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Home > Newsevents > Training > Lecture_notes >DA  
   

Data Assimilation and the Use of Satellite Data

 
 

 

Click on the titles below to access pdf files of the lecture notes

 

"Introduction to microwave radiative transfer" by Peter Bauer
        1. Radiative transfer
        2. Radiative transfer models
        3. Atmospheric absorption
        4. Surface emission / reflection
        5. Hydrometeors
        6. Fast models
        7. Outstanding issues
        References

"Microwave radiative transfer modeling in clouds and precipitation. Part I Model description" by Peter Bauer
        1. Introduction
        2. Microwave radiative transfer
        3. Radiative transfer models
        4. Optical properties
        References

"Microwave radiative transfer modeling in clouds and precipitation. Part II Model evaluation" by Emmanuel Moreau, Peter Bauer and Frédéric Chevallier
        1. Introduction
        2. Edington vs Doubling-Adding model
        3. Comparison with other models
        References

"A data assimilation tutorial based on the Lorenz-95 system" by Martin Leutbecher
        1. Introduction
        2. Data assimilation experiments
        3. Outlook
        References
        (Click here to download programs relating to the tutorial)

"Adaptive observations, the Hessian matrix and singular vectors" by Martin Leutbecher
        1. Introduction
        2. Adaptive observations in the Lorenz 95 system - Methodology
        3. Adaptive observations in the Lorenz 95 system - Results
        4. Reduced rank prediction of forecast error variance reductions in an operational NWP
            context
        5. Discussion
        6. Conclusions
        References

"Assimilation algorithms" by Elias Valur Holm
  1. Basic concepts
  2. Variational data assimilation
  3. Common assimilation algorithms
  4. References

"Assimilation techniques: 3dVar" by Mike Fisher
        1. Introduction
        2. The incremental method
        3. The background cost function
        References

"Assimilation techniques: 4dVar" by Mike Fisher
        1. Introduction
        2. Comparison betwee the ECMWF 3dVar and 4dVar systems
        3. The current operational configuration of 4dVar
        4. Increments from a single observation
        5. A cautionary example
        References

"Assimilation techniques: Approximate Kalman filters and singular vectors" by Mike Fisher
        1. Introduction
        2. Why is the Kalman filter impractical for very large systems?
        3. The ensemble Kalman filter
        4. Subspaces, projections and Hessian singular vectors
        5. The ECMWF reduced-rank Kalman filter
        6. Examples
        References

"Data assimilation concepts and methods" by F. Bouttier and P. Courtier
        1. Basic concepts of data assimilation
        2. The state vector, control space and observations
        3. The modelling of errors
        4. Statistical interpolation with least-squares estimation
        5. A simple scalar illustration of least-squares estimation
        6. Models of error covariances
        7. Optimal interpolation (OI) analysis
        8. Three-dimensional variational analysis (3D-Var)
        9. 1D-Var and other variational analysis systems
        10. Four-dimensional variational assimilation (4D-Var)
        11. Estimating the quality of the analyses
        12. Implementation techniques
        13. Dual formulation of 3D/4D-Var (PSAS)
        14. The extended Kalman filter (EKF)
        15. Conclusion
        Appendix A. A primer on linear algebra
        Appendix B. Practical adjoint coding
        Appendix C. Exercises
        Appendix D. Main symbols
        References

"Observation Impact on the Short Range Forecast" by C. Cardinali
        1. Introduction
        2. Observational Impact on the Forecast
        3. Results
        4. Conclusion
        References

"Observation influence diagnostic of a data assimilation system" by C. Cardinali
        1. Introduction
        2. Classical Statistical Definitions of Influence Matrix and Self-Sensitivity
        3. Observational Influence and Self-Sensitivity for a DA Scheme
        4. Results 
        5. Conclusions
        Appendix:
        A. Influence Matrix Calculation in Weighted Regression Data Assim. Scheme
        B. Approximate calculation of self-sensitivity in a large variational analysis
system
        References

"Observations and diagnostic tools for data assimilation" by H. Järvinen
        1. Observational preprocessing
        2. The observation screening
        3. Use of feedback information
        4. Diagnostic tools for an assimilation system
        References

"The control of gravity waves in data assimilation" by A. Simmons
        1. Introduction
        2. Non-linear normal-mode initialization
        3. Control of gravity waves in the ECMWF variational data asimilation
        4. Digital filtering
        Appendix A. Definition of operators for the ECMWF vertical finite-diference scheme
        Appendix B. The Lamb wave
        Appendix C. The non-recursive implementation of the recursive filter
        References

"Principles of remote sensing of atmospheric parameters from space" by R. Rizzi (revised by R. Saunders)
        1. Introduction
        2. Absorption and transmission of monochromatic radiation
        3. Black-body radiation
        4. Emissivity, Kirchoff law and local thermodynamical equilibrium
        5. The equation for radiative transfer
        6. Spectral distribution of radiance leaving the atmosphere
        7. Modelling the interaction
        8. Line shapes and the absorption coefficient
        9. Continuum absorption
        10. Integration over frequency
        11. The direct problem
        References

"Inversion methods for satellite sounding data" by J. Eyre
        1. Basic ideas
        2. Temperature profile inversion methods
        3. Constituent profile inversion
        4. Clouds
        5. Satellite sounding in numerical weather prediction
        References

 


 

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