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TROPOMI on board of Sentinel-5 Precursor (S5P) provides continuous daily distribution of several cloud properties, which are required as input for trace-gas retrievals. The operational TROPOMI cloud retrieval is a two-step algorithm. At first, the OCRA (Optical Cloud Recognition Algorithm) computes a radiometric cloud fraction using a broad-band UV/VIS color space approach and later the ROCINN (Retrieval of Cloud Information using Neural Networks) retrieves the cloud height, cloud optical thickness and cloud albedo from NIR measurements in and around the oxygen A-band (~760nm). Within the ROCINN algorithm two different models are possible; the Clouds-as-Reflecting-Boundaries (CRB), where the cloud is a simple Lambertian reflector and the Clouds-as-Layers (CAL), where the cloud is a homogeneous layer of scattering liquid-water spherical particles. There is evidence that some TROPOMI cloud retrievals are contaminated by aerosols. This is particularly true in the following cases: (a) when there is co-existence of clouds and aerosols in the same TROPOMI footprint and (b) when there is a pure aerosol layer, appearing in the TROPOMI cloud product. The latter is usually the case of OCRA deriving an elevated radiometric cloud fraction corresponding to the given aerosol conditions. Then, ROCINN is triggered and returns two additional cloud parameters. Often, the false alarms of elevated OCRA cloud fraction can be identified when ROCINN retrieves a cloud height at the surface level. However, there are cases in which ROCINN cloud outputs do not refer to the surface properties of the scene, but to aerosol layers present in the same TROPOMI footprint. Especially for dust aerosols, which are usually large particles and comparable to the cloud droplet size, we expect more frequently those mixed retrievals. In particular, dust layers with large concentrations (i.e., high aerosol optical depth (AOD)) are better candidates for erroneously retrieved clouds in the TROPOMI L2 product. The TROPOMI aerosol algorithm (TropOMAER) makes use of the L1b reflectances in the UV to derive aerosol information in cloud-free and above-cloud aerosol scenes. With the use of ground-based active and passive remote sensing instruments, we are able to characterize well the vertically resolved cloud and aerosol layers in the lower troposphere. In this work, synergistic ground-based measurements from a PollyXT multiwavelength-Raman-polarization lidar and an AERONET sun-photometer are used to discriminate dust aerosols from clouds in TROPOMI measurements. We have selected ground-based observation sites over which the atmospheric column frequently contains large contributions of desert dust particles.
Argyrouli et al. (Fri,) studied this question.