Training Sprint: Tsunami and Meteo-Tsunami Modelling

Course/Event Essentials

Event/Course Start
Event/Course End
Event/Course Format
Live (synchronous)

Venue Information

Country: Spain
Venue Details: Click here

Training Content and Scope

Level of Instruction
Sector of the Target Audience
Research and Academia
Public Sector
HPC Profile of Target Audience
Application Users
Application Developers
Data Scientists
Language of Instruction

Other Information

Supporting Project(s)
Event/Course Description

ChEESE Tsunami and Meteo-Tsunami Modelling Training Course aims to provide the participants with the knowledge and numerical tools required to perform the mathematical modelling and numerical simulation of tsunamis and meteo-tsunamis. A brief theoretical overview on the processes and mechanisms producing this kind of natural events will be first provided. Then, the European flagship code, Tsunami-HySEA, will be used to simulate various tsunami and meteo-tsunami events. Tsunami-HySEA solves the 2D shallow water equations on hydrostatic and dispersive versions. Based on a high-order Finite Volume (FV) discretisation (hydrostatic) with Finite Differences (FD) for the dispersive version on two-way structured nested meshes in spherical coordinates. Meteo-HySEA is a version of Tsunami-HySEA code able to include meteorological forcing (atmospheric pressure and wind stress) as input, more precisely as boundary conditions.

The training course will include an introduction to tsunami and meteo-tsunami modelling, the basics of the numerical code used and the required data. The course will also tackle pre-process and post-process strategies based on Python scripts.


A basic knowledge of Python is recommended (used in preprocessing and postprocessing scripts).

Learning Outcomes:

Participants will learn how to perform tsunami and meteo-tsunami simulations with real data and for high-resolution scenarios. In the case of tsunamis, nested grids and high-resolution inundation will be performed. These simulations can be performed in local GPUs or in HPC multi-GPUs infrastructures.

Academic Staff:

  • Jorge Macías (UMA)
  • Clea Denamiel (RBI)
  • Carlos Sánchez Linares (UMA)
  • Alejandro González del Pino (UMA)

IP Notice:

Attendees can only download, make and retain a copy of the materials for their use for non‐commercial and research purposes. The User may not commercially use the material unless has been granted prior written consent by the Licensor.

Further information:

This webinar, crafted by the ChEESE Equality Committee, welcomes participation from all EuroHPC Centers of Excellence and other interested parties.