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AOSC 615: Advanced Methods in Data Assimilation
[Spring 2009] |
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Instructor
Kayo Ide (Email: ide at umd.edu; Office: 3403 CSS)
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Guest Lecturers
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Name |
Date |
Title/Subject |
| (*) |
Eugenia Kalnay |
(UMD) |
4/14/09 |
Advanced Algorithms of Local Ensemble Transform Kalman Filter |
| (**) |
Takemasa Miyoshi |
(UMD) |
4/23/09 |
Opertional Development of the Ensemble Kalman Filter at JMA |
| (***) |
John Derber |
(NCEP) |
4/30/09 |
Satellite Data Assimilation |
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Course Description
The course will provide an in-depth overview of the advanced data assimilation methods.
It will cover theory and techniques,
as well as possible drawbacks and strategies to overcome them.
The instructions will consist of two classes a week (total 2.5hr/week),
spent between classroom lectures and hand-on exercises.
Some lectures will be given by guest speakers who are the leading experts
of data assimilation.
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Dates and Location
12:30-1:45, TuTh. CSS 1113.
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Office Hours
10:00-11:00, MW. CSS 3403 or by appointment.
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Grading Policty
Attendance/Participation (30%), Projects/Problem sets (40%),
& Final Presentation (30%).
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Assignments
| No. |
Due Date |
Assignment |
Download |
| 1. |
2/ 23, Monday |
Short report (1-2 pages) focusing on
how the background error covariance matrix is constructed in these papers: |
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Parrish, David F. and John C. Derber, 1992:
The National Meteorological Center's Spectral Statistical-Interpolation Analysis
System. Mon. Wea. Rev., 120, 1747-1763. |
pdf |
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Hollingsworth, A., and P. Lonnberg, 1986:
The statistical structure of short-range forecast errors as determined from radiosonde data.
Part I: The wind field.
Tellus, 38A, 111-136. |
pdf |
| 2a. |
3/ 3, Tuesday |
10-min presentation of the Project 1. 3D-var and OI |
pdf |
| 2b. |
3/ 5, Thursday |
Report on Project 1. 3D-var and OI |
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| 3a. |
3/ 26, Thursday |
10-min presentation of the Project 2. EKF |
pdf |
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Ref on observing system example:
Lorenz E. N., and K. A. Emanuel, 1998: Optimal sites for supplemental weather observations:
Simulation with a small model. J. Atmos. Sci,, 55, 399-414. |
pdf |
| 3b. |
3/ 31, Tuesday |
Report on Project 2. EKF |
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| 4a. |
4/ 16, Thursday |
10-min presentation of the Project 3. EnKF |
pdf |
| 4b. |
4/ 17, Friday |
Report on Project 3. EnKF |
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| 5a. |
5/ 5, Tuesday |
10-min presentation of the Project 4. 4D-Var |
pdf (revised) |
| 5b. |
5/ 7, Thursday |
Report on Project 4. 4D-Var |
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| 6a. |
5/ 12, Tuesday |
30-min presentation of the Final Project
(#: Sabrina Rainwater & Steve Greybush) |
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| 6b. |
5/ 14, Thursday |
30-min presentation of the Final Project
(##: Adrienne Norwood & Daryl Kleist) |
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| 6c. |
5/ 18, Monday |
Report on the Final Project |
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Project Models & Supplemental Codes (+)
| No. |
Models |
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Download |
| 1. |
Lorenz 3 dimensional model ('63) |
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[Ref] |
Lorenz, E. N., 1963: Deterministic non-periodic flow.
J. Atmos. Sci., 20, 130-141.
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pdf |
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[Matlab] |
(i) lorenz63.m*; (ii) lorenz63_dxdt.m
[*: main code] |
(i) (ii) |
| 2. |
Lorenz 40 dimensional model ('95) |
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[Ref] |
Lorenz, E. N., 1995: Predictability: a problem partly solved.
ECMWF proceedings for Seminar on Predictability, 1-18.
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pdf |
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[Matlab] |
(i) lorenz95.m*; (ii) lorenz95_dxdt.m |
(i) (ii) |
| 3. |
Point vortex model with tracers |
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[Ref] |
Hassen, Aref, 2007: Point vortex dynamics - A classical mathematics playground.
J. Math. Phys., 48, 065401.
[Tracer dynamcs is obtained by treating tracers as point vortices
with zero circulation.]
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pdf |
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[Matlab] |
(i) pvt.m*; (ii) pvt_dxdt.m |
(i)
(ii) |
| 4. |
SPEEDY
(Simplified Parametrization, primitivE-Equation Dynamics AGCM) |
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[Ref] |
Molteni, F., 2003: Atmospheric simulations uing a GCM with simplified
physical parametrizations.
I: Model climatology and variability in multi-decadal experiments.
Climate Dynamics, 20, 171-191.
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pdf |
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[Fortran] |
(i) documentation; (ii) code package.
[Both prepared by Dr. Junjie Liu for AOSC 614
taught by Prof. Eugenia Kalnay.] |
(i)
(ii) |
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Minimization based on quasi-Newton's method (BFGS) |
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[Matlab] |
(i) bfgs.m; (ii) wolfe.m
[Software by
Prof. Stefan Ulbrich]
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(i)
(ii) |
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Syllabus and Lecture Notes
| Week |
Dates [/2009] |
Topics |
Notes |
| 1. |
1/
29
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Introduction to Data Assimilation |
Lect.1 |
| 2. |
2/ 3 & 5 |
Background Materials & Least Square Estimation |
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| 3. |
2/ 10 & 12 |
Least Square Estimation & 3D-Var |
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| 4. |
2/
17 & 19 |
3D-Var |
Lect.6 |
| 5. |
2/ 24 &
26 |
Optimal Interpolation (OI) & Topics in 3D Data Assimilation |
Lect.9 |
| 6. |
3/ 3 & 5 |
Project 1 Presentation, Observability, & Extended Kalman Fiter (EKF) |
Lect.11 |
| 7. |
3/ 11 |
Observability Seminar
& EKF |
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| ... |
... |
... spring break ... |
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| 8. |
3/ 24 & 26 |
Ensemble Prediction & Project 2 Presentation |
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| 9. |
3/ 31 & 4/ 2 |
Singular Vectors & Ensemble Kalman Filter |
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| 10. |
4/ 7 & 9 |
Ensemble Kalman filter & Breeding |
EnKF |
| 11. |
4/ 14 & 16 |
Advanced Schemes of LETKF (*) & Project 3 (Presentation) |
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| 12. |
4/ 21 & 23 |
4D-Var
& Operational Development of the Ensemble Kalman Filter at JMA (**) |
JMA DA System |
| 13. |
4/ 28
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4D-Var & Satellite Data Assimiltion (***) |
4D-Var (revised)
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| 14. |
5/ 5 & 7 |
Project 4 (Presentation) & Validation of Data Assimilation System |
Adjoint Check by Daryl Kleist |
| 15. |
5/ 12 & 14 |
Final presentations (#,##) |
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| 16. |
5/ 18 |
Final Report Due |
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