SEVIRI_ML: A machine learning based module to derive: (1) A cloud mask, (2) the cloud phase, (3) the cloud top pressure, (4) the cloud top temperature, (4) a multilayer flag and (5) a cloud base height from SEVIRI measurements using its full spectral capabilities. Also to be used as external module with ORAC (https://github.com/ORAC-CC/orac).
Extracts data from one or more landSAF LST HDF5s, resamples them, stacks them, and adds them to a new netCDF4.
Repository of python-based Jupyter Notebooks for learning about EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea surface temperature products for marine applications.
Continuous monitoring of fog life cycles based on geostationary satellite imagery
Repository of python-based Jupyter Notebooks for learning about EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea surface temperature products for marine applications.
Exploring the SEVIRI Level 1.5 dataset
Live colourised Meteosat imagery from your own EUMETCast receiver — nine layers, playback and export, in one Rust binary with no external services.
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