AOSC 615: Advance Methods in Data Assimilation
Spring 2009
Course Description:
Primary Activities
The course provides an in-depth overview of the advanced data assimilation methods. It covers theory and techniques, as well as possible drawbacks and strategies to overcome them. For major methods, student project and presentation are assigned to gain practical experience.
Guest Lectures on Special Topics
Some lectures will be given by guest speakers who are the leading experts of data assimilation.
References: [No required textbook]
Data Assimilation
Atmospheric Modeling, Data Assimilation and Predictability by Kalnay, 2003.
Dynamic Data Assimilation: A Least Squares Approach (Encyclopedia of Mathematics and its Applications) by John M. Lewis. S. Lakshmivarahan, and Sudarshan Dhall, 2006.
Atmospheric Data Analysis (Cambridge Atmospheric and Space Science Series) by Roger Daley, 1993.
Data Assimilation: The Ensemble Kalman Filter by Geir Evensen, 2007.
Related Topics
Sequential Monte Carlo Methods in Practice by Arnaud Doucet, Nando de Freitas, Neil Gordon, (Eds.) 2001.
Stochastic Processes and Filtering Theory by Andrew H. Jazwinski, 1974.
Inverse Problem Theory and Methods for Model Parameter Estimation by Albert Tarantola, 2005.
Prerequisite/Corequisite and Credits:
Prerequisite: AOSC 614 is preferred but not strictly required.
Grading Policy:
Students are responsible for checking the UMD Honor code.
Credits are based on: attendance/participation: 23%; projects/assignment: 40%; & final presentation/report: 30%.
Schedule
Weekly
12:30pm-1:45pm TuTh [CSS 1113] Class
2:00pm-2:45pm Tu [CSS 3403] Office hour
Guest Lectures
(*) 04/14 Tu Dr. Eugenia Kalnay (UMD) Advanced Algorithms of Local Ensemble Transform Kalman Filter
(**) 04/23 Th Dr. Takemasa Miyoshi (UMD) Operational Development of the Ensemble Kalman Filter at JMA
(***) 04/30 Th Dr. John Derber (NOAA) Satellite Data Assimilation
Schedule
Weekly
12:30pm-1:45pm TuTh [CSS 1113] Class
2:00pm-2:45pm Tu [CSS 3403] Office hour
Guest Lectures
(*) 04/14 Tu Dr. Eugenia Kalnay (UMD) Advanced Algorithms of Local Ensemble Transform Kalman Filter
(**) 04/23 Th Dr. Takemasa Miyoshi (UMD) Operational Development of the Ensemble Kalman Filter at JMA
(***) 04/30 Th Dr. John Derber (NOAA) Satellite Data Assimilation
Syllabus:
Week Date [in 09] Topics Notes
1. 01/29 Introduction to Data Assimilation Lect 1
2. 02/03 & 05 Background Material, Least Square Estimation  
3. 02/10 & 12 Least Square Estimation, 3D-Var  
4. 02/17 & 19 3D-Var  
5. 02/24 & 26 Optimal Interpolation, Topics in 3D Data Assimilation Lect 6
6. 03/03 & 05 Project 1 <Presentation>, Observability, Extended Kalman Filter Lect 9
7. 03/11 Observability Seminar, Extended Kalman Filter Lect 11
8. 03/24 & 26 Ensemble Prediction, Project 2 <Presentation>  
9. 03/31 & 04/02 Singular Vectors, Ensemble Kalman Filter  
10. 04/07 & 09 Ensemble Kalman Filter, Breeding EnKF
11. 04/14 & 16 Advanced Schemes of LETKF (*), Project 3 <Presentation>  
12. 04/21 & 23 4D-Var and Operation Development of the Ensemble Kalman Filter at JMA (**) JMA DA
13. 04/28 4D-Var and Satellite Data Assimilation (***) 4DVar
14. 05/05 & 07 Project 4 <Presentation>, Validation of Data Assimilation System Adjoint check by Daryl Kleist
15. 05/12 & 14 Final <Presentation>  
16. 05/18 Term Report  
Suggested Project Models:
Lorenz 3 variable model
  Ref: Lorenz, E. N., 1963: Deterministic non-periodic flow. J. Atmos. Sci., 20, 130-141.
  matlab: (i) lorenz63.m*; (ii) lorenz63_dxdt.m     [*: main code]
Lorenz 40 variable model
  Ref: Lorenz, E. N., 1995: Predictability: a problem partly solved. ECMWF proceedings for Seminar on Predictability, 1-18.
  matlab: (i) lorenz95.m*; (ii) lorenz95_dxdt.m
Point Vortex Model
  Ref: Hassen, Aref, 2007: Point vortex dynamics - A classical mathematics playground. J. Math. Phys., 48, 065401. [Tracer dynamics is obtained by treating tracers as point vortices with zero circulation.]
  matlab: (i) pvt.m*; (ii) pvt_dxdt.m
SPEEDY (Simplified Parameterization, primitivE-Equation Dynamics AGCM)
  Ref: Molteni, F., 2003: Atmospheric simulations using a GCM with simplified physical parameterization. I: Model climatology and variability in multi-decadal experiments. Climate Dynamics, 20, 171-191.
  fortran: (i) documentation; (ii) code package. [Both prepared by Dr. Junjie Liu for AOSC 614 taught by Prof. Eugenia Kalnay.]
Kayo Ide at UMD AOSC 615 Spring 2009