Articles | Volume 17, issue 3
https://doi.org/10.5194/amt-17-961-2024
https://doi.org/10.5194/amt-17-961-2024
Research article
 | 
09 Feb 2024
Research article |  | 09 Feb 2024

Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data

Sarah Brüning, Stefan Niebler, and Holger Tost

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Cited articles

Amato, F., Guignard, F., Robert, S., and Kanevski, M.: A novel framework for spatio-temporal prediction of environmental data using deep learning, Sci. Rep.-UK, 10, 22243, https://doi.org/10.1038/s41598-020-79148-7, 2020. a, b, c
Barker, H. W., Jerg, M. P., Wehr, T., Kato, S., Donovan, D. P., and Hogan, R. J.: A 3D cloud-construction algorithm for the EarthCARE satellite mission, Q. J. Roy. Meteor. Soc., 137, 1042–1058, https://doi.org/10.1002/qj.824, 2011. a, b
Bedka, K., Brunner, J., Dworak, R., Feltz, W., Otkin, J., and Greenwald, T.: Objective Satellite-Based Detection of Overshooting Tops Using Infrared Window Channel Brightness Temperature Gradients, J. Appl. Meteorol. Clim., 49, 181–202, https://doi.org/10.1175/2009JAMC2286.1, 2010. a
Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., and Meirink, J. F.: The MSG-SEVIRI-based cloud property data record CLAAS-2, Earth Syst. Sci. Data, 9, 415–434, https://doi.org/10.5194/essd-9-415-2017, 2017. a
Bieliński, T.: A Parallax Shift Effect Correction Based on Cloud Height for Geostationary Satellites and Radar Observations, Remote Sens.-UK, 12, 365, https://doi.org/10.3390/rs12030365, 2020. a
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Short summary
We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
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