Modelled and measured effects of clouds on UV Aerosol Indices on a local, regional, and global scale Max Planck Institute for Chemistry, Mainz, Germany
16 Dec 2011
Received: 09 Jul 2010 – Published in Atmos. Chem. Phys. Discuss.: 18 Oct 2010 Abstract. The UV Aerosol Indices (UVAI) form one of very few available tools in
satellite remote sensing that provide information on aerosol absorption. The
UVAI are also quite insensitive to surface type and are determined in the
presence of clouds – situations where most aerosol retrieval algorithms do
not work. The UVAI are most sensitive to elevated layers of absorbing
aerosols, such as mineral dust and smoke, but they can also be used to study
non-absorbing aerosols, such as sulphate and secondary organic aerosols.
Although UVAI are determined for cloud-contaminated pixels, clouds do affect
the value of UVAI in several ways: (1) they shield the underlying scene
(potentially containing aerosols) from view, (2) they enhance the apparent
surface albedo of an elevated aerosol layer, and (3) clouds unpolluted by
aerosols also yield non-zero UVAI, here referred to as "cloudUVAI".
Revised: 30 Sep 2011 – Accepted: 02 Dec 2011 – Published: 16 Dec 2011
The main purpose of this paper is to demonstrate that clouds can cause
significant UVAI and that this cloudUVAI can be well modelled using simple
assumptions on cloud properties. To this aim, we modelled cloudUVAI by using
measured cloud optical parameters – either with low spatial resolution from
SCIAMACHY, or high resolution from MERIS – as input. The modelled cloudUVAI
were compared with UVAI determined from SCIAMACHY reflectances on different
spatial (local, regional and global) and temporal scales (single
measurement, daily means and seasonal means). The general dependencies of
UVAI on cloud parameters were quite well reproduced, but several issues
remain unclear: compared to the modelled cloudUVAI, measured UVAI show a
bias, in particular for large cloud fractions. Also, the spread in measured
UVAI is larger than in modelled cloudUVAI.
In addition to the original, Lambert Equivalent Reflector (LER)-based UVAI
algorithm, we have also investigated the effects of clouds on UVAI
determined using the so-called Modified LER (MLER) algorithm (currently
applied to TOMS and OMI data). For medium-sized clouds the MLER algorithm
performs better (UVAI are closer to 0), but like for LER UVAI, MLER UVAI can
become as large as −1.2 for small clouds and deviate significantly from zero
for cloud fractions near 1. The effects of clouds should therefore also be
taken into account when MLER UVAI data are used.
Because the effects of clouds and aerosols on UVAI are not independent, a
simple subtraction of modelled cloudUVAI from measured UVAI does not yield a
UVAI representative of a cloud-free scene when aerosols are present. We here
propose a first, simple approach for the correction of cloud effects on
UVAI. The method is shown to work reasonably well for small to medium-sized
clouds located above aerosols.
Citation: Penning de Vries, M. and Wagner, T.: Modelled and measured effects of clouds on UV Aerosol Indices on a local, regional, and global scale, Atmos. Chem. Phys., 11, 12715-12735, doi:10.5194/acp-11-12715-2011, 2011.