A leading source of uncertainty in predicting future climate is the effect of clouds. This uncertainty arises not only because the dynamic and thermodynamic processes involved in cloud formation occur at scales smaller than climate model grid size, but also from the need to parameterize microphysical processes involved in condensation. Availability of the Stony Brook supercomputer makes it possible for our faculty to develop cloud resolving models in which the grid size is made so small that clouds can be calculated from first principles.
System for Atmospheric Modeling (SAM) – a cloud-resolving model (CRM) that evolved from a Large-Eddy Simulation (LES) model created by Dr. Marat Khairoutdinov while a Ph.D student at the University of Oklahoma. SAM has been used by several researchers in the US and Canada. It is a parallel model that uses domain decomposition and MPI communication protocol.