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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.04135 |
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| _version_ | 1866914965663776768 |
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| author | Jeggle, Kai Czerkawski, Mikolaj Serva, Federico Saux, Bertrand Le Neubauer, David Lohmann, Ulrike |
| author_facet | Jeggle, Kai Czerkawski, Mikolaj Serva, Federico Saux, Bertrand Le Neubauer, David Lohmann, Ulrike |
| contents | IceCloudNet is a novel method based on machine learning able to predict high-quality vertically resolved cloud ice water contents (IWC) and ice crystal number concentrations (N$_\textrm{ice}$). The predictions come at the spatio-temporal coverage and resolution of geostationary satellite observations (SEVIRI) and the vertical resolution of active satellite retrievals (DARDAR). IceCloudNet consists of a ConvNeXt-based U-Net and a 3D PatchGAN discriminator model and is trained by predicting DARDAR profiles from co-located SEVIRI images. Despite the sparse availability of DARDAR data due to its narrow overpass, IceCloudNet is able to predict cloud occurrence, spatial structure, and microphysical properties with high precision. The model has been applied to ten years of SEVIRI data, producing a dataset of vertically resolved IWC and N$_\textrm{ice}$ of clouds containing ice with a 3 kmx3 kmx240 mx15 minute resolution in a spatial domain of 30°W to 30°E and 30°S to 30°N. The produced dataset increases the availability of vertical cloud profiles, for the period when DARDAR is available, by more than six orders of magnitude and moreover, IceCloudNet is able to produce vertical cloud profiles beyond the lifetime of the recently ended satellite missions underlying DARDAR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04135 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI Jeggle, Kai Czerkawski, Mikolaj Serva, Federico Saux, Bertrand Le Neubauer, David Lohmann, Ulrike Atmospheric and Oceanic Physics Artificial Intelligence Computer Vision and Pattern Recognition J.2 IceCloudNet is a novel method based on machine learning able to predict high-quality vertically resolved cloud ice water contents (IWC) and ice crystal number concentrations (N$_\textrm{ice}$). The predictions come at the spatio-temporal coverage and resolution of geostationary satellite observations (SEVIRI) and the vertical resolution of active satellite retrievals (DARDAR). IceCloudNet consists of a ConvNeXt-based U-Net and a 3D PatchGAN discriminator model and is trained by predicting DARDAR profiles from co-located SEVIRI images. Despite the sparse availability of DARDAR data due to its narrow overpass, IceCloudNet is able to predict cloud occurrence, spatial structure, and microphysical properties with high precision. The model has been applied to ten years of SEVIRI data, producing a dataset of vertically resolved IWC and N$_\textrm{ice}$ of clouds containing ice with a 3 kmx3 kmx240 mx15 minute resolution in a spatial domain of 30°W to 30°E and 30°S to 30°N. The produced dataset increases the availability of vertical cloud profiles, for the period when DARDAR is available, by more than six orders of magnitude and moreover, IceCloudNet is able to produce vertical cloud profiles beyond the lifetime of the recently ended satellite missions underlying DARDAR. |
| title | IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence Computer Vision and Pattern Recognition J.2 |
| url | https://arxiv.org/abs/2410.04135 |