AOSC 615: Advance Methods in Data Assimilation
Spring 2011
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%.
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:
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
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.
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.
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.]
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
Kayo Ide at UMD AOSC 615 Spring 2011