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WP1: Algorithms and Mathematics

Aim: WP1 will develop mathematical methods and implement advanced and disruptive algorithms suitable for extreme-scale parallelism that achieve major improvements in the accuracy, efficiency, fault-tolerance, and scalability of dynamical cores and of physical parametrizations for next-generation weather and climate prediction models. Moreover, WP1 will extract and provide a range of relevant algorithmic motifs (weather and climate dwarfs) as a prerequisite for other work packages. These will include key algorithms of advection, time-stepping methodologies, and of physical parametrizations, representative for leading European weather and climate models.

Approach and methodology: A significant contribution to this work package will build on weather and climate dwarfs developed in ESCAPE. In order to widen the spectrum of algorithmic concepts used in Earth system modelling, contributions will be extracted from the ocean models NEMO and ICON-ocean, and the radiation physical parametrizations in the context of Artificial Neural Networks (ANN). Additionally, newly developed dwarfs will cover a semi-Lagrangian higher-order, DG-approach and a fault-tolerant implementation of an iterative elliptic solver.

Suitability of the research approach: WP1 will cover a wide range of relevant algorithms used in weather and climate sciences. It facilitates the comprehensive definition of a user-friendly and widely applicable DSL toolchain as well as the definition of highly-relevant benchmarks for vendors and HPC hardware developers. 

Measures for Success of the Work Package/ KPIs:  

  • Weather and climate dwarfs delivered, covering at least eight different algorithmic motifs (dwarfs).
  • Development of a large time-step, highly-scalable, higher-order method.
  • Proof-of-concept of the ANN approach.