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Atmospheric Modeling, Data Assimilation and Predictability by Kalnay, 2003. |
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Dynamic Data Assimilation: A Least Squares Approach (Encyclopedia of Mathematics and its Applications) by John M. Lewis. S. Lakshmivarahan, and Sudarshan Dhall, 2006. |
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Atmospheric Data Analysis (Cambridge Atmospheric and Space Science Series) by Roger Daley, 1993. |
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Data Assimilation: The Ensemble Kalman Filter by Geir Evensen, 2007. |
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Sequential Monte Carlo Methods in Practice by Arnaud Doucet, Nando de Freitas, Neil Gordon, (Eds.) 2001. |
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Stochastic Processes and Filtering Theory by Andrew H. Jazwinski, 1974. |
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Inverse Problem Theory and Methods for Model Parameter Estimation by Albert Tarantola, 2005. |
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NMC Method for Background Covariance Matrix Construction | |
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Parrish, D. F. and J. C. Derber, 1992: The national-meteorological-centers spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763. | |
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Correlation Function for Covariance Matrix | |
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Gaspari, G. and S.E. Cohn, 1999: Construction of correlation functions in two and three dimensions, Quat. J. Roy. Meteor. Soc, 125, 723-757. | |
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Data assimilation diagnostics in observation space | |
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Desrozier, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space, Quat. J. Roy. Meteor. Soc, 131, 3385–3396. | |
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Predictability and Probability Evolution | |
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Epstein, E.S., 1969: Stochastic dynamic prediction, Tellus, 21, 739-759. | |
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Leith, C.E., 1974: Theoretical skilll of Monte Carol Forecasts, Mon. Wea. Rev., cal102, 409-418. | |
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Ehrendorfer, M., 1994: The Liouville equation and its potential usefulness for the prediction of forecast skill, Part I & II, J. Atmos. Sci., 122, 703-713 & 714-728. | |
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Legras, B. and R. Vautard, 1995: A Guide to Liapunov Vectors, ECMWF Seminar Series "Predictability". | |
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Ensemble Kalman Filters | |
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Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic ocean model, JGR Ocean, 97, 17905-17924. | |
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Houtekamer, P.L., H.L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique, Mon. Wea. Rev., 126, 796-811. | |
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Burgers, G., P.J. van Leewen, G. Evensen, 1998: Analysis scheome in the ensemble Kalman filter, Mon. Wea. Rev., 126, 1719-1724. | |
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Bishop, C.H. B. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420–436. | |
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Whitaker, J.S., T.M. Hamill,2002: Ensemble data assimilation without perturbed observations, Mon. Wea. Rev., 130, 1913-1934. | |
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Tippett, M.K., J.L. Anderson, C.H. Bishop, T.M. Hamill, J.S. Whitaker, 2003: Ensemble square-root filters, Mon. Wea. Rev., 131, 1485-1490. | |
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Hunt, B.R., E.J. Kostelich, I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112-126. | |
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Hybdrid Schemes | |
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Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905–2919. | |
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Lorenc, A. 2003: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905–2919. | |
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Buehner, M. 2005: Ensemble-derived stationary and flow dependent background error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 1013–1043. | |
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Wang, X., C. Snyder, and T.M. Hamill. 2007: On the Theoretical Equivalence of Differently Proposed Ensemble–3DVAR Hybrid Analysis Schemes. Mon. Wea. Rev., 135, 222–227. | |
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Particle Filters | |
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TBA |
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Prerequisite: AOSC 614 is preferred but not strictly required. |
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Students are responsible for checking the UMD Honor code. |
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Credits are based on: attendance/participation: 30%; projects/assignment: 50%; & final presentation/report: 20%. |
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Class | PLS 1164 | TuTh 12:30pm-1:45pm |
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Office hour | CSS 3403 | By appointment |
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Lorenz 3 variable model | |||
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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. |
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Lorenz 40 variable model | |||
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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. |
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Lorenz 960 variable model | |||
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Ref: | (i) | Lorenz, Ed 2005: Designing Chaotic Models, JAS, 62, 1574-1587 | |
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Point Vortex Model | |||
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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. |
1. | [Self Practice] | No Due. | Implementation of Optimization Algorithms |
2. | [Self Practice] | No Due. | Construction of Background Covariance Matrix |
3. | [Self Practice] | No Due. | Preconditioning for Optimization (3D-Var) |
4. | [Self Practice] | No Due. | Tangent Linead and Adjoint Models |
4. | [Self Practice] | No Due. | Diagnostics in Observation Space |
Ia. | Project | Feb 04, 5pm. | Model and Language selection |
Ib. | Project | Feb 11, 5pm | Basic framework of Data Assimilation [with Analysis=Forecast] |
II. | Presentation | Mar 03. | 3D Methods: 3D-Var and OI |
Report | Mar 04, 5pm | ||
III. | Presentation | Mar 29 & 31 | Extended Kalman Filter: |
Report | April 01, 5pm | ||
IV. | Presentation | April 12 & 14 | Ensemble Kalman Filter: |
Report | April 15, 5pm | ||
V. | Presentation | May 03 | 4DVar: |
Report | May 06, 5pm | ||
VI. | Presentation | May 10 | Final: |
Report | May 13, 5pm |
Kayo Ide at UMD | AOSC 615 | 2016 Spring |