SES  TOPICS  KEY DATES 

1 
**Introduction/Prediction Needs ** Course Description and Expectations Motivation Presentation of Possible Project Topics 

24 
**Attractors and Dimensions ** Definitions (Ses #2) Attractor Dimensions (Ses #3) Embedding (Ses #4) 
Problem Set 1 out (Ses #3) 
510 
Sensitive Dependence to Initial Conditions
Lyapunov Exponents (Ses #56) Singular Vectors and Norms (Ses #79) 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) 
1118 
**Probabilistic Forecasting
**Probability Primer (Ses #12) StochasticDynamic Prediction (Ses #1112) MonteCarlo (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) 
1922 
**Data Assimilation
**Definition and Kalman Filter Derivations (Ses #1920) 3dVar and 4dVar Derivations (Ses #20) Adjoint Models (Ses #21) Nonlinear Data Assimilation (Ses #21) EnsembleBased Data Assimilation (Ses #22) 
Problem Set 4 out (Ses #19)
Problem Set 4 due (Ses #22) 
Calendar
Course Info
Instructor
Departments
As Taught In
Spring
2003
Level
Topics
Learning Resource Types
assignment
Programming Assignments