Department of Meteorology

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COURSE OUTLINE

 

GENERAL INFORMATION

 

 

INSTRUCTOR:
Professor Eugenia Kalnay
CSS 3431 ; (301) 405-5370
Office Hours: Mon-Wed-Fri: 9-11
E_mail
Biopage

COURSE SCHEDULE:
Lectures: TTh 2:00-3:15 Room: CSS 1113

COURSE DESCRIPTION: Solid foundation for atmospheric and oceanic modeling and numerical weather prediction: numerical methods for partial differential equations, an introduction to physical parameterizations, modern data assimilation, and predictability.

COURSE GOALS: This course should provide you with a solid understanding of modern numerical weather forecasting, data assimilation and predictability, concentrating on basic concepts and essential developments.

PREREQUISITES:

  • Meto 610 - Dynamics of Atmosphere and Oceans I or permission of instructor

HOMEWORK AND LABORATORY - There will be 5 homework sets; we will build simple models and test basic concepts on these models. Some COMET modules maybe used, available at: http://www.meted.ucar.edu/nwp/course/index.htm, http://www.meted.ucar.edu/topics_nwp.php

TEXT: Required: Atmospheric Modeling, Data Assimilation and Predictability published by Cambridge U. Press, 2003, (second printing), available in class with author's discount. The students are required to read the sections of the book before the classes, even if the content is not understood!

ADDITIONAL REFERENCES:
Daley, 1990: Atmospheric data analysis (for data assimilation). Outdated but still useful books: Haltiner and Martin (1980), Thompson (1961). New book on computational methods by Dale D Durran (Springer, 1999).

GRADING:
Homework and lab: 30%; mid-term exam: 30%; Final exam: 40%

TOPICAL COVERAGE

 

HISTORICAL OVERVIEW (about 3 lectures)
(Text, Chapter 1)

  1. Introduction
  2. Early developments: V. Bjerknes vision, Richardson's experiment, Charney et al (1950), filtered models.
  3. Data assimilation: Initial conditions for computer forecasts
  4. Operational NWP: Primitive equation models, global and regional models, ensemble forecasting,
  5. Nonhydrostatic mesoscale models
  6. Evolution of forecast skill
  7.  

THE CONTINUOUS EQUATIONS (about 4 lectures)
(Text, Chapter 2)

  1. Governing equations, atmospheric equations of motion in spherical coordinates
  2. Basic wave oscillations in the atmosphere: gravity and sound waves, weather waves
  3. Filtering approximations, quasi-geostrophic, hydrostatic and incompressive filtering of sound and inertia-gravity waves.
  4. Primitive equations and vertical coordinates

DISCRETIZATION OF THE EQUATIONS (about 6 lectures)
(Text, Chapter 3)

  1. Classification of PDEs: initial and boundary value problems
  2.  Initial value problems: numerical solution
  3. Space discretization methods
  4. Boundary value problems
  5. Lateral boundary conditions for regional models

INTRODUCTION TO THE PARAMETERIZATION OF SUBGRID-SCALE PHYSICAL PROCESSES (about 2 lectures)
(Text, Chapter 4)

  1. Reynolds averaging
  2. Overview of model parameterizations

DATA ASSIMILATION (about 6 lectures)
(Text, Chapter 5)

  1. Empirical methods for data assimilation: successive corrections, nudging
  2. Introduction to least squares
  3. Multivariate statistical data assimilation methods: Optimal Interpolation, 3D-Var and  PSAS
  4. Advanced data assimilation methods with evolving forecast error covariance
    • 4D-Var and Kalman Filtering, ensemble Kalman Filtering
  5. Dynamical and physical balance, initialization, digital filters
  6. Quality control of the observations

ATOSPHERIC PREDICTABILITY AND ENSEMBLE FORECASTING (about 5 lectures)
(Text, Chapter 6)

  1. Introduction to atmospheric predictability
  2. Review of concepts on chaos
  3. Tangent linear and adjoint models, Singular vectors and Lyapunov vectors
  4. Ensemble forecasting: early methods
  5. Operational ensemble forecasting methods:
  6. Growth rate of errors and the limit of predictability, mid-latitudes and tropics
  7. Role of oceans and land in monthly, seasonal and interannual predictability
  8. Decadal predictability and climate change