# OMAERUV README File

Released October 31, 2007

## Overview

This document provides a brief description of the OMAERUV Level-2
aerosol data product derived from observations by the Ozone Monitoring
Instrument (OMI) on the EOS-AURA satellite. The information in this
README file applies only to the public release version 3 of the OMAERUV
data. As subsequent data versions are produced and released, the README
file will be updated accordingly to reflect the latest algorithm
modifications and data quality assessments. The OMAERUV algorithm is one
of two OMI algorithms that will be used to characterize the atmospheric
aerosol load. A spectral fit algorithm (OMAERO) that uses measurements
at several wavelengths in the range 350-500 (developed and maintained by
KNMI in The Netherlands) is also applied to the OMI observations.

The OMAERUV data product consists of the UV aerosol index (UVAI),
aerosol extinction optical depth (AOD) and aerosol absorption optical
depth (AAOD) at 354, 388 and 500 nm. AOD is a dimensionless measure of
the extinction of light by aerosols due to the combined effect of
scattering and absorption, while AAOD is that due to aerosol absorption
only. A visible aerosol index (VISAI) is also calculated.

The UVAI is a measure of the departure of the spectral dependence of the
near UV upwelling radiation at the top of the actual surface-atmosphere
system from that of a hypothetical pure molecular atmosphere bounded at
the bottom by a wavelength independent Lambertian surface. The UVAI
concept was developed empirically from TOMS observations. The
mathematical definition of UVAI is given in the algorithm description
section. Near-zero values of UVAI result when the radiative transfer
processes accounted for in the simple Rayleigh scattering model
adequately explain the observations. In the absence of calibration
difficulties non-zero residues are produced solely by geophysical
effects, of which absorbing aerosols are by far the most important
source of positive UVAI values. Non-absorbing aerosols yield small
negative UVAI values but the difficulty to separate the non-absorbing
aerosol signal from other non-aerosol related effects limits its
usefulness.

The UVAI has become an invaluable tool for tracking long-range transport
of absorbing aerosols (smoke and dust) throughout the globe, even when
the aerosols are over clouds. The UVAI has been instrumental in the
discovery of important aspects of aerosol transport both horizontally
and vertically. For instance, UVAI observations indicate that smoke
aerosol plumes generated by boreal forest fires at mid and high
latitudes are associated with the formation of pyro-cumulonimbus clouds
capable of transporting carbonaceous aerosols to the lower stratosphere.
Although these aerosols intrusions had been detected by SAGE and POAM
observations, their connection to biomass burning was unequivocally
established by UVAI observations \[Fromm et al, 2005\].

The UVAI is very nearly proportional to AAOD over both dark scenes as
well as over clouds and snow/ice. However, since the proportionality
constant varies with aerosol type, height, and the scene brightness, it
is not yet possible to convert the information content of the UVAI into
AAOD, except over dark cloud-free scenes.

The VISAI was originally designed to differentiate between absorbing
aerosol types. Its applicability, however, has been hindered by the
difficulty in removing the component of the spectral dependence
associated with the UV-to-visible surface reflectance dependence. Thus,
the VISAI remains a research parameter of limited usefulness to most
users.

The optical depth values at 388 nm are inverted from radiance
observations while the 354 and 500 nm results are obtained by conversion
of the 388 nm retrievals. The conversions to 354 and 500 nm are carried
out to facilitate comparisons with measurements from other space-borne
and ground based sensors, as well as with model calculations, which
often report values at 500 nm. However, this transformation increases
the dependence of the algorithm on the assumed model of aerosols, so the
reported values at the other wavelengths, particularly those at 500 nm,
should be considered less reliable. The file also contains the
geo-location information associated with each OMI pixel in the global
observation mode, as well as ancillary information used in the inversion
procedure. In the global mode each file contains a single orbit of data
covering the sunlit portion of the Earth from pole-to-pole with a swath
width of approximately 2600 km.

Because of the large sensitivity of the OMI near UV observations to
particle absorption, the AAOD is the most reliable quantitative OMAERUV
aerosol parameter. Since, by definition, AAOD is insensitive to clouds,
the AAOD retrieval can be extended to pixels with small amounts for
cloud contamination and is not restricted to completely cloud-free
scenes as it is the case for the AOD retrieval.

For environments where cloud free conditions prevail the AOD can be
reliably retrieved. Cloud interference with satellite retrieval is
minimum over arid and semi-arid regions where dust aerosols are commonly
present. Clear skies are also frequent in areas of seasonal biomass
burning and forest fires in the vicinity of the sources. As the plumes
of dust and smoke aerosols drift away from their source regions, they
mixed with clouds and the OMAERUV AOD retrieval becomes very
challenging.

Please refer to the OMAERUV [release specific
information](http://disc.sci.gsfc.nasa.gov/Aura/OMI/documents/v003/OMAERUVReleaseSpecificInformation.doc)
file for details on software versions and known problems.

**Summary of Algorithm Upgrades in this release**

A brief listing of the algorithm upgrades implemented in the current
release is presented here. When needed, a more detailed description of
the upgrades is presented in the Algorithm Description section below.

The use of a new calibrated OMI level1b (L1b) radiance data set is most
important change in this release. The collection 003 L1b data includes a
radiometric correction as well as an adjustment for stray light effects.
The first correction is largely spectrally independent whereas the
latter is wavelength dependent. The OMI L1b products are available
through the NASA S4PA system
(<http://disc.gsfc.nasa.gov/Aura/OMI/index.shtml>).

Threshold values of reflectivity and UV aerosol index used for aerosol
type selection and assignment of algorithm flags were modified as shown
in Table 1.

Sun glint angle threshold over the oceans for Aerosol Index calculation
has been lowered from 40º to 20º.

## Algorithm Description

Though OMI is a hyper-spectral instrument and measures over a wide
wavelength range from UV to blue (270-500nm), the OMAERUV aerosol
algorithm currently uses the measurements made at just two wavelengths:
354 and 388 nm. This is partly to maintain heritage with similar
algorithm used for TOMS \[Torres et al, 1998\], and partly because of
lack of reliable surface reflectance data at the longer OMI wavelengths.
Wavelengths below 340 nm cannot be used due to strong ozone absorption.

There are two advantages of the near-UV technique for deriving aerosols.
In the UV, the reflectance of all terrestrial surfaces is very low,
therefore, the retrieval of aerosol properties is possible over both
water and land surfaces, including the arid and semi-arid regions of the
world that appear very bright in the visible and near-IR. The second
advantage is that interaction between aerosol absorption and molecular
scattering from below the aerosol layer allows one to estimate AAOD.

A climatological data-set of surface albedo derived from TOMS
observations at 331, 340, 360 and 380 nm \[Torres et al., 2008, in
preparation\] is used to characterize surface reflective properties.
Surfaces are assumed to be Lambertian. This data set replaces the
climatology of Herman and Celarier \[1997\] that did not account for the
spectral dependence of UV surface albedo. Surface albedo values at 354
and 388 nm are obtained by interpolation of the TOMS-based climatology.

In the OMAERUV algorithm the first step is the calculation of the
observed Lambert Equivalent Reflectivity
($R_{\text{388}}^{\text{obs}}$), from the expression

$R_{\text{388}}^{\text{obs}} = \frac{I_{\text{388}}^{\text{obs}} - I_{\text{388}}^{\text{Ray}}}{T_{\text{388}}^{\text{Ray}} + S_{b_{\text{388}}^{\text{Ray}}}(I_{\text{388}}^{\text{obs}} - I_{\text{388}}^{\text{Ray}})}$
(1)

where $I_{\text{388}}^{\text{obs}}$is the OMI observed radiance a 388
nm, and $I_{\text{388}}^{\text{Ray}}$, $T_{\text{388}}^{\text{Ray}}$,
and $S_{b_{\text{388}}^{\text{Ray}}}$are respectively the calculated
atmospheric radiance, transmittance, and spherical albedo for a Rayleigh
atmosphere.

An adjusted observed reflectivity $R_{\text{388}}^{}$ is calculated as

$R_{\text{388}}^{} = R_{\text{388}}^{\text{obs}} - (R_{\text{388}}^{\text{sfc}} - R_{\text{354}}^{\text{sfc}})$
(2)

where the subtractive term
$(R_{\text{388}}^{\text{sfc}} - R_{\text{354}}^{\text{sfc}})$ represents
a correction for the spectral dependence of the surface albedo.

A key step of the OMAERUV algorithm is to calculate the UVAI as

$\text{UVAI} = - \text{100}\text{log}_{\text{10}}\lbrack\frac{I_{\text{354}}^{\text{obs}}}{I_{\text{354}}^{\text{calc}}(R_{\text{388}}^{})}\rbrack$
(3)

In the calculation of $I_{\text{354}}^{\text{calc}}$the reflectivity of
the lower boundary is assumed to be $R_{\text{388}}^{}$ as obtained from
Equation 2. Since UVAI is sensitive only to the UV-absorbing aerosols,
it is used to differentiate between carbonaceous and desert dust
aerosols, which absorb in the UV, from weakly or non-absorbing aerosols
types, such as sulfates, nitrates, most organics, and sea-salt. To
differentiate between carbonaceous and desert dust aerosols, the
algorithm takes advantage of the scene darkening effect of desert dust
aerosols in the near UV as explained below.

The retrieval algorithm assumes that the column atmospheric aerosol load
can be represented by one of three types of aerosols: desert dust (DST),
carbonaceous aerosols associated with biomass burning (BIO), and weakly
absorbing sulfate-based aerosols (SLF). Each aerosol type is represented
by seven aerosol models of varying single scattering albedo, for a total
of twenty-one models. The micro-physical properties of the twenty-one
aerosol models used by OMAERUV are based on long-term statistics of
ground-based observations by the Aerosol Robotic Network (AERONET). For
a full description of the aerosol models see Appendix 1. Given an
aerosol type we calculate 354 and 388 nm reflectances in the OMI
measurement geometry for a range of values of optical thickness and
single scattering albedo using Mie scattering radiative transfer
calculations.

The selection of an aerosol type makes use of a combination of spectral
and geographic considerations. Dust aerosols usually increase only
slightly or reduce the background reflectivity of the scene at 388 nm,
while carbonaceous aerosols produce larger reflectivity increases. The
net aerosol-related 388 nm reflectivity change, ΔR, is calculated as

$\Delta R = R_{\text{388}}^{\text{obs}} - R_{\text{388}}^{\text{sfc}}$
(4)

UVAI and ΔR are used to select aerosol type in conjunction with the
International Geosphere/Biosphere Programme scene type data as shown on
Figure 1.

Since the retrieval of desert dust and carbonaceous aerosols is
sensitive to the aerosol layer height, results are reported for five
different assumptions on the location of the aerosol center of mass: at
the surface, plus 1.5, 3.0, 6.0 and 10.0 km above the surface. Assuming
a particular value of aerosol layer height, as explained in the Product
Description section below, best-guess estimate values of AOD and AAOD
are made.

**Figure 1**. Procedure for aerosol type selection

Retrieved values of AOD and AAOD are also reported at 354 and 500 nm.
The wavelength conversion from 388 nm to 354 and 500 nm is done using
the spectral dependence associated with the assumed aerosol particle
size distribution and retrieved absorption information.

A simplified algorithm flag scheme has been implemented. Flag categories
and their description are summarized in Table 1.

More detailed description of the algorithm is available in the Algorithm
Theoretical Basis Document (ATBD) on
<http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/OMI/ATBD-OMI-03.pdf>

+-----+-----------------+-------------------------+-------------------+
| F   | Meaning         | Criteria                | Recommendation    |
| lag |                 |                         |                   |
| va  |                 |                         |                   |
| lue |                 |                         |                   |
+-----+-----------------+-------------------------+-------------------+
| 0   | Minimum         | ΔR ≤0.04 (DST and SLF)  | Both AOD and AAOD |
|     |                 |                         | can be use with   |
|     | cloud           | UVAI ≥ 1.0 and ΔR \<    | confidence        |
|     | contamination   | 0.08 (BIO)              |                   |
+-----+-----------------+-------------------------+-------------------+
| 1   | Minimum cloud   | UVAI ≥ 1.2, and R~obs~  | AAOD is           |
|     | interference in | \< 0.30                 | considered        |
|     | AAOD            |                         | reliable.         |
|     |                 |                         |                   |
|     |                 |                         | AOD probably      |
|     |                 |                         | cloud             |
|     |                 |                         | contaminated.     |
+-----+-----------------+-------------------------+-------------------+
| 2   | Both AOD and    | UVAI \<1.2 , and 0.04   | Cloud             |
|     | AAOD are less   | \< ΔR \<0.08            | contamination     |
|     | reliable        |                         | likely, use with  |
|     |                 |                         | caution           |
+-----+-----------------+-------------------------+-------------------+
| 3   | Retrieved       | Extrapolation in LUT's  | Do not use        |
|     | values out of   |                         |                   |
|     | bounds          |                         |                   |
+-----+-----------------+-------------------------+-------------------+
| 4   | Cloud, snow or  | Conditions not met for  | Do not use        |
|     | ice             | flag categories 0, 1, 2 |                   |
|     | contamination   |                         |                   |
+-----+-----------------+-------------------------+-------------------+
| 5   | Solar zenith    | SZA \> 70º              | Do not use        |
|     | angle above     |                         |                   |
|     |                 |                         |                   |
|     | threshold.      |                         |                   |
+-----+-----------------+-------------------------+-------------------+
| 6   | Sun-glint       | Sun-glint angle \< 40º  | Do not use        |
|     | contamination   |                         |                   |
|     | (water          |                         |                   |
|     | surfaces)       |                         |                   |
+-----+-----------------+-------------------------+-------------------+
| 7   | Terrain         | P~0~ \< 628.7 hPa       | Do not use        |
|     | pressure below  |                         |                   |
|     | threshold       |                         |                   |
+-----+-----------------+-------------------------+-------------------+

Table 1. Algorithm flag scheme

## Data Quality Assessment

Because of the relatively large footprint of the OMI observations (13x24
km^2^ at nadir), the major factor affecting the quality of aerosol
products is sub-pixel cloud contamination. Currently the cloud mask is
based on simple reflectivity and UVAI thresholds (as shown in Table 1),
which can cause significant overestimation of the mean AOD. However,
experience with TOMS suggests that monthly mean AODs do reliably capture
variation in the AOD with time. It is important to note, however, that
the AAOD is less affected by cloud contamination and hence is more
reliable.

In general OMAERUV retrievals are more reliable over land than over
water surfaces. The near-UV retrieval method is particularly sensitive
to carbonaceous and mineral aerosols. The sources of these aerosol types
are located over the continents, and the atmospheric aerosol load
associated with these events is generally large. In addition, dust and
smoke aerosol events tend to take place under meteorological conditions
which do not favor the formation of clouds in the vicinity of the
sources, such as arid and semi-arid areas in the case of dust, and the
dry season in the case of biomass burning. The OMAERUV retrieved AOD of
sulfate-based aerosols is less accurate due to its low values, higher
spatial variability and increased levels of sub-pixel cloud
contamination.

Ocean OMAERUV retrievals are affected by other factors. In addition to
sub-pixel clouds, the ocean surface reflectance has both angular and
spectral variations, the latter due to spectrally varying scattering
from the water, often called water-leaving radiances. (WLR),
chlorophyll, sediments and other types of suspended matter decrease WLR.
Spectral variations of ocean reflectance are accounted for in a
climatological sense using a wavelength dependent surface reflectance
data set. Short-term variability, however, is not taken into account in
the current version of the algorithm. Ocean retrievals of AOD and AAOD
are not reported for sun glint angles smaller than 40º. The UV aerosol
index is not reported for sun glint angles less than 20º.

![](media/image1.jpeg){width="6.0in" height="3.8673611111111112in"}The
AOD over land is expected to have the same root --mean square (rms)
error as TOMS retrievals (0.1 or 30% whichever is larger). The rms error
in AOD over water is likely to be 2 times larger. The rms error for AAOD
is estimated to be \~0.01.

**Figure 2.** OMI-AERONET comparison over Northern Africa. At the
Capo-verde and Banizombou sites OMI AOD retrievals have been reduce to
440 nm to facilitate the comparison.

Preliminary comparisons of OMAERUV-retrieved optical depth to AERONET
observations at several sites show that when cloud-free conditions
prevail OMAERUV-observed AOD values are in reasonable agreement with
AERONET. Figure 2 illustrates a comparison of OMIAERUV retrieved AOD to
AERONET measurements at four Northern Africa sites, where the
atmospheric aerosol load typically consists of desert dust. The level of
agreement is very good with correlation coefficients between 0.75 and
0.89.

For environments where sub-pixel cloud contamination is persistent
during all seasons the statistics of the OMAERUV-AERONET comparisons are
poor. For these conditions comparisons over a longer period are needed
to better assess the quality of the OMI aerosol product.

As part of the data quality assessment OMAERUV retrievals of AOD have
been compared to MODIS data for different aerosol types. In general,
when clear conditions predominate, the OMAERUV and MODIS AOD products
are well correlated, especially for large-scale dust and smoke events.
For background aerosol conditions, sub-pixel cloud contamination
significantly affects the OMI retrieval.

A comparison of OMAERUV AOD retrievals to airborne AOD observations by
the AMES Airborne Tracking Sun-photometer (AATS-14) during the
MILAGRO/INTEX-B field campaign was carried out. Most collocated
measurements corresponded to oceanic background conditions. In general,
the collection 2 OMAERUV retrieved AOD was significantly larger larger
than the AATS-14 observation. The comparison using OMAERUV collection 3
data shows significant improvement. Users are strongly advised to
consult with the OMI Quality Assurance Team for most recent information
on our ongoing assessment of OMAERUV data quality.

## Product Description

A 2600-km wide OMI swath contains 60 pixels. Due to optical aberrations
and a small asymmetry between the instrument optic axis with the S/C
nadir, the pixels on the swath are not symmetrically aligned on the line
perpendicular to the orbital plane. However, the latitude and longitude
provided with each pixel represent the location of each pixel on the
ground to a fraction of a pixel.

The OMAERUV product is written as an HDF-EOS5 swath file. For tools to
read HDF-EOS5 data files, please visit the link:
<http://disc.gsfc.nasa.gov/Aura/tools.shtml>.

A single OMAERUV file contains information retrieved from each OMI pixel
over the sun-lit portion of one Aura orbit. The data are ordered in
time. The information provided in these files includes latitude,
longitude, viewing geometry, aerosol extinction and absorption optical
depth (at 352, 388 and 500 nm), and ancillary parameters used in the
retrieval scheme. A very important parameter also reported is the
algorithm quality flag (field name AlgorithmFlags), which contains the
processing error flag in its first byte. Most users should use data with
a data quality flag 0 and 1. For a complete list of the parameters,
please read the [OMAERUV file
specification](http://disc.sci.gsfc.nasa.gov/Aura/OMI/OMAERUV_fs.pdf).
Gridded products will also be available. Please check the [Goddard Earth
Sciences (GES) Distributed Active Archive Center (DAAC)
website](http://disc.gsfc.nasa.gov/) for current information on these
products.

Best estimate values of AOD (variable name FinalAerosolOpticalDepth) and
AAOD (variable name FinalAerosolAbsOpticalDepth) associated with a
reported particular choice of aerosol vertical distribution are also
made available. The choice of vertical distribution varies with aerosol
type and location. The concentration of sulfate-based aerosols is
largest at the surface and decreases exponentially with height.
Carbonaceous aerosol layers between within 30 degrees of the Equator are
assumed to have maximum concentration at 3 km above ground level,
whereas mid and high-latitude smoke layers are assumed to peak at 6 km.
The location of desert dust aerosol layers varies between 1.5 and 10 km,
and is given by a multi-year climatological average of Chemical Model
Transport (CTM) calculations using the GOCART model.

Full OMAERUV data, as well as subsets of these data over many ground
stations and along Aura validation aircraft flights paths are also
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. [B.R.
Bojkov](mailto:Bojan.Bojkov@gsfc.nasa.gov") is the point of contact at
the AVDC.

Questions related to the OMAERUV dataset should be directed to the [GES
DAAC](mailto:help@daac.gsfc.nasa.gov). For questions and comments
related to the OMAERUV algorithm and data quality, please contact Omar
Torres (torres@qhearts.gsfc.nasa.gov) who has the overall responsibility
for this product.

References

Fromm, M., R. Bevilacqua, R. Servranckx, J. Rosen, J.P. Thayer, J.
Herman, and D. Larko, Pyro-cumulonimbus injection of smoke to the
stratosphere: Observations and impact of a super blowup in northwestern
Canada on 3-4 August 1998, J. Geophys. Res.,110,
doi:10.1029/2004JD005350, 2005

IGBP,
[**http://www-surf.larc.nasa.gov/surf/pages/sce_type.html**](http://www-surf.larc.nasa.gov/surf/pages/sce_type.html)

Herman, J.R., and E. A. Celarier, Earth surface reflectivity climatology
at 340 and 380 nm from TOMS data, J. Geophys. Res., 102, 28,003-28,011,
1997

Torres O., P.K. Bhartia, J.R. Herman and Z. Ahmad, Derivation of aerosol
properties from satellite measurements of backscattered ultraviolet
radiation. Theoretical Basis, J. Geophys. Res., 103, 17099‑17110, 1998

Torres, O., P.K. Bhartia, A. Syniuk, and E. Welton, TOMS Measurements of
Aerosol Absorption from Space: Comparison to SAFARI 2000 Ground based
Observations, J. Geophys. Res., 110, D10S18, doi:10.10129/2004JD004611,
2005

# [APPENDIX 1]{.underline}

# 

# [OMI SLF AEROSOL MODELS]{.underline}

# Models 1:7

# Real refractive index 1.40 (wavelength independent)

  -------------- -------------- -------------- -------------- --------------
  R~F~           R~C~           σ~F~           σ~C~           Fraction

  0.088          0.509          1.499          2.160          4.04 10^-4^
  -------------- -------------- -------------- -------------- --------------

Nodal points on imaginary refractive index:

0.000, 0.002, 0.004, 0.006, 0.008, 0.010, 0.012 (wavelength independent)

## [OMI BIO AEROSOL MODELS]{.underline}

#### Real refractive index 1.50 (wavelength independent)

# Models 1:3

  -------------- -------------- -------------- -------------- --------------
  R~F~           R~C~           σ~F~           σ~C~           Fraction

  0.087          0.567          1.537          2.203          2.06 10^-4^
  -------------- -------------- -------------- -------------- --------------

Nodal points on imaginary refractive index: 0.000, 0.005, 0.010
(wavelength independent)

### Models 4:7

  -------------- -------------- -------------- -------------- --------------
  R~F~           R~C~           σ~F~           σ~C~           Fraction

  0.08           0.705          1.492          2.075          2.05 10^-4^
  -------------- -------------- -------------- -------------- --------------

Nodal points on imaginary refractive index: 0.020, 0.030, 0.040,0.048
(wavelength independent)

## [OMI DST AEROSOL MODELS]{.underline}

Real refractive index 1.55 (wavelength independent)

# Models 1:7

  -------------- -------------- -------------- -------------- --------------
  R~F~           R~C~           σ~F~           σ~C~           Fraction

  0.052          0.67           1.697          1.806          4.35 10^-3^
  -------------- -------------- -------------- -------------- --------------

Nodal points on imaginary refractive index (wavelength dependent):

354.0 nm: 0.00000, 0.00128, 0.00256, 0.00561, 0.00832, 0.01279, 0.02303

388.0 nm: 0.00000, 0.00092, 0.00185, 0.00405, 0.00600, 0.00923, 0.01662