Syllabus

Lecture notes and Reference Papers on Objective Analyses

Lecture #1

Lecture #2

Lecture #3

Lecture #4

Review of Probability

Lecture #5

Daley Book Appendex D and E

Computer Program #1 and Input data and Map Projections. Revised assignment with updated grid parameters

Lecture #6

Panofsky (1949) Gilchrist and Cressman (1954)
Bergthorsson and Doos (1954) Cressman (1959)

Lecture #7

Lecture #8

Lecture #9, #10

Barnes (1964)

Koch et al. (1983)

Lecture #11

Lecture #12-13

Lecture #14

Knowledge Expectation for Exam 1

Computer Problem #2. With revised grid parameters

• Introduction. Basic Concepts and Mathematical Tools
• Additional materials on matrix algebra
• Appendices from Book "Dynamic Data Assimilation"

Computer Problem #3

Optimal Interpolation (OI)  Part I    Part II

ECMWF Lecture Notes on Data Assimilation
Lorenc, A. C., 1986: Quart. J. Roy. Meteor. Soc.
Ide et al (1997) on Unified Notations for Data Assimilation.
Book section on Random Variables

• Three-Dimensional Variational Analysis and Data Assimilation (3DVAR)

Huang (2000) MWR paper on various 3D analysis methods and corresponding analysis package. Download the package, examine the source code and run the analysis experiments with included data. Plotting program is also included. A user documentation file, analab_docs.pdf is found in the package.

3DVAR Assimilation of GPS Data

• Liu and Xue (2006)  and Liu et al (2007) on the use of anisotropic filter to model flow-dependent B.
• Paper Outline  of Liu and Xue (2006) and Presentation and Seminar Talk on 3DVAR analysis of simulated GPS slant-path water vapor observations using explicit and implicit filters.
• Gao et al (1999) 3DVAR analysis for dual Doppler radar and Hu et al. (2006) that examines the behavior of divergence constraint when analyzing wind from radial velocity data (section 2c).

Optimiation Methods

Homework assignment No. 4. The ANALAB package to be used, including the input data, is here.

Knowledge Expectations for Exam 2

• Four-Dimensional Variational Data Assimilation (4DVAR)

Recipes for Adjoint Code Construction (Giering and Kaminski 1998).
Errico, R. M., 1997: What is an adjoint model? BAMS

• Kalman Filter and Ensmble Kalman Filter
  
  • Lecture Notes
  • Hamill Review on Ensemble Data Assimilation
  • Houtekamer and Zhang (MWR 2016) Review on EnKF
  • Evansen (1994) - The original EnKF paper
  • Evensen (2003) Review Article
  • Burgers et al. (1998) on the need for perturbing observations in deterministic EnKF
  • Hamill (2000) NWP Conference paper on EnKF
  • Whitaker and Hamil (2002 MWR) on EnKF without obs perturbations - EnSRF.
  • Hamill et al (MWR 2001) on distance-dependent filtering of background error covariance, i.e., spatial covariance localization.
  • Anderson (2001) on need for covariance inflation and Ensemble Adjustment Filter (EAKF)
  • Hunt et al. (2007) - Local Ensemble Transform Kalman filter (LETKF).
  • Tippett et al (MWR 2003) on EnSRFs
  • Tong and Xue EnKF Workshop presentation on radar data assimilation with EnKF and MWR paper .
  
  
• Hybrid Ensemble-Variational Data Assimilation
• Knowledge Expectations for Third Exam