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Code coupling using OASIS

  From Wednesday 30 August 2017 to Thursday 31 August 2017

  Training    

Announced
Deadline for registration: 15 days before the starting date of each training
Duration : 2 days / (14 hours)

Pre-registration

Abstract

The OASIS coupler, developed over the past 20 years at CERFACS, is a software allowing synchronized exchanges of coupling information between numerical codes representing different components of the climate system. The objectives of this course are to help the participants learn how to use OASIS and install it on their own computing platforms.

Target participants

This training session is for engineers, physicists, computer scientists and numerical analysts wishing to use this code coupling software in their own coupled model.

Prerequisites

Significant experience with the Linux operating system and Fortran programming.

Scientific contact : Laure COQUART and Sophie VALCKE

Fee

  • Trainees/PhDs/PostDocs : 100 €
  • CERFACS shareholders/INRIA : 300 €
  • CNRS : free
  • Public : 600 €**

** The training will be half price (300 € out of taxes instead of 600 € out of taxes) for the first 4 European participants registered due to explicit financing by a European project.

Program

(Every day from 9h to 17h30)

The session will start with a general presentation of OASIS and with a tutorial during which the participants will be guided step-­by-­step through the compilation and running (on CERFACS training room PCs) of a toy coupled model reproducing coupling exchanges between two pseudo models, testing different functionalities of the coupler. Then the participants will be introduced to a specific running environment developed to assess the quality of OASIS interpolations and will be invited to use it with their own model grids. Finally, the participants will install OASIS on their own computing platforms, if accessible through remote connection, and if time allows, they will start interfacing OASIS in their own model codes. This crucial step in setting up a real coupled model will be achieved more efficiently if the participants master their codes.

 

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