Simone graduated in Physics at the University of Turin in 2016, after completing an Erasmus internship at the Karlsruhe institute of technology (KIT) where he performed a statistical analysis of cosmic rays to speed up the data retrieval process in a non-relational database. In 2018 he obtained a Master’s degree in Stochastics and Data Science in Turin, with his final thesis dealing with the impact of Recommender systems on a network of online consumers. He is looking forward to widening his knowledge about dynamical processes on biological and social complex systems. In his spare time, he practises Aikido, running and enjoys watching Japanese anime. He is keen on video-games and programmed two indie games for portable devices with some geeky friends.

Research project title:
Adaptive Semi-implicit Semi-Lagrangian (SISL) method for the Shallow Water System

Supervisor(s):
Chris Budd

Project description:
Numerical Weather prediction is an essential component to weather forecasting and climate modelling. It is based on the design of accurate and efficient numerical schemes to simulate the motion of ocean and atmosphere. In such context, explicit numerical methods have to satisfy the CFL condition, which imposes a strict time step restriction, in order to be stable. To overcome this limit, the Met Office is currently adopting the Semi-Implicit, Semi-Lagrangian method (SISL), which permits the use of larger time steps without stability issues. However, many global meteorological phenomena of relevance (storms, tsunami) occur on a scale smaller than 25km, that cannot be efficiently resolved by SISL with an uniform grid. A natural way to fix this is to cluster the mesh points in proximity of small features evolving in time. Such adaptive methods, though, are inefficient to use because are either unstable or require small time steps. This issue can be avoided by coupling them with a SISL method. Simone will investigate the adaptive SISL scheme applied on the Shallow Water system, that models the shallow atmosphere. He will start to examine the accuracy and stability of this method in the 1D case. This will be then extended to 2/3 dimensions based on the optimal transport moving mesh strategy.