Scientific Computing Group Seminars - Detail View

Speaker: Y Xing

Affiliation: Courant Institute, NYU

Talk Title: New Efficient Sparse Space-Time Algorithms for Superparameterization on Mesoscales

Invited by: Chi-Wang Shu

Time: Oct. 24 2008 11 a.m.

Location: 182 George Street, Room 110

Abstract:

Superparameterization (SP) is a large-scale modeling system with explicit representation of small-scale processes provided by a cloud-resolving model (CRM) embedded in each column of a large-scale model. Here we present new efficient sparse space-time algorithms based on the original idea of SP: the small scale model is solved in a reduced spatially periodic domain and in addition the time interval of integration of the small scale model is reduced systematically, while keeping the same large scale dynamics. The new algorithms have been applied to a stringent two-dimensional test suite involving moist convection interacting with shear with regimes ranging from strong free and forced squall lines to dying scattered convection as the shear strength varies. The numerical results are compared with the CRM and original SP. It is shown here that for all the regimes of propagation and dying scattered convection, the large scale variables such as horizontal velocity and specific humidity are captured in a statistically accurate way based on space-time reduction of the small scale models by a factor of 1/3; thus, the new efficient algorithms for SP result in a gain of roughly a factor of 10 in efficiency while retaining a statistical accuracy on the large scale variables. These results suggest the possibility of using these efficient new algorithms for limited area mesoscale ensemble forecasting.