This course describes contemporary methods, recent advancements, and current challenges in numerical techniques used across a broad range of weather prediction models for solving the governing equations of atmospheric dynamics on High-Performance Computing (HPC) systems. The methods covered are relevant to short and medium-range forecasting, seasonal prediction, climate modelling, and both global and limited area simulations.
Topics
- Governing equations and hydrostatic/non-hydrostatic dynamics
- The spectral transform, semi-implicit, semi-Lagrangian integration method of the ECMWF model dynamical core
- Horizontal and vertical discretizations
- Advection schemes, time-integration methods, semi-implicit methods and elliptic solvers
- Finite volume and finite element methods, high-order discontinuous spectral element discretization methods
- Structured and quasi-uniform grids, unstructured meshes and mesh generation
- Massively parallel computing
- Contrasts and synergies between established numerical weather prediction models and currently explored machine learning models for weather prediction
Requirements
Participants should have a good mathematical background and are expected to be familiar with the contents of standard meteorological and applied mathematical textbooks, along with some knowledge of numerical discretization methods for partial differential equations. A familiarity with python is useful for some of the practical sessions.
Other complementary material can be found in our lecture note series.
Some general knowledge or experience in numerical weather prediction is an advantage.
All lectures are given in English.