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000906810 1001_ $$0P:(DE-Juel1)166264$$aMozaffari, Amirpasha$$b0$$eCorresponding author
000906810 245__ $$aHPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction
000906810 260__ $$aCambridge, MA$$bMIT Press$$c2022
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000906810 520__ $$aMachine learning (ML) applications in weather and climate are gaining momentum as big data and the immense increase in High-performance computing (HPC) power are paving the way. Ensuring FAIR data and reproducible ML practices are significant challenges for Earth system researchers. Even though the FAIR principle is well known to many scientists, research communities are slow to adopt them. Canonical Workflow Framework for Research (CWFR) provides a platform to ensure the FAIRness and reproducibility of these practices without overwhelming researchers. This conceptual paper envisions a holistic CWFR approach towards ML applications in weather and climate, focusing on HPC and big data. Specifically, we discuss Fair Digital Object (FDO) and Research Object (RO) in the DeepRain project to achieve granular reproducibility. DeepRain is a project that aims to improve precipitation forecast in Germany by using ML. Our concept envisages the raster datacube to provide data harmonization and fast and scalable data access. We suggest the Juypter notebook as a single reproducible experiment. In addition, we envision JuypterHub as a scalable and distributed central platform that connects all these elements and the HPC resources to the researchers via an easy-to-use graphical interface.
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000906810 536__ $$0G:(BMBF)01IS18047A$$aVerbundprojekt DeepRain: Effiziente Lokale Niederschlagsvorhersage durch Maschinelles Lernen (01IS18047A)$$c01IS18047A$$x1
000906810 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x2
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000906810 7001_ $$0P:(DE-Juel1)180790$$aLangguth, Michael$$b1
000906810 7001_ $$0P:(DE-Juel1)177767$$aGong, Bing$$b2
000906810 7001_ $$0P:(DE-Juel1)167529$$aAhring, Jessica$$b3
000906810 7001_ $$0P:(DE-HGF)0$$aCampos, Adrian Rojas$$b4
000906810 7001_ $$0P:(DE-HGF)0$$aNieters, Pascal$$b5
000906810 7001_ $$0P:(DE-HGF)0$$aEscobar, Otoniel José Campos$$b6
000906810 7001_ $$0P:(DE-HGF)0$$aWittenbrink, Martin$$b7
000906810 7001_ $$0P:(DE-HGF)0$$aBaumann, Peter$$b8
000906810 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin G.$$b9
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