NDLI: The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

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The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

Content Provider	MDPI
Author	Barbieux, Kévin Hautecoeur, Olivier Bartolomei, Maurizio De Carranza, Manuel Borde, Régis
Copyright Year	2021
Description	Atmospheric Motion Vectors (AMVs) are an important input to many Numerical Weather Prediction (NWP) models. EUMETSAT derives AMVs from several of its orbiting satellites, including the geostationary satellites (Meteosat), and its Low-Earth Orbit (LEO) satellites. The algorithm extracting the AMVs uses pairs or triplets of images, and tracks the motion of clouds or water vapour features from one image to another. Currently, EUMETSAT LEO satellite AMVs are retrieved from georeferenced images from the Advanced Very-High-Resolution Radiometer (AVHRR) on board the Metop satellites. EUMETSAT is currently preparing the operational release of an AMV product from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. The main innovation in the processing, compared with AVHRR AMVs, lies in the co-registration of pairs of images: the images are first projected on an equal-area grid, before applying the AMV extraction algorithm. This approach has multiple advantages. First, individual pixels represent areas of equal sizes, which is crucial to ensure that the tracking is consistent throughout the processed image, and from one image to another. Second, this allows features that would otherwise leave the frame of the reference image to be tracked, thereby allowing more AMVs to be derived. Third, the same framework could be used for every LEO satellite, allowing an overall consistency of EUMETSAT AMV products. In this work, we present the results of this method for SLSTR by comparing the AMVs to the forecast model. We validate our results against AMVs currently derived from AVHRR and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The release of the operational SLSTR AMV product is expected in 2022.
Starting Page	1702
e-ISSN	20724292
DOI	10.3390/rs13091702
Journal	Remote Sensing
Issue Number	9
Volume Number	13
Language	English
Publisher	MDPI
Publisher Date	2021-04-28
Access Restriction	Open
Subject Keyword	Remote Sensing Atmospheric Motion Vector Amv Sentinel-3 Slstr Cross-correlation Cloud Satellite
Content Type	Text
Resource Type	Article

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