Argovis: Building a FAIR Ocean Data Service

Abstract The current ecosystem of Internet-facing search and distribution tools for ocean data has limited options suitable for supporting the fast queries needed to underwrite applications that update their data frequently and on demand, such as visualization websites, interactive educational activities, and analyses constructed as living documents. These applications are best served by a Representational State Transfer (RESTful) application programming interface (API) with incisive search capabilities over an appropriately indexed database of ocean datasets represented with consistent encoding. For this purpose, a new Argovis API was developed and released, along with a web-facing frontend and a collection of Jupyter notebooks that leverage it and demonstrate its capabilities. This paper reviews the key engineering decisions and reference architecture used by Argovis to create a responsive, FAIR ocean data service for Argo and ship-based profiles, derived gridded fields and other products, observations from the Global drifter program, tropical cyclone track data, an atmospheric river climatology, and weekly gridded fields of sea surface temperature, sea level anomaly, and surface winds based on satellite data. We also tour some of the use cases and applications of this data service, both by the Argovis team and third-party consumers.