E-PSCI 236: Environmental Modeling and Data Analysis

The syllabus and detailed lecture schedule is found here.

The lecture notes can be found here: Lecture Notes

Details such as lecture dates are subject to change.

 

Lecture Schedule and Summary:

Part 1a. Linear modeling of environmental systems (“box models”)

(Prof. Wofsy)

Note: There will be a training session in the use of the R programming language.

Jan 30. Introduction to EPS 236 – what we will be doing and why we will be doing it

Linear Modeling Part I: time evolution operator, solutions to the general problem.

Feb 1 and Feb 6. Linear Modeling; part II Analytical properties of linear systems; Markov chain equiv. Mean Age and Age spectrum; time evolution operators, tangent linear approximations for non-linear dynamical systems; application to estimating global fluxes of atmospheric tracers.

Feb 8. Introduction to the 1^st Class Data Project:

A 5-box model of greenhouse gases in the atmosphere (Workshop 1). Application of linear modeling methods, R coding skills development

Part 1b. Statistical inference, regressions, curve fitting, confidence intervals, bootstrapping, MCMC —focus on concepts and advanced applications

Feb 13. Statistical Inference – a close look at the fundamentals from the point of view of atmospheric, marine, and environmental Scienc

Feb 15.  Linear regressions: Fitting a line (curve) to data;

             Correlated parameters, degrees of freedom, overfitting.

Feb 20. Type II regressions, York regressions, Fitexy (Chi-sq fitting)

Feb 22. Confidence intervals, t-tests,

bootstrap error estimates, non-parametric assessment of data

1c. Time series and wavelet methods

Feb 27. Data Filtering; Classifying data smoothing methods

Mar 1. Modeling and analyzing atmospheric time series data (Workshop 2)

Mar 6. Autoregressive data; systems with serial correlation

Mar 8. Filtering and interpolation of data: wavelets and image processing

Mar 13. Filtering and interpolation: Frequency domain, FFT, spectral decomposition

Mar 13, 15. The global Atmospheric Tomography Mission

         Introduction; examples of using our tools

Data Workshop 3 (short): students apply the tools

2^nd Class Data Project: Emissions of CO and other pollutants from Africa

 

Mar 20, 22. Spring Recess

 

Mar 27. Brief introduction to machine learning

 

Mar 29. Workshop on the 2nd class project.

Selection and initiation of individual/group projects.

Apr 3, Apr 5, Apr 10.

Hands-on class projects in groups of 2 or 3, interacting with instructors Wofsy and Zhao;

Lectures may be interspersed to include:

• Introduction to machine learning in R.

Apr 12. Continuity equation, Eulerian and Lagrangian models (introductory for Part 2a, Chemical Transport Models).

Part 2a. Chemical transport models (Prof. Jacob)

Apr 17. Numerical methods for advection 

Apr 19. Numerical solution of chemical mechanisms

2b. Inverse modeling (Prof. Jacob)

Apr 24. Applications of inverse modeling to atmospheric problems, Bayes’ theorem

Apr 26. Vector-matrix tools for inverse modeling

Analytical solution of the inverse problem

May 1. Kalman filtering and 3-DVAR data assimilation

Adjoint methods and 4-DVAR data assimilation

Reading period. Presentations of individual and group projects