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Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
Modeling dependencies between climate extremes is important for climate risk assessment, for inst...
Y Boulaguiem, J Zschleischler, E Vignotto, K van der Wiel, S Engelke | Journal: Environmental Data Science | Volume: 1 | Year: 2022 | First page: e5 | doi: 10.1017/eds.2022.4
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Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements.
Geostationary satellites observe the earth surface and atmosphere with a short repeat
time, thus...
Chen, X.; Zhao, L.; Zheng, F.; Li, J.; Li, L.; Ding, H.; Zhang, K.; Liu, S.; Li, D.; de Leeuw, G. | Journal: Remote Sensing | Volume: 14 | Year: 2022 | First page: 980 | doi: https://doi.org/10.3390/rs14040980
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CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record
Given the important role of clouds in our planet’s climate system, it is crucial to further impro...
Tzallas, V.; Hünerbein, A.; Stengel, M.; Meirink, J.F.; Benas, N.; Trentmann, J.; Macke, A. | Journal: Remote Sensing | Volume: 14 | Year: 2022 | doi: 10.3390/rs14215548
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The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model
The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable ...
Eliasson, S., Karlsson, K. G., van Meijgaard, E., Meirink, J. F., Stengel, M., andWill´en, U | Journal: Geoscientific Model Development | Year: 2019 | doi: https://doi.org/10.5194/gmd-12-829-2019
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The MSG-SEVIRI-based cloud property data record CLAAS-2
Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial for ...
Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., and Meirink, J. F | Journal: Earth System Science Data | Year: 2017 | doi: https://doi.org/10.5194/essd-9-415-2017
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