Developments of NRT Level-2 Cloud products in Copernicus Sentinel-3

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Authors

Edouard Martins · European Organisation for the Exploitation of Meteorological Satellites

Julien Chimot · European Organisation for the Exploitation of Meteorological Satellites

Loredana Spezzi · European Organisation for the Exploitation of Meteorological Satellites

Alessio Bozzo · European Organisation for the Exploitation of Meteorological Satellites

J. Riédi · Laboratoire d'Optique Atmosphérique

Kévin Barbieux · European Organisation for the Exploitation of Meteorological Satellites

Abstract

Entrusted by both the European Commission (EC), and Member states, EUMETSAT is on the verge of implementing the comprehensive portfolio of operational Near Real Time (NRT) L2 cloud products for the Copernicus Sentinel-3 mission.To that end, several preparatory studies were conducted in the last years to assess the scientific feasibility and potential expected quality. These include: i.e., Cloud Top Pressure (CTP) from the OLCI O2 spectral bands, Synergy Cloud Mask and obstruction (from both OLCI & SLSTR). Additionally, besides the already-existing cloud-related algorithms (e.g., the Basic Cloud Mask and Bayesian Cloud Mask in L1 SLSTR, the IdePix algorithm among which for OLCI data, etc.), EUMETSAT has also developed two operational NRT L2-related processors for cloud masking and cloud tracking applications:The SLSTR Naïve Probabilistic Cloud and Aerosol detection algorithm (initiated in 2022), tailored for enhanced cloud and aerosol discrimination, and preserving pixels contaminated by dust / ashes / smoke. It is now running within the S3 NRT AOD processor.The SLSTR Atmospheric Motion Vectors (AMVs), which are mesoscale estimations of the wind (speed, direction and height), obtained by tracking cloud features across sequences of images.EUMETSAT is now preparing the phase 2 of Sentinel-3 NRT L2 cloud products and application developments. These will be summarised in this presentation, notably:A cloud mask validation framework of the Naïve Probabilistic Cloud and Aerosol detection algorithm, relying on the comparison of S3 LEO products vs. GEO products (e.g., from MTG/FCI) and machine learning techniques to improve tests, thresholds and decision trees, in partnership with the Laboratoire d’Optique AppliquThe extension of the Naïve Probabilistic Cloud and Aerosol detection algorithm to future NRT L2 aerosol processors, such as the S3 NRT Aerosol Layer Height (ALH). Additionally, thanks to the current internal development of a SLSTR/OLCI L1 synergy product, .And the development of operational NRT cloud products suite that could support not only operational meteorological agencies but also the Sentinel-3 AMV processor, in a similar way as done for the operational AMV products on GEO sensors (MSG/SEVIRI and MTG-I/FCI).

Topics & Keywords

Atmospheric aerosols and clouds Atmospheric Ozone and Climate Optical Systems and Laser Technology

UN Sustainable Development Goals

Peace, Justice and strong institutions

Publication Details

DOI: 10.5194/egusphere-egu26-20661

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