Abstract
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.
Highlights
In the field of wind observations, Atmospheric Motion Vectors (AMVs) prevail as a globally available, mesoscale measurement of winds
The Level 1B (L1B) S8 data is disseminated in the form of Brightness Temperatures (BT), which are used by the AMV algorithm for the height assignment
Our AMV data are first checked against the ECMWF forecast model
Summary
In the field of wind observations, Atmospheric Motion Vectors (AMVs) prevail as a globally available, mesoscale measurement of winds. AMVs are derived from pairs of images from satellite sensors, by tracking clouds, or water vapour features, from one image to the other. The time gap between successive overpasses is much higher, either 101 min (time needed to complete one orbit of Metop or Sentinel-3), or less when using images from different satellites. Does this increased time gap make the tracking of features from one image to another harder, but it limits the derivation of AMVs to the area of overlap between the images.
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