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. 
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. 
NMC Method for Background Covariance Matrix Construction  
Parrish, D. F. and J. C. Derber, 1992: The nationalmeteorologicalcenters spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763.  
Correlation Function for Covariance Matrix  
Gaspari, G. and S.E. Cohn, 1999: Construction of correlation functions in two and three dimensions, Quat. J. Roy. Meteor. Soc, 125, 723757.  
Data assimilation diagnostics in observation space  
Desrozier, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysiserror statistics in observation space, Quat. J. Roy. Meteor. Soc, 131, 3385–3396.  
Predictability and Probability Evolution  
Epstein, E.S., 1969: Stochastic dynamic prediction, Tellus, 21, 739759.  
Leith, C.E., 1974: Theoretical skilll of Monte Carol Forecasts, Mon. Wea. Rev., cal102, 409418.  
Ehrendorfer, M., 1994: The Liouville equation and its potential usefulness for the prediction of forecast skill, Part I & II, J. Atmos. Sci., 122, 703713 & 714728.  
Legras, B. and R. Vautard, 1995: A Guide to Liapunov Vectors, ECMWF Seminar Series "Predictability".  
Ensemble Kalman Filters  
Evensen, G., 1994: Sequential data assimilation with a nonlinear quasigeostrophic ocean model, JGR Ocean, 97, 1790517924.  
Houtekamer, P.L., H.L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique, Mon. Wea. Rev., 126, 796811.  
Burgers, G., P.J. van Leewen, G. Evensen, 1998: Analysis scheome in the ensemble Kalman filter, Mon. Wea. Rev., 126, 17191724.  
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.  
Whitaker, J.S., T.M. Hamill,2002: Ensemble data assimilation without perturbed observations, Mon. Wea. Rev., 130, 19131934.  
Tippett, M.K., J.L. Anderson, C.H. Bishop, T.M. Hamill, J.S. Whitaker, 2003: Ensemble squareroot filters, Mon. Wea. Rev., 131, 14851490.  
Hunt, B.R., E.J. Kostelich, I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112126.  
Hybdrid Schemes  
Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter3D variational analysis scheme. Mon. Wea. Rev., 128, 2905–2919.  
Lorenc, A. 2003: A hybrid ensemble Kalman filter3D variational analysis scheme. Mon. Wea. Rev., 128, 2905–2919.  
Buehner, M. 2005: Ensemblederived stationary and flow dependent background error covariances: Evaluation in a quasioperational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 1013–1043.  
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.  
Particle Filters  
TBA 
Prerequisite: AOSC 614 is preferred but not strictly required. 
Students are responsible for checking the UMD Honor code.  
Credits are based on: attendance/participation: 30%; projects/assignment: 50%; & final presentation/report: 20%. 
Class  PLS 1164  TuTh 12:30pm1:45pm  
Office hour  CSS 3403  By appointment 

Lorenz 3 variable model  
Ref:  (i)  Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141.  
(ii)  Kalnay, E. and coauthors, 2007: 4DVar or Ensemble Kalman filter? Tellus, 59A, 758773. 

Lorenz 40 variable model  
Ref:  (i)  Lorenz, E. N., 1995: Predictability: a problem partly solved. ECMWF proceedings for Seminar on Predictability, 118.  
(ii)  Lorenz, E. N. and K. Emanuel, 1998: Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model, J. Atmos. Sci. 45, 399414. 

Lorenz 960 variable model  
Ref:  (i)  Lorenz, Ed 2005: Designing Chaotic Models, JAS, 62, 15741587  
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, 22472260. 
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 (3DVar) 
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: 3DVar 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 