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Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models
Climate change has a large influence on the occurrence of extreme hydrological events. However, r...
SM Hauswirth, K van der Wiel, MFP Bierkens, V Beijk, N Wanders | Journal: Frontiers in Water | Volume: 5 | Year: 2023 | First page: 11008108 | doi: 10.3389/frwa.2023.1108108
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Development of a SeaWinds wind product for weather forecasting
SeaWinds data are provided by the Jet Propulsion Laboratory (JPL) at 25-km resolution, whereas mo...
M Portabella, A Stoffelen, J de Vries | Conference: International Geoscience and Remote Sensing Symposium (IGARSS) | Organisation: IEEE | Place: Sydney, Australia | Year: 2001 | First page: 1076 | Last page: 1078
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CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations
CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible
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Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink | Journal: Earth System Science Data | Volume: 15 | Year: 2023 | First page: 5153 | Last page: 5170 | doi: 10.5194/essd-15-5153-2023
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SCIAMACHY's View of the Changing Earth's Environment
Since August 2002 SCIAMACHY delivers a wealth of high-quality data permitting to study the status...
H Bovensmann, I Aben, M van Roozendael, S Kuehl, M Gottwald, C von Savigny, M Buchwitz, A Richter, C Frankenberg, P Stammes, M de Graaf, F Wittrock, M Sinnhuber, A Schoenhardt, S Beirle | Year: 2011
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Isoprene emissions track the seasonal cycle of canopy temperature, not primary production: evidence from remote sensing
Isoprene is important in atmospheric chemistry, but its seasonal emission pattern – especially in...
PN Foster, M van Weele, et al. | Status: published | Journal: Biogeosciences | Volume: 11 | Year: 2014 | doi: 10.5194/bg-11-3437-2014
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