from P.
Ciren, C. Xu, H. Zhang29
Slide3030
Air Quality Proving Ground (AQPG)
NOAA has created the AQPG – a subset of the GOES-R Proving Ground – focusing on the aerosol products that will be available from the ABI.
Goal
: build a user community that is ready to use
GOES-R air quality products as soon as they become available.
This distinction is important because the air quality community has very different needs than the majority of NOAA users (NWS meteorologists).
AQPG is using
simulated GOES-R ABI data
for training and interaction with the user community.
http://alg.umbc.edu/aqpg/
Proxy ABI Aerosol Optical Depth
AOD indicates areas of high particulate concentrations in atmosphere
AOD is
unitless
; high AOD values (yellow, orange, red) indicate high particulate concentrationsClouds block AOD retrievals31
Slide3232
Proxy ABI Aerosol Type
New product - not available with current GOES imager
Qualitative and untested
Useful for distinguishing between smoke and dust but can be noisy, especially at low AOD values
Slide3333
Proxy ABI Synthetic Natural Color
(RGB)
No green band on ABI
Algorithm development underway to improve RGB product
Slide34Haboob (intense dust storm) over Pheonix, Arizona in the evening of July 5, 2011.
Photo by Nick Ozac/ The Arizona Republic
MODIS RGB Image (bottom left) and Aerosol Optical Depth (bottom right)
the next morning
during Terra overpass show widespread dust. Neither Aqua nor Terra captured the event as it happened on July 5th because it happened at the night fall
Slide35Compared to a single snapshot of Terra overpass (bottom) the morning after haboob, 30-min refresh rate movie of GOES shows changing dust plume features. However, note the noise in GOES data.
For GOES-R, 5-min refresh rates with good quality “MODIS-like retrievals” will be the norm
to track episodic events such as dust storms and smoke plumes..
Widespread dust over Phoenix on July 6
th
: the remnant of the
haboob
http://www.star.nesdis.noaa.gov/smcd/spb/aq/
NOAA’s IDEA Site
(dynamic flat
webpages
)