Physics 2506, Fall 2015

Atmospheric Data Assimilation
and Retrieval Theory

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Papers

• Hou et al., Variational Continuous Assimilation of TMI and SSMI/I Rain Rate, 2004.

• Innes et al., The MACC reanalysis: an 8 yr data set of atmospheric composition, 2013.

• Kondrashov et al., Data Assimilation for a Coupled Ocean-Atmosphere Model, 2008.

• Ide et al., Unified Notation for Data Assimilation: Operational, Sequential and Variational, 1997.

• Andersson et al., The ECMWF implementation of three-variational data assimilation (3D-var), 1998.

• Cohn et al., Assessing the Effects of Data Selection with the DAO Phyiscal-State Statistical Analysis System, 1998.

• Evensen (1992), Using the extended Kalman filter with a multilayerquasi-geostrophic ocean model

• Gauthier et al. (1993), Assimilation of simulated wind lidar data with a Kalman filter

• Rabier et al. (2000), The ECMWF operational implementation of four-dimensional variational assimilation. I:Experimental results with simplified physics

• Thepaut et al. (1996), Dynamical structure functions in a four-dimensionalvariational assimilation: A case study

• Evensen (2009), The Ensemble Kalman Filter for Combined State and Parameter Estimation

• Greybush et al. (2011), Balance and Ensemble Kalman Filter Localization Techniques
  

• Muller et al. (2003), Ozone profile retrieval from Global Ozone Monitoring Experiment (GOME) data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY))
  

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Projects

• Possible final projects

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Problem Sets

• Problem Set 1: Assigned Oct 18th, due Nov 4th

• Problem Set 2: Assigned Nov 12th, due Nov 20th

• Problem Set 3: Assigned Nov 21st, due Dec 2nd

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Lecture Slides

• Lecture 1: Wednesday, Sept 16th
  

• Lecture 2: Friday, Sept 18th (Polavarapu: Chapters 1 - 2)
  

• Lecture 3: Friday, Sept 25th (Rodgers: Chapters 2.1 - 2.2.2)
  

• Lecture 4: Wednesday, Sept 30th (Rodgers: Chapters 2.3)

• Lecture 5: Wednesday, Oct 7th

• Lecture 6: Wednesday, Oct 14th (Rodgers: Chapters 2.4 - 2.6, 3.2)

• Lecture 7: Wednesday, Oct 21st (Polavarapu: Chapters 5.1 - 5.2)

• Lecture 8: Wednesday, Oct 28th (Rodgers: Chapters 5.1 - 5.4, 5.6)

• Lecture 9: Wednesday, Nov 5th (Rodgers: 5.6; Polavarapu: Chapter 5.3)

• Lecture 10: Wednesday, Nov 11th (Polavarapu: Chapter 6)

• Lecture 11: Wednesday, Nov 18th (Polavarapu: Chapter 7)

• Lecture 12: Wednesday, Nov 25th (Polavarapu: Chapter 7)

• Lecture 13: Wednesday, Dec 2nd (Polavarapu: Chapter 6, Evensen (2009), Graybush Muller et al. (2003), Greybush et al. (2011))
  

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Lecture Notes:

1. Introduction and Review of Probability and Statistics

S. Polavarapu's lecture notes: | Chapter 1  |  Chapter 2 |

2. Introduction to Estimation Theory

               S. Polavarapu's lecture notes: Chapter 5

               Rodgers Chapter 4.1 - 4.2

Sherman Morrison Woodbury formula

3. Three-dimensional Variational Data Assimilation

               S. Polavarapu's lecture notes: Chapter 5

4. The Kalman filter

               S. Polavarapu's lecture notes: Chapter 6

5. Four-dimensional Variational Data Assimilation

               S. Polavarapu's lecture notes: Chapter 7

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