| 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) |