Abstract. Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low
cloud coverage. However, even fully clear pixels can be affected by
clouds in the vicinity, either by shadowing or by scattering of
radiation from clouds in the clear region. Quantifying the error of
retrieved trace gas concentrations due to cloud scattering is a
difficult task. One possibility is to generate synthetic data by
three-dimensional (3D) radiative transfer simulations using
realistic 3D atmospheric input data, including 3D cloud
structures. Retrieval algorithms may be applied on the synthetic
data, and comparison to the known input trace gas concentrations
yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has
been generated using the Monte Carlo radiative transfer model
MYSTIC (Monte Carlo code for the phYSically
correct Tracing of photons In Cloudy atmospheres). The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover
it includes layer air mass factors (layer-AMFs) calculated at
460 nm. All simulations are performed for a fixed background
atmosphere for various sun positions, viewing directions and surface
albedos. Two cloud setups are considered: the first includes simple
box clouds with various geometrical and optical thicknesses. This
can be used to systematically investigate the sensitivity of the
retrieval error on solar zenith angle, surface albedo and cloud
parameters. Corresponding 1D simulations are also provided. The
second includes realistic three-dimensional clouds from an ICON
large eddy simulation (LES) for a region covering Germany and parts
of surrounding countries. The scene includes cloud types typical of
central Europe such as shallow cumulus, convective cloud cells,
cirrus and stratocumulus. This large dataset can be used to
quantify the trace gas concentration retrieval error statistically. Along with the dataset, the impact of horizontal photon transport on
reflectance spectra and layer-AMFs is analysed for the box-cloud
scenarios. Moreover, the impact of 3D cloud scattering on the NO2
vertical column density (VCD) retrieval is presented for a specific
LES case. We find that the retrieval error is largest in cloud
shadow regions, where the NO2 VCD is underestimated by more than
20 %. The dataset is available for the scientific community to assess the
behaviour of trace gas retrieval algorithms and cloud correction
schemes in cloud conditions with 3D structure.