ATM S 591: Predictability and Data Assimilation

Winter Quarter 2005. (2009)

Instructor: Greg Hakim

Class meets: MWF 2:30-3:20. ATG 310C.

syllabus  |  resources  | lecture log  |

Prerequisites: interest in geophysical modeling; basic linear algebra.

Textbook: Kalnay (2003).

Grading: 50% homework + 50% class project.

Course description

Syllabus:

  • highlights from dynamical systems theory.
  • flow stability and error growth.
  • deterministic and probabilistic forecasting; ensembles.
  • data assimilation background.
  • optimum interpolation and Kalman filters.
  • 3D variational assimilation.
  • ensemble Kalman filters.
  • 4D variational assimilation.
  • Kalman smoothers.
  • sensitivity analysis, targeting observations, network design.