# OMTO3 README File

**\
Released January 30, 2008**

## Overview

This document provides a brief description of the OMTO3 data product.
OMTO3 comprises total ozone, aerosol index, and ancillary information
produced from the TOMS Version 8.5 (V8.5) algorithm applied to OMI
"global mode" measurements. In the global mode each file contains a
single orbit of data. In each orbit OMI measurements cover approximately
2600 km wide cross-track swath from pole to pole (sunlit portions only).
Currently, OMTO3 products are not produced when OMI goes into the "zoom
mode" for one day every 32 days.

## The accuracy and precision of the OMTO3 total ozone data is similar to the legacy Total Ozone Mapping Spectrometer (TOMS) data started in 1978, except over cloudy areas where OMTO3 data are more accurate than that of the TOMS. Since there are significant differences between the two products over cloudy areas, users should exercise caution in combining TOMS data with OMTO3 data for long- term trends. The TOMS project is planning to reprocess the entire TOMS record to make the dataset more consistent with OMTO3.  Algorithm Description

The basic algorithm uses 2 wavelengths (317.5 and 331.2 nm under most
conditions, and 331.2 and 360 nm for high ozone and high solar zenith
angle conditions). The longer of the two wavelengths is used to derive
effective cloud fraction (fc) based on the Mixed Lambert Equivalent
Reflectivity (MLER) model \[Ahmad et al 2004\] that was developed to
model the effect of clouds on Rayleigh scattering. When fc becomes less
than zero or when there is snow/ice, we assume that no cloud is present
and use the Lambert Equivalent Reflectivity (LER) model described by
Ahmad et al \[2004\] to derive the clear scene reflectivity R. When fc
exceeds 1 we assume100% cloud cover and derive cloud reflectivity using
the LER model. Given the fc/R the shorter (stronger ozone-absorbing)
wavelength is used to derive total ozone.

An important difference between the V8.5 algorithm and previous
versions, as well with the archived TOMS dataset, is the assignment of
effective cloud height. It has been assumed in the previous algorithms
that the absorption of backscattered solar radiation essentially stops
at the cloud-top level when the clouds are optically thick. To estimate
the total column amount, the "un-measured" column below the cloud-top
(computed using climatology) is added to the measured column. Recent
analysis of the OMI data in conjunction with CloudSat radar data
\[Vasilkov et al 2008\] indicates that this assumption is invalid. Mie
scattering calculations using CloudSat data indicate that in all cloudy
scenes, including deep convective clouds, the UV radiation received at
the satellite is sensitive to the ozone column below the nominal
cloud-top pressure reported by thermal infrared sensors such as MODIS.
Analysis shows that photons actually penetrate some distance into a
cloud. In V8.5, we use the Radiative Cloud Pressure (RCP) inferred from
Rotational-Raman Scattering
([OMCLDRR](http://daac.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml)) to
derive the total column ozone. Since the pressure corresponding to RCP
is usually significantly below the cloud-top pressure climatology
assumed in the V8 algorithm, the V8.5 derived column amounts have
decreased over clouds. The magnitude of the decrease depends on cloud
fraction, location, and solar zenith angle. Please refer to [release
specific information about
OMTO3](http://disc.sci.gsfc.nasa.gov/Aura/OMI/documents/v003/OMTO3_ReleaseDetails_v1.1.0.doc)
for details

The effective cloud fraction (fc) derived from the MLER model is used to
estimate the Radiative Cloud Fraction (RCF). RCF characterizes the
fraction of measured radiation that is scattered by clouds. Mie
scattering calculations indicate that the clear and cloudy ozone columns
weighted by RCF provides a value very close to what one would get if one
were to use the plane parallel Mie cloud model with the independent
pixel approximation to account for mixed scenes. The advantage of the
MLER model is that one doesn't need independent knowledge of geometrical
cloud fraction to calculate the ozone column accurately in cloudy scenes

The algorithm also calculates the absorbing Aerosol Index (AI) from the
radiance residuals at 360 nm. The AI is useful for tracking global
transport of smoke and dust, for it can track these aerosols above and
through clouds, as well as over snow/ice covered surfaces. Various
studies have indicated that AI is very nearly proportional to the
aerosol absorption optical depth at 360 nm. However, the proportionality
constant varies with the altitude (of the center of mass) of the aerosol
layer- the lower the altitude the smaller the constant. Most aerosols
have stronger absorption in the UV than in the visible, including
mineral dust from deserts and carbonaceous aerosols containing organic
and black carbon. Since the AI is also affected by the spectral
dependence of surface albedo caused by sea-glint and water--leaving
radiance, and since there are residual errors in the MLER model in
estimating Rayleigh scattering in presence of clouds, we recommend that
only the AI values larger than +1 should be used for aerosol studies and
areas contaminated by sea-glint should be avoided completely. Since
absorbing aerosols cause the ozone derived from the basic algorithm to
be overestimated, a parametric relationship based on AI is used to
correct the ozone column derived from the basic algorithm. This
relationship also appears to remove a large portion of errors caused by
sea-glint.

Other than the three primary wavelengths mentioned above, the OMTO3
algorithm uses additional wavelengths for quality control and error
correction in more restricted geophysical situations. These include
correction for ozone profile shape errors at large solar zenith angles
using 312.5 nm measurements, and the detection of strong sulfur-dioxide
contamination using multiple wavelength pairs. For a more detailed
description of the algorithm please refer to the Algorithm Theoretical
Basis Document (ATBD) on
<http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/viewInstrument.php?instrument=13>.
This website contains a description of the most recent updates to the
algorithm, along with other related documents related to this algorithm.

This algorithm is one of the two algorithms that derive total ozone
values from OMI. The other is an algorithm based on the Differential
Optical Absorption Spectroscopy (DOAS) approach that takes advantage of
OMI\'s hyperspectral measurements. The DOAS algorithm is maintained by
KNMI/The Netherlands. The DOAS ATBD is also on the above website. After
the recent reprocessing of both data sets (Collection 3), the two
datasets are in much better agreement than before, though some
differences still exist in high latitudes and over snow/ice.

## Data Quality Assessment

Overall the quality of total ozone and AI data produced by OMTO3 is
similar to that from TOMS, except for cloudy observations, as discussed
earlier. Based on experience with TOMS, the total ozone data provided in
OMTO3 should have a root-mean squared error of 1-2%, depending on solar
zenith angle, aerosol amount, and cloud cover. These errors are best
described as pseudo-random: systematic over small areas with a unique
geophysical regime, random over large areas containing a mixture of
geophysical regimes. Preliminary analyses show that OMTO3 data compare
about as well with Dobson and Brewer stations as did Nimbus-7/TOMS data.
(The overall quality of EP/TOMS data is poorer compared to both Nimbus-7
TOMS and OMI. The EP/TOMS total ozone data have been reprocessed
recently by applying an empirical correction, based on internal
consistency criteria for ozone and surface reflectivity and comparisons
with NOAA/SBUV-2 data, to remove the effect of several poorly understood
instrument anomalies. The AI data from EP/TOMS, however, have not yet
been corrected).

## Product Description

A 2600 km wide OMI swath contains 60 pixels. Due to optical aberrations
and small misalignment between the instrument optic axis with the
spacecraft nadir, the pixels on the ground 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 the
center of each pixel on the ground to a fraction of a pixel.

The OMTO3 product is written as an HDF-EOS5 swath file. For a list of
tools that read HDF-EOS5 data files, please visit these links:\
\
<http://disc.gsfc.nasa.gov/Aura/tools.shtml>,\
<http://hdfeos.net/>\
\
A single OMTO3 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, solar zenith angle, reflectivity and total column ozone,
aerosol index, radiative cloud fraction and radiative cloud pressure
from OMCLDRR algorithm, and a large number of ancillary parameters to
assess data quality. The most important of these parameters is the
"QualityFlags" field, which contains the processing error flag in its
first byte. Most users should use data with a data quality flag = 0
(good sample) or 1 (glint contamination corrected) only. For a complete
list of the parameters, please read the [OMTO3 file
specification](http://disc.sci.gsfc.nasa.gov/Aura/OMI/documents/v003/OMTO3_FileSpec_V003.doc).\
\
For users not interested in the detailed swath information provided on
OMTO3 level 2 dataset we are providing several gridded products.
Presently, we grid OMTO3 data in a format similar to that used for TOMS
(1˚ x 1˚ lat/long) and it is available through the [TOMS
website](http://toms.gsfc.nasa.gov/). However, to take advantage of the
higher spatial resolution of the OMI products we also produce higher
resolution gridded products for OMI datasets, including OMTO3. In
addition, we make OMTO3 level 2 data available in a geographically
ordered (rather than time-ordered) format
([OMTO3G](http://daac.gsfc.nasa.gov/Aura/OMI/omto3g_v003.shtml)) that
can be more easily subsetted and manipulated on-line prior to ordering.
Please check the [Goddard Earth Sciences (GES) Data and Information
Services Center (DISC) website](http://disc.gsfc.nasa.gov/Aura/OMI/) for
current information on these products.\
\
Full OMTO3 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 OMTO3 dataset should be directed to the [GES
DISC](mailto:help@disc.gsfc.nasa.gov). For questions and comments
related to the OMTO3 algorithm and data quality please contact [Kai
Yang](mailto:Kai.Yang-1@nasa.gov) . Please send a copy of your e-mail to
[P. K. Bhartia](mailto:pawan.bhartia@nasa.gov), who has the overall
responsibility for this product.

### Reference

> Ahmad, Z., P.K. Bhartia, N. Krotkov, Spectral properties of
> backscattered UV radiation in cloudy atmospheres, *J. Geophys. Res*.,
> 109, D01201, doi:10.1029/2003JD003395, 2004
>
> Vasilkov, A.P., Joiner, J., Spurr ,R.J.D, Bhartia, P.K., Levelt, P.,
> and Stephens, G., Evaluation of the OMI Cloud Pressures Derived from
> Rotational Raman Scattering by Comparisons with Other Satellite Data
> and Radiative Transfer Simulations, *Geophys. Res*., in press, 2008.