# OMAERO README File

B. Veihelmann, J.P. Veefkind, KNMI

Last update: November 23, 2007

## Overview

The OMAERO Level 2 data product contains aerosol characteristics such as
aerosol optical thickness (AOT), aerosol type, aerosol absorption
indices as well as ancillary information. The aerosol optical thickness
is retrieved from OMI spectral reflectance measurements and a best
fitting aerosol type is determined. The single-scattering albedo, the
layer height and the size distribution associated with the best fitting
aerosol type are provided as well as diagnostic information. Each file
contains the sunlit part of an OMI orbit, from the South Pole to the
North Pole. In the so-called global observation mode, the OMI swath is
approximately 2600 km wide, providing daily global coverage.

There are two OMI aerosol products: The OMAERUV product uses the near-UV
algorithm and is described in the [OMAERUV Readme
File](http://www2.knmi.nl/omi/research/product/aerosol/omaeruv.html).
The OMAERO product is based on the so-called multi-wavelength aerosol
retrieval algorithm and is described in the present document.

The multi-wavelength algorithm is based on the spectral information in
the near UV and the visible between 342.5 nm and 483.5 nm. Including the
near UV wavelength range enhances the capability of the retrieval to
distinguish between weakly absorbing and strongly absorbing aerosol
types. Therefore, the OMAERO product can provide additional information
on the aerosol type as compared to other aerosol products from sensors
that do not include the near UV such as MODIS, MISR, or POLDER.

Current OMAERO data over land are not recommended for quantitative
analyses because of possible errors due to the currently used surface
albedo climatology. A reprocessing with an improved surface albedo
climatology is envisaged beginning of the year 2007.

OMAERO is a standard product and has been publicly released in November
2007. OMAERO data are available at the [NASA Goddard Earth Science
DISC](http://disc.sci.gsfc.nasa.gov/Aura/OMI) (Data and Information
Services Center). For details about software versions and known problems
see the [OMAERO Release Specific
Information](#omaero-release-specific-information). For a detailed
description of the data format of the OMAERO products we refer to the
[Data Product Format Specification
document](http://www.knmi.nl/omi/documents/data/SD-OMIE-KNMI-645_Aerosol_PFS.pdf).
More information about the product can be found on the [KNMI OMI
website](http://www.knmi.nl/omi/research/product/aerosol/omaero.html).

Apart from the orbit-based level 2 product OMAERO, OMI aerosol data from
the multi-wavelength algorithm are available as geo-location based daily
global level 2 G product (OMAEROG). Each OMAEROG product contains the
essential aerosol data from the orbit-based OMAERO products of an entire
day. This product is also available at the [NASA Goddard Earth Science
DISC](http://disc.sci.gsfc.nasa.gov/Aura/OMI/).

## 1. Algorithm Description

The multi-wavelength algorithm uses the reflectance spectrum in the near
UV and the visible wavelength range. Including the near UV enhances the
capability of the retrieval to distinguish between weakly absorbing and
strongly absorbing aerosol types. The main reason for this is the
enhanced Rayleigh scattering in the near UV. Furthermore, mineral dust
aerosol can be distinguished from weakly absorbing material by its
strong absorption in the UV. Therefore, the OMAERO product can provide
additional information on the aerosol type as compared to other aerosol
products from sensors that do not include the near UV such as MODIS,
MISR, or POLDER. The retrieval strategy of the multi-wavelength
algorithm and the assumptions made are discussed briefly in this
section. More information on the multi-wavelength algorithm can be found
in the [OMI Algorithm Theoretical Basis Document
(ATBD)](http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/viewInstrument.php?instrument=13)
\[RD1\].

Figure 1 shows a flowchart that sketches the essential processing steps.
For cloud-free scenes, aerosol parameters are derived from the
sun-normalized radiance spectrum (hereafter referred to as
*reflectance*) defined as

$R(\lambda) = \frac{\pi E(\lambda)}{\text{cos}(\theta_{s})F(\lambda)}$
(1)

where *E*(*λ*) and *F*(*λ*) are the Earth radiance and the solar
irradiance spectrum respectively and θ~s~ is the solar zenith angle. In
the current implementation the multi-wavelength algorithm uses 14
wavelength bands, which are about 1 nm wide and are located at 342.5,
367.0, 376.5, 388.0, 399.5, 406.0, 416.0, 425.5, 436.5, 442.0, 451.5,
463.0, 477.0, and 483.5 nm. The wavelength band at 477 nm, which
comprises an O~2~-O~2~ absorption band, is used in the OMI cloud product
[OMCLDO2](http://www2.knmi.nl/omi/research/product/Cloud/omcldo2.html)
to derive cloud height data. This band has been included in the
multi-wavelength algorithm in order to obtain more information about the
aerosol height. The other bands are essentially free from molecular
absorption and strong Raman signatures. A simulation study on the
aerosol information content of OMI spectral reflectance measurements
\[RD2\] shows that OMI measurements contain two to four degrees of
freedom of signal. This imposes a limit on the number of aerosol
parameters that can be retrieved independently and indicates that the
surface reflection cannot be retrieved from OMI measured reflectance
spectra independently from the aerosol parameters. The multi-wavelength
algorithm accounts for these limitations using the strategies described
in the following.

The multi-wavelength algorithm uses a set of aerosol models defined by
size distribution, complex refractive index, and aerosol layer altitude.
The models are representative for four major aerosol types including
desert dust, biomass burning, volcanic and weakly absorbing aerosol. The
particle shape is assumed to be spherical for all aerosol types, except
for desert dust. The non-sphericity of desert dust particles is taken
into account using the spheroidal shape approximation. In this
approximation the irregular particles are represented by an ensemble of
randomly oriented spheroidal model shapes. The single-scattering
properties of the dust aerosol models have been extracted from a
pre-computed light scattering database, assuming the shape distribution
that is used in AERONET retrievals for non-spherical aerosol types \[RD
3\]. ![](media/image1.jpeg){width="6.290277777777778in"
height="4.445138888888889in"}

*Figure 1: Flowchart of the multi-wavelength algorithm.*

The choice of the aerosol models is made such that the all important
aerosol types that are present in the Earth's atmosphere are
represented. Note that the aerosol retrieval is constrained implicitly
by this choice of the aerosol models. The on-line radiative transfer
calculations are based on a pre-computed Look-Up Table (LUT) containing
the radiative properties of aerosol containing atmospheres. The
radiative coupling of the atmosphere with the surface is performed
on-line. This approach allows efficient forward simulations for scenes
with a given aerosol model, AOT, viewing geometry and given surface
reflection properties. The surface reflection is taken from an ocean
model or a land albedo climatology (see Section 1.1).

The aerosol parameters are determined by evaluating the merit function
Ψ*~m~*

$\Psi_{m} = \sum_{}^{}\left( \frac{R^{}(\lambda_{l}) - R_{m}(\tau(\lambda_{\text{ref}}),\lambda_{l})}{\varepsilon(\lambda_{l})} \right)^{2}$
(2)

where *R\**(*λ*~l~) is the measured reflectance, *R~m~(τ(λ~ref~),λ~l~)*
is the reflectance for the aerosol model *m* as a function of the
aerosol optical thickness at the reference wavelength *τ(λ~ref~)*, and
*ε(λ~l~)* is the error in the measured reflectance. The optimal aerosol
optical thickness is determined for each aerosol model by a non-linear
fitting routine using a modified Levenberg-Marquardt method \[RD4\]. The
choice of the aerosol models made by the retrieval is constrained in a
post-selection that uses an aerosol main type climatology. The aerosol
main type climatology has been derived from a chemical transport model
(TM5) for the year 2005. In this way only aerosol types are allowed that
can be expected for a given geo-location and time of the year. Note that
the aerosol climatology can allow for more than one aerosol main type
for a given scene. Amongst the post-selected aerosol models the aerosol
model with the smallest Ψ*~m~* is selected for the present ground pixel.
The SSA, the size distribution and the aerosol height are directly
determined by the selected aerosol model. A cloud screening scheme is
applied in order to exclude cloudy scenes from the retrieval; ocean
scenes that are affected by specular reflection at the sea surface
(sun-glint) are flagged such that they can be excluded from the analysis
of the retrieved data (see Section 1.2).

The level 2 product also contains information on the quality of the
retrieved aerosol parameters. The precision in the retrieved AOT is
given in terms of the retrieval error of the non-linear fitting routine.
The AOT, SSA and Root Mean Square (RMS) of the residual reflectance of
up to ten aerosol models with an RMS lower than a given threshold value
are included in order to provide information about how well the best
selected aerosol model is defined.

### 1.1 Surface reflection

The surface reflection cannot be retrieved from OMI measured reflectance
spectra independently from the aerosol parameters. Over oceans the
spectral bidirectional reflectance distribution function (BRDF) of the
surface is computed using a model that accounts for the chlorophyll
concentration of the ocean water and the near-surface wind speed
\[RD5\]. The chlorophyll concentration is taken from a climatology that
has been derived from﻿ monthly averaged data from the MODIS ocean color
product. Over land the surface albedo spectrum is taken from a global
seasonally resolved climatology assuming Lambertian reflectance at the
surface. Datasets for the Lambertian equivalent surface albedo spectrum
of land scenes are currently available from sensors such as GOME
\[RD6\], TOMS \[RD7\], MODIS or MISR (Multiangle Imaging
SpectroRadiometer). Analyses of these surface data has shown that none
of these datasets is optimal for being used in the multi-wavelength
algorithm: The GOME dataset is affected by cloud contamination, which is
related to the large pixel size. The TOMS dataset is limited to the UV,
whereas the shortest wavelength that is reported in the MODIS and MISR
surface data products are 470 nm and 446 nm respectively. Merged
datasets suffer from radiometric inconsistencies between the sensors.
Amongst the currently available datasets an extrapolated surface albedo
climatology from MISR data appears to perform best in the context of
aerosol retrieval using the multi-wavelength algorithm \[RD8\].
Currently, a new surface albedo climatology is being generated that is
derived from the three-year OMI dataset. Naturally, no spectral
extrapolation is necessary when employing this climatology in the OMI
aerosol retrieval. The relatively small pixel size of OMI yields a
probability of observing cloud free scenes that is sufficiently high in
order to extract surface reflection properties from OMI reflectance
spectra. It is expected that this new surface albedo climatology will
improve the aerosol retrieval over land significantly.

### 1.2 Cloud and Sun-glint screening 

Three tests are applied in order to exclude cloudy scenes from the
retrieval. The first test excludes bright scenes with very low
absorption using reflectance data in combination with the UV aerosol
index. Scenes with a reflectance larger than the threshold value of 0.3
and a UV aerosol index below 0.12 (low absorption) are classified as
cloudy. The second test uses cloud fraction data from the OMI O~2~-O~2~
cloud product
([OMCLDO2](http://www2.knmi.nl/omi/research/product/Cloud/omcldo2.html)).
Scenes with a cloud fraction larger than 0.34 are classified as cloudy.
The third test is based on the spatial homogeneity of the scene. OMI
level 1b radiance data are spatially binned. OMI level 1b radiance data
contain the small pixel variance, which is a measure for the spatial
homogeneity of the scene within one pixel. Inhomogeneous scenes with a
small pixel variance larger than 0.00015 times the average radiance
value (standard deviation larger than \~1 % of the average radiance
value) are classified as cloudy. Note that the spatial homogeneity test
is capable of screening out a large part of the cloudy scenes. A large
cloud fraction threshold is hence chosen in order to avoid screening out
of desert dust scenes to which the O~2~-O~2~ cloud product tends to
assign large cloud fraction values. Note that unrecognized cloud
contamination leads to an overestimation of the AOT and is considered to
be one of the most important error sources.

Ocean scenes that are affected by specular reflection at the sea surface
(sun-glint) should be excluded from analyses of the retrieved aerosol
data. Sun-glint screening can be performed using the sun-glint warning
flag (bit 4 of the ground pixel quality flag) provided in the OMAERO
product. The sun-glint warning flag is raised when the sun-glint angle
(emergent zenith angle relative to the specular reflection angle
\[RD9\]) is smaller than 40 degrees.

## 2 Data Quality Assessment

Various validation studies have been made or are in progress for the
aerosol optical thickness from the OMAERO product using ground based,
airborne, and satellite based observations. Note that the results of
current validation studies for land scenes are affected by issues
related to the currently used surface albedo climatology. The validation
studies for land will be updated and extended once the new surface
albedo climatology from OMI data is implemented.

For ocean scenes, global AOT data from the OMAERO product have been
compared with measurements from other satellite instruments. Here we
show such a comparison for the period of June 2006.

![](media/image2.png){width="2.8570866141732285in"
height="2.8570866141732285in"}![](media/image3.png){width="2.8570866141732285in"
height="2.8570866141732285in"}

*Figure 2: AOT from the OMAERO product compared with quality assured
data from the MODIS standard product (left) and with quality parameter
filtered data from POLDER (right).*

The comparison with quality assured data from the MODIS standard product
(Fig.2, left) shows excellent agreement between the datasets. For this
comparison only quality assured MODIS data (QA flag=3) have been
included. By using this data flag many partly clouded OMI scenes are
excluded that are not recognized as being cloudy by the OMI cloud
screening scheme. When OMI data are used alone, unrecognized cloud
contamination is an important source of errors. For the comparison with
POLDER on the PARASOL platform, the data have been filtered based on the
quality parameter given by the POLDER aerosol algorithm. A correlation
coefficient of larger than 0.8 and a slope of the regression line of
1.07 indicate a good agreement between the datasets.

For land scenes, we report the results of various validation studies.
Curier et al. (2007) \[RD8\] have investigated the AOT data from the
OMAERO product for Europe and adjacent oceans. A comparison with MODIS
AOT data yields correlation coefficients between 0.76 and 0.81 for ocean
and between 0.59 and 0.70 for land. The difference between land and sea
is due to shortcomings in the currently used surface albedo climatology.
In the same study strongly site-dependent correlations are reported for
comparisons of the AOT from the OMAERO product with ground based data
from various AERONET stations in Europe.

The level 2 product contains diagnostic information about the quality of
the fit. Typical values for the retrieval error of the AOT obtained
using the non-linear fitting routine are below 0.03. This error concerns
the AOT retrieval for a given aerosol model and hence does not include
error correlations of AOT and microphysical aerosol parameters or the
aerosol height. The standard deviation of the AOT values of the aerosol
models with an RMS lower than a given threshold is below 0.11 for 95% of
the cases. The standard deviation of the SSA values of the aerosol
models with an RMS lower than a given threshold is below 0.1 in 95% of
the cases.

## 3 Product Description

A single OMAERO product file contains all OMI measurements on the sunlit
portion of the Earth, for one Aura orbit. During one orbit OMI performs
approximately 1650 measurements, which take 2 seconds each. In the
global observation mode, 60 across track ground pixels are measured
simultaneously during a measurement. These 60 measurements cover a swath
of approximately 2600 km wide. During so-called zoom-in measurements the
swath width is reduced. The operational baseline includes zoom-in
measurements for 1 day a month. In case of zoom-in measurements 30 of
the 60 across track pixels contain data, the other 30 contain fill
values.

Aerosol characteristics such as aerosol indices, the aerosol optical
thickness and the aerosol model that has been selected as the best
fitting model by the multi-wavelength algorithm, are provided in the
product file for each ground pixel. The aerosol optical thickness and
the single-scattering albedo of the best fitting model is given for all
wavelengths used in the retrieval. Additionally, the aerosol optical
thickness and the single-scattering albedo of models with a merit
function below a given threshold are provided for the diagnostic
wavelengths 342.5, 367.0, 388.0, 406.0, 425.5, 442.0, 463.0, 477.0, and
483.5 nm (see attribute data fields in the product file). The standard
deviation of the AOT and SSA values of the models that passed the
threshold, given in the level 2 product, is a measure for how well the
best fitting model is defined by the retrieval. An error-weighted
average of AOT and SSA of the models that passed the threshold can be
used alternatively to the AOT of the best fitting model. A complete
description of the format of the OMAERO data product is available in the
[Product Format
Specification](http://www2.knmi.nl/omi/research/product/aerosol/SD-OMIE-KNMI-645_Aerosol_PFS.pdf).

The OMAERO product is written as an HDF-EOS5 swath file. For a list of
tools that read HDF-EOS5 data files, please visit this link:
<http://disc.gsfc.nasa.gov/Aura/tools.shtml>. The OMI level 2 products
can be read using an [IDL reading
routine](http://www.knmi.nl/omi/documents/data/read_omi_level2.pro) that
can be found at the [KNMI product
site](http://www.knmi.nl/omi/research/product/aerosol/omaero.html).

## 4 Data Availability

The OMAERO product is available through the [NASA Goddard Earth Science
DISC](http://disc.sci.gsfc.nasa.gov/Aura/OMI). Subsets of these data
over many ground stations and along Aura validation aircraft flights
paths will also be made available through the [Aura Validation Data
Center (AVDC) website](http://avdc.gsfc.nasa.gov/)) to those
investigators who are associated with the various Aura science teams.
[Bojan Bojkov](mailto:Bojan.Bojkov@avdc.gsfc.nasa.gov") is the point of
contact at the AVDC.

## 5 Contact Information

Questions related to the OMAERO dataset should be directed to
<help-disc@listserv.gsfc.nasa.gov>. For questions and comments related
to the OMAERO algorithm and data quality please send a mail to
<omaero@ltpmail.gsfc.nasa.gov>.

# OMAERO Release Specific Information

Ben Veihelmann, Pepijn Veefkind\
November 26, 2007

## Software Version

This information applies to the software version 1.0.6 and higher.

## ECS Collection Number

The OMAERO v1.0.1 is provisionally released for ECS Collection 2.

The OMAERO v1.0.3 is provisionally released for ECS Collection 3.

The OMAERO v1.0.6 is publicly released for ECS Collection 3.

## Known Issues List

The following contains a list of known issues in the OMAERO data
product:

1.  The cloud screening scheme does not detect all cloud contaminated
    pixels. The retrieved aerosol parameters at some pixels may
    therefore be affected by the presence of clouds.

2.  The surface albedo climatology contains erroneous values for part of
    the desert regions. Results of aerosol retrievals over deserts are
    therefore not reliable. It is expected that this problem will be
    mitigated significantly by implementing an improved surface albedo
    climatology derived from OMI data, which is expected to become
    available in winter 2007.

3.  In some cases the multi-wavelength algorithm tends to select aerosol
    models with too large absorption.

4.  The radiometric calibration shows inconsistencies in the
    across-track direction causing along-track stripes in the aerosol
    indices provided by the OMAERO product.

5.  In some rare cases (about 5 pixels per one orbit of about 99000
    pixels) the fields AersolOpticalThicknessPrecision,
    RootMeanSquareErrorOfFitPassedThreshold,
    AerosolOpticalThicknessPassedThresholdStd and
    SingleScatteringAlbedoPassedThresholdStd contain the erroneous value
    of -32768 in about 5 pixels per orbit (\~ 99000 pixels). Note that
    the fill value for this field is -32767. This error will be
    corrected in the next update of the code.

6.  The title in the attributes of the data fields
    AerosolOpticalThicknessPassedThresholdStd and
    SingleScatteringAlbedoPassedThresholdMean is erroneous. Note that
    the field names are correct.

## Release History

### OMAERO v1.0.6:

1.  Public release November 2007

2.  Provisional release in December 2006.

### Related Documents

\[RD1\] Torres, O., R. Decae, J.P. Veefkind, and G. de Leeuw (2002): OMI
Aerosol Retrieval Algorithm, in OMI Algorithm Theoretical Basis
Document: Clouds, Aerosols, and Surface UV Irradiance, Vol. 3, version
2, (OMI-ATBD-03, P. Stammes, Ed.),
http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/OMI/ATBD-OMI-03.pdf.

\[RD2\] Veihelmann, B., P.F. Levelt, P. Stammes and J.P. Veefkind
(2007): Aerosol Information Content in OMI Spectral Reflectance
Measurements, Atmos. Chem. Phys., 7, 3115-3127.

\[RD3\] Dubovik, O., et al. (2006): Application of spheroid models to
account for aerosol particle nonsphericity in remote sensing of desert
dust, J. Geophys. Res., 111, D11208, doi:10.1029/2005JD006619.

\[RD4\] More, J.J. (1978): The Levenberg-Marquardt algorithm:
implementation and theory. In Numerical Analysis Proceedings (Dundee,
June 28 - July 1, 1977, G. A.Watson, Editor), Lecture Notes in
Mathematics 630, Springer-Verlag.

\[RD5\] Veefkind J.P. and G. de Leeuw (1997): A new aerosol retrieval
algorithm applied to ATSR-2 data Journal of Aerosol Science, Volume 28,
Supplement 1, pp. 693-694(2).

\[RD6\] Koelemeijer, R.B.A., J. F. de Haan and P. Stammes (2003): A
database of spectral surface reflectivity in the range 335--772 nm
derived from 5.5 years of GOME observations, J. Geophys. Res., Vol. 108
(D2), 4070.

\[RD7\] Herman, J.R., and E. Celarier (1997): Earth surface reflectivity
climatology at 340 and 380 nm from TOMS data, J. Geophys. Res., Vol.
102, 12,059-12,076.

\[RD8\] Curier R.L., J.P. Veefkind, R. Braak, O.Torres, G. de Leeuw
(2007): Retrieval of aerosols optical properties from OMI radiances
using a multi-wavelength algorithm: Application and validation to
Western Europe, J. Geophys. Res., in press.

﻿\[RD9\] King, M.D., Y.J. Kaufman, D. Tanre, and T. Nakajima (1999):
Remote Sensing of Tropospheric aerosols from Space: Past, present and
future, Bull. Am. Meteorol. Soc,. 80, 2229-2259.