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: 30%; projects/assignment: 50%; & final presentation/report: 20%.

Schedule

Weekly

	12:30pm-1:45pm	TuTh	[CSS 1113]	Class
	2:00pm-2:45pm	Th	[CSS 3403]	Office hour

Guest Lectures

	12:30pm-1:45pm	April 12, Tu.	[CSS 1113]	Lecture Note
	Dr. Ron Errico (NASA GSFC) "Advantages and Disadvantages of Adjoint Approach in Comparison with Other Methods"
	12:30pm-1:45pm	TBA (April)	[CSS 1113]
	Daryl Kleist (NOAA NCEP / UMD AOSC) "Operational Hybrid Data Assimilation System at NOAA NCEP"

Special Topic Seminars [To Be Updated Throughout the Semester]

	2:00pm-3:00pm	Feb 23, W	[CSIC 4122]
	Professor Tom Hain (Johns Hopkins University) "Simulation and Assimliation of Denmark Strait Overflow"
	3:00pm-:00p4m	Feb 25, F	[MATH3206]
	Professor Andy Majda (NYU Courant Institute) "Mathematical Strategies for Real Time Filtering of Turbulent Dynamical Systems"
	3:30pm-4:30pm	March 3, Th	[CSS 2400]
	Dr. Steve Lord (NOAA NWS) "Recent Progress in Analysis and Prediction at the National Centers for Enviromental Prediction, Environmental Modeling Center" [Note: No AOSC 615 Lecture on March 3]
	12:00pm-1:00pm	March 9, W	[CSS 2324]
	Dr. Lars Nerger (Alfred Wegener Institute, Germany) "The Data-Assimilation Zoo. Section: Kalman Filters"
	11:30am-12:30pm	March 14, M	[CSS 2324]
	Dr. Takemasa Miyoshi (U. Maryland) "Ideas for Improving Ensemble Data Assimilation"
	11:30am-12:30pm	March 16, W	[CSS 2324]
	Dr. Nicholas Schutgens (U. Tokyo, Japan) "Aerosol Assimilation and its Applications"
	3:30pm-4:30pm	April 21, Th	[CSS 2400]
	Prof. Michael Morgan (U. Wisconsin - Madison) "TBA - Maybe on adjoint"

Topics to be Covered:

1.	Introduction
2.	Background
3.	"3D" Methods
4.	Uncertainty Evolution
5.	"4D" Methods
6.	Advanced Methods
7.	Special Topics

Exercises and Projects:

Exercises

1.	No Due [Expected for Project 2]	Construction of Covariance from Data Set
2.	No Due [Expected for Project 2]	Implementation of Computational Optimization Methods
3.	No Due [Expected for Project 3]	Verification & Diganostic Module
4.	No Due [Expected for Project 4]	4D-ness in Observation and Dynamics
5.	No Due [Expected for Project 5]	Ensemble Forecast as Data Assimilation System

Projects

1.	Report:	Feb 17, Th	Computational Framework of Data Assimilation [Forecast=Analysis]
2.	Presentation:	Mar 01, Tu	3D Methods
	Report:	Mar 03, Th
3.	Presentation:	Mar 17, Th	Extended Kalman Filter
	Report:	Mar 24, Th
4.	Presentation:	Apr 14, Th	4D-Var (revised description as of Apr 10)
	Report:	Apr 19, Tu
5.	Presentation:	May 03, Tu	Ensemble Kalman Filter
	Report:	May 05, Th
6.	Presentation:	May 10, Tu	Final Project
	Report:	May 17, Tu

Suggested Project Models:

	Lorenz 3 variable model
		Ref:	(i)	Lorenz, E. N., 1963: Deterministic non-periodic flow. J. Atmos. Sci., 20, 130-141.
			(ii)	Kalnay, E. and co-authors, 2007: 4-D-Var or Ensemble Kalman filter? Tellus, 59A, 758-773.
		matlab:	(i)	lorenz63.m*; (ii) lorenz63_dxdt.m     [*: main code]
	Lorenz 40 variable model
		Ref:	(i)	Lorenz, E. N., 1995: Predictability: a problem partly solved. ECMWF proceedings for Seminar on Predictability, 1-18.
			(ii)	Lorenz, E. N. and K. Emanuel, 1998: Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model, J. Atmos. Sci. 45, 399-414.
		matlab:	(i)	lorenz95.m*; (ii) lorenz95_dxdt.m
	Point Vortex Model
		Ref:	(i)	Aref, H.. 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.]
			(ii)	Kuznetsov, L., K. Ide, CKRT Jones, 2003: A Method for Assimilating Lagrangian Data. MWR, 131, 2247-2260.
		matlab:	(i)	pvt.m*; (ii) pvt_dxdt.m