Recent advances and new features in the operational cloud products of Sentinel-5 Precursor and prospects for Sentinel-4

The measurement of atmospheric composition from space requires also a precise knowledge regarding the appearance of clouds within the observed scene, and if present, a quantification of cloud properties such as cloud fraction, cloud height and cloud optical thickness/cloud albedo. The Copernicus mission Sentinel-5 Precursor, being operational since 2 years, covers the UV/VIS/NIR/SWIR spectral region. By covering the spectral region of the Oxygen A-band in the NIR, it provides an excellent prerequisite to retrieve the cloud parameters mentioned above. The same holds true for the anticipated Sentinel-4 mission (foreseen launch in 2023). In this contribution we present the most recent advances in the algorithms for retrieving the operational cloud products from TROPOMI onboard Sentinel-5 Precursor and from the future Sentinel-4/UVN onboard MTG-S. The applied cloud retrieval algorithms OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) have their heritage with GOME/ERS-2 and GOME-2 on MetOp-A/B/C, where they have already been successfully implemented in an operational environment. OCRA uses a broad band color space approach in the UV/VIS in combination with a set of cloud-free reflectance background composite maps to determine a radiometric cloud fraction while the ROCINN algorithm retrieves the cloud top height, cloud optical thickness and cloud albedo from NIR measurements in and around the oxygen A-band, taking as a priori input the cloud fraction computed by OCRA. ROCINN includes two different cloud models. One which treats clouds more realistically as layers of scattering water droplets (clouds-as-layers, CAL) and another one which treats clouds as simple Lambertian reflectors (clouds-as-reflecting boundaries, CRB). Substantial improvements to the algorithms have been implemented recently, some of which will be presented here, e.g. improved background maps, inclusion of cloud phase, retrieval of surface properties using machine learning. Further validation efforts via satellite-to-satellite comparisons with VIIRS on Suomi-NPP have been carried out and consolidate the product quality.