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HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction 被引量:1
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作者 Amirpasha Mozaffari Michael Langguth +7 位作者 Bing Gong Jessica Ahring Adrian Rojas Campos Pascal Nieters Otoniel Jose Campos Escobar Martin Wittenbrink Peter Baumann Martin G.Schultz 《Data Intelligence》 EI 2022年第2期271-285,共15页
Machine 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 pract... Machine 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. 展开更多
关键词 FAIR REPRODUCIBILITY Machine learning earth system sciences WORKFLOW
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