The Operational Cloud Products for Sentinel-5 Precursor and Sentinel-4 and comparisons with GEMS

The status and most recent developments of the operational L2 Cloud product will be presented in this contribution for the ongoing Sentinel-5 Precursor and upcoming Sentinel-4 missions. These Copernicus missions are focused on atmospheric composition, operate in the UV/VIS/NIR/(SWIR) spectral region and comprise the retrieval of trace gases, greenhouse gases, aerosol and cloud properties. A good knowledge about the latter, i.e. the presence and characteristics of clouds, is a pre-requisite for an accurate retrieval of the aforementioned trace gases and greenhouse gases. Additionally, clouds are by themselves an interesting indicator to measure and monitor because of their contribution to the radiation budget, and hence, impact on climatological applications. 
The algorithms for retrieving the operational cloud products from TROPOMI onboard Sentinel-5 Precursor and the UVN spectrometer onboard Sentinel-4 are called OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) and both have their heritage with GOME/ERS-2 and GOME-2 MetOp-A/B/C, where they have already been successfully implemented in an operational environment. OCRA applies a broad band color space approach to the measured reflectance in order to retrieve a radiometric cloud fraction that is used as an a priori input to ROCINN, which retrieves cloud top height, cloud optical thickness and cloud albedo from measurements of sun-normalized radiances in the NIR in and around the oxygen A-band. The cloud parameters retrieved by ROCINN are provided for two different cloud models. The Clouds-as-Layers (CAL) model treats clouds as layers of scattering water droplets, which is physically more realistic than the second model, Clouds-as-Reflecting Boundaries (CRB), which treats a cloud as a simple Lambertian reflector.
In addition, this contribution will cover initial results of applying the OCRA algorithm to the recently launched Korean geostationary GEMS instrument. Applying the algorithm to GEMS does provide a great opportunity to test the performance under a geostationary configuration and to transfer lessons learned in a synergetic way to the Sentinel-4 development. Also, initial comparisons of the S5P and GEMS cloud products will be shown.