ATMO 603: Quantitative Methods in Atmospheric Sciences

(Atmospheric Modeling and Data Analysis)

 

Fall 2005

 

 

Instructor

Dr. Fuqing Zhang, Room 1002B, O&M Bldg, 862-1580, email: fzhang@tamu.edu

 

Office Hours: TBA

  

Class Meets

Lectures: MF 10:20-11:10 in Room O&M 1209

Labs: W 10:20-11:10 in Room O&M 1201

 

Course Description: Mathematical and numerical methods applied to ODEÕs, PDEÕs and statistical methods; data analysis and modeling of atmospheric phenomena.

 

Prerequisite:  Concurrent registration in ATMO 601 and CPSC 203 or equivalents.

 

Textbook

Atmospheric Modeling, Data Assimilation and Predictability by Eugenia Kalnay, Cambridge University Press, 2003

 

Grading policy

Grading: Two exams (25% each); Lab assignments (40%); Class performance (10%).

 

Lectures (two 50-min sessions a week)

  1. Preliminary statistics: variance, covariance and correlations; least square approach; etc (1 week)
  2. Time series analysis: Fourier transform and spectral analysis; wavelet analysis (2 weeks)
  3. Introduction to atmospheric models and modeling (6 weeks)
    1. Components of atmospheric models (1 week)
    2. Numerical methods in solving PDEs (2 weeks)
    3. Parameterization of subgrid-scale physical processes (1 weeks)
    4. Numerical weather prediction models (1 week)
    5. Air pollution modeling (1 weeks)
  4. Introduction to data assimilation methods (2 weeks)
    1. Empirical methods: Cressman-type schemes and nudging (1 week)
    2. Variational methods: 3Dvar, PSARS and 4Dvar (1 week)
    3. Sequential methods: Optimum interpolation (OI), Kalman filter (KF) and ensemble-Kaman filter (EnKF) (2 weeks)
  5. Ensemble forecasting and atmospheric predictability (2 weeks)

 

Lab (One 50-min lab session per week)

  1. Introduction to Matlab (1 week)
  2. Variance, covariance and correlations (1 week)
  3. Spectrum analysis (1 week)
  4. Wavelet Analysis (1 week)
  5. Solving PDEs: LorenzÕ63 model (1 week)
  6. Solving ODEs: 1-D advection model (1 weeks)
  7. Solving ODEs: tank model (1 weeks)
  8. Running the next generation weather research and forecast model (WRF, 2 weeks)
  9. Running the air pollution models (EPA MODEL-3, 2 weeks)
  10. Simple data assimilation experiments ( 1 week)

 

 

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