As Ocean General Circulation Models (OGCMs) move into the petascale age, where the output from global high-resolution model runs can be of the order of hundreds of terabytes in size, tools to analyse the output of these models will need to scale up too. Lagrangian Ocean Analysis, where virtual particles are tracked through hydrodynamic fields, is an increasingly popular way to analyse OGCM output, by mapping pathways and connectivity of biotic and abiotic particulates. However, the current software stack of Lagrangian Ocean Analysis codes is not dynamic enough to cope with the increasing complexity, scale and need for customisation of use-cases. Furthermore, most community codes are developed for stand-alone use, making it a nontrivial task to integrate virtual particles at runtime of the OGCM. Here, we introduce the new Parcels code, which was designed from the ground up to be sufficiently scalable to cope with petascale computing. We highlight its API design that combines flexibility and customisation with the ability to optimise for HPC workflows, following the paradigm of domain-specific languages. Parcels is primarily written in Python, utilising the wide range of tools available in the scientific Python ecosystem, while generating low-level C-code and using Just-In-Time compilation for performance-critical computation. We show a worked-out example of its API, and validate the accuracy of the code against seven idealised test cases. This version~0.9 of Parcels is focussed on laying out the API, with future work concentrating on optimisation, efficiency and at-runtime coupling with OGCMs.
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