Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas
algorithms make use of climatological surface reflectivity databases. For
example, cloud and

Satellite ultraviolet and visible (UV–vis) nadir backscattered
sunlight trace-gas, aerosol, and cloud retrieval algorithms must accurately
estimate the reflection by the Earth's surface in order to produce high-quality data sets. Surface reflectivity climatologies used in most current
algorithms are typically gridded monthly Lambertian equivalent reflectivities
(LERs) that have been derived from satellite observations

Here, we give some basic definitions that have been used in the literature to
provide context to our problem and for clarity because sometimes different
definitions have been used for similar or the same quantities. This
dependence is described by the bidirectional reflectance distribution
function (BRDF), mathematically expressed as

The frequently used dimensionless bidirectional reflectance factor (BRF) is
defined as “the ratio of the radiant flux reflected by a sample surface to
the radiant flux reflected into the identical beam geometry by an ideal
(lossless) and diffuse (Lambertian) standard surface, irradiated under the
same conditions as the sample surface”

BRF and BRDF are both inherent properties of the surface that do not depend
on the illumination conditions

Many satellite UV–vis algorithms are based on the so-called mixed Lambert
equivalent reflectivity (MLER) model, first introduced by

The MLER model typically assumes

The MLER model compensates for photon transport within a cloud by placing the
Lambertian surface somewhere in the middle of the cloud instead of at the top

In one of the early studies to explore the effects of surface BRDF on
satellite trace-gas retrievals,

To account for surface BRDF in the existing MLER cloud and trace-gas algorithms, we introduce the concept of a geometry-dependent surface LER. The geometry-dependent LER is derived from TOA radiance computed with Rayleigh scattering and BRDF for the exact geometry of a satellite-based pixel. This approach does not require any major changes to existing MLER trace-gas and cloud algorithms. The main revision to the algorithms requires replacement of the existing static LER climatologies with LERs calculated for specific field-of-view (FOV) sun–satellite geometries. The geometry-dependent surface LER approach can be applied to any current and future satellite algorithms that use the MLER concept.

The main goal of this paper is to document a new global surface reflectivity
product that will be publicly available and could be easily used within
several existing operational satellite trace-gas and cloud algorithms. We
implement the geometry-dependent LERs based on a MODIS BRDF product and use
these LERs within OMI cloud and

It should be noted that the MODIS BRDF product is derived from the
atmospherically corrected TOA reflectances (i.e., aerosol and Rayleigh
scattering effects are removed at the high spatial resolution of MODIS). In
contrast, the climatological LERs currently used in OMI algorithms, from
either the Total Ozone Mapping Spectrometer (TOMS) or OMI, are derived by
correcting only for Rayleigh scattering and thus include aerosol effects

For all radiative transfer calculations, we use the VLIDORT code

We use the MODIS gap-filled BRDF Collection 5 product MCD43GF

In this paper, we examine the BRDF effects on two OMI cloud algorithms, one
based on RRS in the UV and the other on

Both the RRS and

The OMI RRS cloud algorithm is detailed in

Our

The OCP is estimated using the MLER method to compute the appropriate AMFs

The OMI

In this section we describe our approach of generating the geometry-dependent
LER. First, we average all input data over a nominal OMI pixel. The input
data include MODIS-derived land BRDF kernel coefficients, land–water flags,
terrain heights from a digital elevation model, and chlorophyll values and
wind speed over water surfaces. Second, we compute the TOA radiance
accounting for surface BRDF. Third, we calculate the geometry-dependent LER
from the TOA radiance. Then we use this geometry-dependent LER in cloud and

The BRDF over land is calculated using the Ross-Thick Li-Sparse (RTLS) kernel
model

The BRDF coefficients depend on wavelength. For the present study we selected
two wavelengths in the UV and vis: 354 and 466

To calculate TOA radiance over water surfaces, we account for both light
specularly reflected from a rough water surface and diffuse light
backscattered by water bulk and transmitted through the water surface. We
neglect contributions from oceanic foam that can be significant for high wind
speeds. Reflection from the water surface is described by the Cox–Munk slope
distribution function

Diffuse light from the ocean is described by a Case 1 water model that has
chlorophyll concentration as a single input parameter

To estimate LER over mixed surface types, we compute an area-weighted
radiance for uniform land and water contributions within an OMI FOV. The LER
for heterogeneous surface pixels is then calculated from this linear
combination of radiances. The high spatial resolution MCD43GF product

Given the computed TOA radiance,

High spatial resolution MODIS-based LERs for the
Baltimore–Washington area of the United States for 17 (left) and 18 (right)
January 2005 computed with the original spatial resolution of
30

Averaging the BRDF coefficients over an OMI pixel may not be equivalent to
averaging the high-resolution surface LER over the OMI pixel. We carried out
a numerical experiment of calculations of TOA radiances using the
high-resolution BRDF coefficients and OMI geometries for the
Washington–Baltimore area of the United States (Fig.

It should be noted that aerosols are not included in the computation of the
geometry-dependent LER. Scattering by aerosols in the atmosphere reduces the
BRDF effects

Figure

Data flow diagram of generating the geometry-dependent LER.

Because reflection of incoming direct and diffuse solar light from
non-Lambertian surfaces depends on satellite observational geometry, the same
area observed at different geometries can have different LERs.
Figure

A comparison of the computed geometry-dependent and climatological LERs at
466

Figure

Over ocean, the geometry-dependent LERs are systematically higher than the climatological LERs in areas affected by sun glint and at large VZAs. This is because the climatological LERs are based on the mode of LERs from a long time series of observations over a given area; this minimizes the impact of observations affected by sun glint and high values that occur at large VZAs. The total ocean reflectance is comprised of three components: direct and diffuse solar light reflected from the ocean surface and water-leaving light. The fraction of each component strongly depends on geometry. Reflection of direct solar light dominates in the sun-glint area. At the edges of the swath the relative contribution of reflected diffuse light increases because the sky radiance increases to the horizons and the reflection angle increases thus the Fresnel reflection increases. The higher values of LER nearer to the eastern part of the swath than at the western part are mostly due to sky light reflected from the ocean surface. The angular distribution of the sky radiance is not symmetric in the plane of satellite observations because the sun is in the western part of the swath. The sky radiance is higher in the eastern part of the swath, and it is reflected at higher angles than the light from the western part. Additionally, the higher reflection angle results in higher Fresnel reflection in the eastern part of the swath. This is confirmed by our calculations of the view angle dependence of the reflected light only, i.e., no water-leaving radiance included.

Figure

Over the ocean, the LER differences additionally result from the spectral
dependence of water-leaving radiance. Over the sunglint areas, the solar
light reflected from the ocean surface is significantly brighter at
466

Similar to Fig.

Comparison of RRS-retrieved OCPs computed with geometry-dependent
and climatological LERs for OMI orbit 12414 of 14 November 2006; data are for

It is interesting to note that the patterns of rivers and their tributaries
are evident in the LER maps of Fig.

Figure

Figure

Figure

Spatial distributions of the effective cloud fraction and cloud pressure
retrieved from

Comparison of

Figure

The effect of replacing the climatological surface LERs with the
geometry-dependent LERs is much more pronounced for the

To make the numbers characterizing the ECF and OCP differences more
representative, we processed OMI data for 2 days of 14 November and 14 July
2006. Figure

We consider the BRDF effect on the

The tropospheric

An effect of replacing the climatological LERs with geometry-dependent LERs
on AMF

To estimate the BRDF effect on AMF

Scatter diagrams of AMFs calculated using geometric-dependent
MODIS-based LER vs. OMI-based climatological LER for the orbit 12414 for
clear to moderately cloudy sky (

AMFs calculated with geometry-dependent MODIS-based LERs and
climatological OMI-based LERs over

Figure

Figure

Figure

We developed a new concept of geometry-dependent surface LER and provided a means for computing it. Spatially averaged high-resolution MODIS BRDFs are used for computation of the geometry-dependent LER over land for OMI pixels. The Cox–Munk slope distribution function and the Case 1 water-leaving radiance model are utilized for computation of the geometry-dependent LER over ocean. This method accounts for the geometrical dependence of LER within the existing framework of MLER trace-gas and cloud algorithms with only minimal changes. It is important to note that the geometry-dependent surface LER approach can be applied to any current or future satellite algorithms that utilize MLER trace-gas and cloud algorithms.

We examined the effects of the geometry-dependent LER on OMI cloud and

We also find that replacing the climatological OMI-based LERs with
geometry-dependent LERs can increase the OMI

In the future, we plan to implement the use of geometry-dependent LERs in our
cloud and

The MODIS gap-filled BRDF Collection 5 product MCD43GF used in this paper is
available at

The authors declare that they have no conflict of interest.

Funding for this work was provided in part by the NASA through the Aura science team program. We thank Pawan K. Bhartia for helpful discussions, Ziauddin Ahmad for providing data for comparisons, Andrew Sayer for provision of an updated ocean optics model used in the water-leaving supplement of the VLIDORT code, and Crystel B. Schaaf for consultation on the use of the MODIS BRDF product. Edited by: F. Boersma Reviewed by: three anonymous referees