| SES | TOPICS | KEY DATES |
|---|---|---|
| 1 |
Introduction/Prediction Needs Course Description and Expectations Motivation Presentation of Possible Project Topics |
|
| 2-4 |
Attractors and Dimensions Definitions (Ses #2) Attractor Dimensions (Ses #3) Embedding (Ses #4) |
Problem Set 1 out (Ses #3) |
| 5-10 |
Sensitive Dependence to Initial Conditions Lyapunov Exponents (Ses #5-6) Singular Vectors and Norms (Ses #7-9) Validity of Linearity Assumption (Ses #10) |
Problem Set 1 due (Ses #5) Problem Set 2 out (Ses #6) Problem Set 1 returned (Ses #7) Problem Set 2 due (Ses #8) Problem Set 2 returned (Ses #10) Problem Set 3 out (Ses #10) |
| 11-18 |
Probabilistic Forecasting Probability Primer (Ses #12) Stochastic-Dynamic Prediction (Ses #11-12) Monte-Carlo (Ensemble) Approximation (Ses #12) Ensemble Forecasting Climate Change (Ses #13, 15, 17) Ensemble Construction (Perfect, Unconstrained, Constrained) (Ses #16) Ensemble Assessment (Ses #18) |
Problem Set 3 due (Ses #12) Problem Set 3 returned (Ses #13) |
| 19-22 |
Data Assimilation Definition and Kalman Filter Derivations (Ses #19-20) 3dVar and 4dVar Derivations (Ses #20) Adjoint Models (Ses #21) Nonlinear Data Assimilation (Ses #21) Ensemble-Based Data Assimilation (Ses #22) |
Problem Set 4 out (Ses #19) Problem Set 4 due (Ses #22) |