HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction  

作　　者：Amirpasha Mozaffari Michael Langguth Bing Gong Jessica Ahring Adrian Rojas Campos Pascal Nieters Otoniel Jose Campos Escobar Martin Wittenbrink Peter Baumann Martin G.Schultz 

机构地区：[1]Forschungszentrum Julich GmbH,52425 Julich,Germany [2]Osnabruick University,49074O snabrick,Germany [3]Jacobs University Bremen,28759 Bremen,Germany [4]Deutscher Wetterdienst,63067 Offenbach am Main,Germany

出　　处：《Data Intelligence》2022年第2期271-285,共15页数据智能（英文）

基　　金：German Bundesministerium fuer Bildung und Forschung(BMBF)for funding the DeepRain project under grant agreement 01 IS18047A-E.

摘　　要：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.

关 键 词：FAIR REPRODUCIBILITY Machine learning Earth system sciences WORKFLOW 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] P4[自动化与计算机技术—控制科学与工程] P208[天文地球—大气科学及气象学]