GOES-R ABI Aerosol Algorithms - PowerPoint Presentation

Slide1

GOES-R ABI Aerosol Algorithms

GOES-R Algorithm Working Group Aerosol, Atmospheric Chemistry and Air Quality (AAA) Application Team

1

Slide2

Presentations

Suspended Matter/Aerosol Optical Depth Algorithm – Istvan

Laszlo, STAR

Aerosol Detection Algorithm –

Shobha Kondragunta, STARProving Ground and User Interaction – Shobha Kondragunta, STAR

2

Slide3

3

3

3

Suspended Matter (SM)

Aerosol Optical Depth (

AOD

)

Presented by

Istvan Laszlo

With contributions from Mi Zhou,

Pubu

Ciren

, and

Hongqing

Liu

Slide4

4

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

Slide5

5

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

Slide6

6

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.

Slide7

7

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

Slide8

8

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.

Slide9

9

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

Slide10

10

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

Slide11

11

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

Slide12

12

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

Slide13

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

Slide14

14

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

Slide15

15

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.

Slide16

16

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

Slide17

17

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

Slide18

18

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.

Slide19

SM/

AOD Algorithm VerificationComparison with

MODIS

/Terra

19

ABI AOD

MODIS-ABI AOD

MODIS/Terra aerosol reflectances are used; 03/15/2012

Slide20

20

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

Slide21

21

Aerosol Detection

(Smoke & Dust)

Presented by

Shobha

Kondragunta

With contributions from

Pubu

Ciren

Slide22

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

Slide23

Physical 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

Slide24

Physical Basis of the Algorithm

24

Clear Sky

Thin Dust

Thick Dust

Slide25

25

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

Slide26

26

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

Slide27

Routine 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

Slide28

28

”Deep-Dive”

Validation Tools

Percentage of Pixels (%)

Slide29

Proving Ground and User Interaction

Shobha

Kondragunta

With contributions from P.

Ciren, C. Xu, H. Zhang29

Slide30

30

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/

Slide31

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

Slide32

32

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

Slide33

33

Proxy ABI Synthetic Natural Color

(RGB)

No green band on ABI

Algorithm development underway to improve RGB product

Slide34

Haboob (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

Slide35

Compared 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

Slide36

http://www.star.nesdis.noaa.gov/smcd/spb/aq/

NOAA’s IDEA Site

(dynamic flat

webpages

)