MR4323 Air and Ocean Numerical Prediction Systems

Numerical models of atmospheric and oceanic phenomena. Major components and sources of error for operational primitive equation prediction systems. Data assimilation concepts, techniques, and limitations. Finite difference, spectral, and finite element methods, computational instability, and approximation error. Horizontal grid variants, vertical coordinate systems, and factors affecting resolution. Overview of subgridscale processes and boundary conditions: physical parameterizations of moisture and convection; land surface models; air-ocean coupling; ocean surface forcing; topography and bathymetry; hydrostatic and nonhydrostatic ocean models. Verification methods and model output. Introduction to uncertainty, chaos, and ensembles.

Prerequisite

MR4322, OC4211, partial differential equation, MA3232 desirable

Lecture Hours

4

Lab Hours

2

Course Learning Outcomes

• Apply basic numerical methods and approximation techniques to discretize the primitive equations of fluid flow and quantify associated truncation errors
• Identify and describe the main components of numerical prediction systems such as the dynamical core, physics parameterizations, and data assimilation (DA) and how they combine to constitute the forecast cycle
• Apply simple DA methods and explain how they relate to operational DA systems
• Understand the theoretical basis of ensemble forecast systems using concepts of chaotic dynamical systems and probabilistic prediction
• Articulate the advantages and limitations of commonly used forecast verification metrics