Submitted as: model description paper
21 Mar 2023
Submitted as: model description paper |  | 21 Mar 2023
Status: a revised version of this preprint is currently under review for the journal GMD.

ParticleDA.jl v.1.0: A real-time data assimilation software platform

Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas

Abstract. Digital twins of physical and human systems informed by real-time data, are becoming ubiquitous across weather forecasting, disaster preparedness, and urban planning, but researchers lack the tools to run these models effectively and efficiently, limiting progress. One of the current challenges is to assimilate observations in highly nonlinear dynamical systems, as the practical need is often to detect abrupt changes. We developed a software platform to improve the use of real-time data in highly nonlinear system representations where non-Gaussianity prevents the use of more standard Data Assimilation. Optimal Particle filtering data assimilation (DA) techniques have been implemented within an user-friendly open source software platform in Julia – ParticleDA.jl. To ensure the applicability of the developed platform in realistic scenarios, emphasis has been placed on numerical efficiency, scalability and optimisation for high performance computing frameworks. Furthermore, the platform has been developed to be forward model agnostic, ensuring that it is applicable to a wide range of modelling settings, for instance unstructured and non-uniform meshes in the spatial domain or even state spaces that are not spatially organised. Applications to tsunami and numerical weather prediction demonstrate the computational benefits in terms of lower errors, lower computational costs (due to ensemble size and the algorithm's overheads being minimised) and versatility thanks to flexible I/O in a high level language Julia.

Daniel Giles et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-38', Anonymous Referee #1, 13 Apr 2023
  • CEC1: 'Comment on gmd-2023-38', Juan Antonio Añel, 05 May 2023
    • AC1: 'Reply on CEC1', Daniel Giles, 05 May 2023
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 05 May 2023
  • RC2: 'Comment on gmd-2023-38', Anonymous Referee #2, 27 May 2023

Daniel Giles et al.

Daniel Giles et al.


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Short summary
Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. One of the current challenges is the optimal use of real-time observations. The work presented here focuses on a developed open source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency and scalability.