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.]