Retrieval algorithms used in GlobAEROSOL

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Retrieval algorithms used in GlobAEROSOL. Gareth Thomas gthomas@atm.ox.ac.uk. Introduction. The GlobAEROSOL project uses two separate aerosol retrievals: ORAC (Oxford-RAL Aerosol and Cloud) Used in the analysis of ATSR-2, AATSR and SEVIRI data ESA MERIS L2 Aerosol

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Retrieval algorithms used in GlobAEROSOL

Gareth Thomas

gthomas@atm.ox.ac.uk

Introduction

The GlobAEROSOL project uses two separate aerosol retrievals:

• ORAC (Oxford-RAL Aerosol and Cloud)
• Used in the analysis of ATSR-2, AATSR and SEVIRI data
• ESA MERIS L2 Aerosol
• The standard ESA L2 atmospheric correction aerosol product
ORAC
• ORAC is an optimal estimation retrieval:

Initial state estimate: x0A priori: xa

Run forward model: f(xi)

Compare to J = [y - f(xi)]Sy-1[y - f(xi)] +

measurements (y):[xi - xa]Sa-1[xi - xa]

Update state: xi→ xi+1(Levenburg-Marquardt)

Stop when:J is small , or when i is large.

What are “y” and “x”
• The ORAC measurement vector “y” consists of the TOA reflectance measured in each (vis/near-IR) channel
• These can have differing viewing geometries from each other
• The state vector “x” is made up of
• AOD at 550 nm
• Surface white sky albedo for one or more channels

Microphysical properties

Every component is characterized by:

Spectral refractive index

r(l) + i m(l)

log normal size distribution by number

Changing the mixing ratio between component we obtain the optical properties corresponding to different effective radii

Kext()

() < 1

P(, )

WE-Heraeus Seminar

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6/7 December 2007

MISR Data Users Science Symposium Gareth Thomas gthomas@atm.ox.ac.uk

Aerosol optical properties
• ORAC is run for 5 separate aerosol classes:

Maritime Clean† Continental Clean†

Biomass Burning‡ Desert Dust† Urban†

• From these types the “best” type is selected, based on how well the retrieval fit the measurements and a priori constraints.

†From OPAC, Hess et al., Bull. Am. Met. Soc., 831–844, 1998.

‡From Dubovik et al., J. Atmos. Sci., 590–608, 2002.

The Foward model

Aerosol Spectral

optical properties

Kext()

() < 1

Look Up Tables

Mie theory

(spherical approx.)

Instrument’s filter characterization

transfer model

DISORT

P(, )

Molecules

gas(,h)

Molecular scattering

Molecules + aerosol

m()

Pm(, )

Molecules

MODTRAN

computations

Aerosol

Microphysical

properties

Plane parallel

Atmosphere

tatm(,h)

atm(,h)

Refractive index

r() + i m()

Patm(,,h)

Size distributions

N(r)

Mixing ratio

Black surface

Gas absorption

profile

Surface reflectance
• For single view retrieval, ratio of surface reflectance at different wavelengths is fixed
• For dual view retrieval, surface reflectance is retrieved independently at each channel, but nadir-forward ratio is fixed
Fast forward model
• ORAC uses lookup tables of atmospheric reflection and transmission, calculated by DISORT, to predict TOA reflectance with the expression

LUT value

Retrieved value

• The ORAC algorithm produces the best fit to the observed radiances that can be achieved with the forward model:
• All of the supplied radiances are used to fit all of the retrieved parameters simultaneously
• The algorithm is easily adaptable for use with different instruments, or even multiple instruments at the same time
• The optimal estimation framework allows the inclusion of a priori knowledge in a rigorous way AND provides uncertainly estimates for every retrieval
MERIS aerosol
• MERIS uses different algorithms to derive AOD and Ångström coefficient over land and ocean.
• The ocean scheme is a “black ocean” algorithm
• The land scheme is a “dark-dense vegetation” algorithm
• Both were developed as part of atmospheric correction schemes for the retrieval of surface properties.
MERIS “black ocean” retrieval
• Assumes ocean is completely black in the near-IR (775 and 865 nm)
• Uses a range of aerosol classes to fit the radiance in the near-IR. The Ångström coefficient of the best-fitting classes is then used to derive the AOD at shorter wavelengths
• Multiple scattering is explicitly included in the retrieval
• Limitations:
• Doesn’t allow for multiple scattering between surface and atmosphere.
• Doesn’t allow for specular reflection off the surface
MERIS DDV retrieval
• The principle of DDV is that the reflectance of vegetated land surfaces can be predicted at certain wavelengths
• The MERIS algorithm uses 412, 442 and 665 nm
• The algorithm assumes that any residual radiance in these channels not accounted for by Rayleigh scattering or gas absorption (H2O or O2) is due to aerosol.
• 12 different aerosol models fitted
• The surface is treated as Lambertian reflector
An asideATSR-2 pixel counts
• ATSR-2 does not provide data from all channels all of the time
• Visible channels are put in narrow swath mode, where the edges of the swath are missing
• Some channels are dropped entirely, particularly in the forward view
• This is predominately done over the ocean, but also occurs over land
Effects of missing channels
• Over the ocean, dropped channels and narrow swath mode has a fairly insignificant effect
• Over the land it’s a different story
• Cloud flagging is very badly effected if short wavelength channels are missing
• The reduction of dual view information doesn’t help
• GlobAEROSOL ORBIT file don’t contain enough information to distinguish retrievals with missing channels
PIXELCOUNT files

New PIXELCOUNTS files have been developed for ATSR-2, which contain the number of instrument pixels for each channel included in each sinusoidal grid cell.

Another aside: comparing ATSRs

Together ATSR-2 and AATSR should provide a consistent 12 year record of aerosol optical depth

The instruments are very similar and follow the same orbit track separated by 30 minutes.

There are, however, important differences between the two instruments

ATSR-2’s low bandwidth modes

Although both instruments are very well calibrated, vicarious calibration of the visible channels has concentrated on removing changes in calibration, not absolute radiance calibration.

4 November 2008

GlobAEROSOL PM6

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Potential differences

The selection of channels were active on ATSR-2, plus the digitisation resolution used for ATSR-2.

Cloud flagging differences (due to differences in active channels)

Changes in atmospheric state in the 30 minutes between each measurement

Differences in speciation

4 November 2008

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Most basic comparison

550 nm AOD

Ångström Coeff.

AATSR

ATSR-2

September

2002

Most basic comparison

Number of points in average

Dominant aerosol class

AATSR

ATSR-2

September

2002

Differences in calibration

Collocated ATSR-2 and AATSR cloud free radiances

y = -0.005 + 1.02 x

y = -0.004 + 1.05 x

y = -0.003 + 1.04 x

y = -0.006 + 0.96 x

Differences in calibration

Offset = -0.007

ρ = 0.84

Offset = 0.013

ρ = 0.84

Offset = 0.010

ρ = 0.86

Offset = 0.005

ρ = 0.99

Changes in active channels

Offset = -0.008

ρ = 0.78

Offset = 0.019

ρ = 0.77

Offset = 0.010

ρ = 0.76

Offset = -0.0002

ρ = 0.98

Cloud flagging

cloud flag

ATSR-2 - AATSR

cloud flag

Speciation

Offset = 0.006

ρ = 0.55

Offset = 0.017

ρ = 0.63

Offset = 0.006

ρ = 0.27

Offset = 0.000

ρ = 0.99

Summary

Care must be taken if using ATSR-2 and AATSR as a single consistent dataset

Largest differences seem to be introduced by ATSR-2 low data-rate modes

Differences in speciation between the two products also introduce problems

Prior to 2000 the lack of MODIS BRDF products for setting a priori land surface reflectance is also an issue

Differences in absolute radiance calibration between the two instruments don’t seem to be significant for GlobAEROSOL