• Two artificial neural network (ANN) models are built to forecast SYM-H index 1 hour ahead using interplanetary magnetic field measurements. • The developed models are based on two conceptually different neural networks: Long Short-Term Memory and Convolutional Neural Network (CNN). • CNN, used here for the first time for geomagnetic indices forecasting, has proved potentialities worth being further explored.

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This paper presents the project Comprehensive spAce wEather Studies for the ASPIS prototype Realization (CAESAR), which aims to tackle the relevant aspects of Space Weather (SWE) science and develop a prototype of the scientific data centre for Space Weather of the Italian Space Agency (ASI) called ASPIS (ASI SPace Weather InfraStructure). To this end, CAESAR involves the majority of the SWE Italian community, bringing together 10 Italian institutions as partners, and a total of 92 researchers. The CAESAR approach encompasses the whole chain of phenomena from the Sun to Earth up to planetary environments in a multidisciplinary, comprehensive, and unprecedented way. Detailed and integrated studies are being performed on a number of well-observed “target SWE events”, which exhibit noticeable SWE characteristics from several SWE perspectives. CAESAR investigations synergistically exploit a great variety of different products (datasets, codes, models), both long-standing and novel, that will be made available in the ASPIS prototype: this will consist of a relational database (DB), an interface, and a wiki-like documentation structure. The DB will be accessed through both a Web graphical interface and the ASPIS.py module, i.e., a library of functions in Python, which will be available for download and installation. The ASPIS prototype will unify multiple SWE resources through a flexible and adaptable architecture, and will integrate currently available international SWE assets to foster scientific studies and advance forecasting capabilities.