Atmosphere Ocean Science Colloquium
Data-driven parameterization and super-parameterization of subgrid-scale processes using deep learning
Speaker: Pedram Hassanzadeh, Rice University
Date: Wednesday, November 4, 2020, 3:30 p.m.
Resolving all the relevant length and time scales in simulations of the turbulent atmospheric and oceanic circulations remains out of reach due to computational constraints. In practice, low-resolution models resolve the large-scale processes while the effects of the small-scale processes are often parameterized in terms of the large-scale variables. More recently, super-parameterization (SP), which involves solving for small-scale processes on a high-resolution grid embedded within the low-resolution grid, has attracted attention and shown advantages over parameterization, but SP’s applicability remains limited to its high computational cost. In the past few years, data-driven parameterization (DD-P) using deep learning has shown promising results, but numerical stability (in online or a posteriori simulations) and generalization (i.e., extrapolation) have remained as challenging and important issues to address. In this talk, using a multi-scale Lorenz 96 system and 2D turbulent flow as testbeds, we 1) Introduce a data-driven SP (DD-SP) framework in which the equations of small-scales are integrated data-drivenly using deep learning to reduce the cost, 2) Show the promises of DD-P in representing subgrid-scale effects, in particular by capturing energy backscattering, while remaining numerically stable, and 3) Demonstrate how transfer learning enables DD-P and DD-SP to generalize to more chaotic systems or flows with higher Reynolds numbers.