Abstract

The forecast of weather relies on computer models that need to be executed in real-time, meaning that a forecast needs to be disseminated to users well before the time period for which it is made. A challenge in the future will be to succeed in using the computing power available in massively parallel high-performance computers and meet the real-time requirement. Until now weather forecast and related climate simulation models have taken advantage of the parallelism of the computers by dividing the task to be performed in the horizontal space dimensions.

The purpose of this work is to develop algorithms that allow also parallelism in the time dimension. This increased parallelism should allow an acceleration of the execution time of weather and climate models. This acceleration in turn permits an increase in the space accuracy of models while still meeting the real-time requirement. The talk will present our preliminary work on the “Parareal” algorithm that has been developed for that purpose (Lions et al., 2001) and whose applications to date have included among others air quality, but ignored weather forecasting.

Weather forecasting presents a challenge for the method because of the presence of waves and advection. An important question is to examine how the traditional way to accelerate models with the semi-implicit semi-Lagrangian methodology can be advantageously blended with the Parareal approach.