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  • title: IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision
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            IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision
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            IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision

            Dec 15, 2023

            Speakers

            KJ

            Kai Jeggle

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            MC

            Mikolaj Czerkawski

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            FS

            Federico Serva

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            About

            Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this work, we create a new observational constraint of regime-dependent ice microphysical properties at the spatio-temporal coverage of geostationary satellite instruments and the quality of active satellite retrievals. We achieve this by training a convolutional neural network on three years of SEVIRI and DARDAR data sets.…

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            NeurIPS 2023

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