Time Schedule:

**Dennis L Hartmann
ATM S 552
Seattle Campus**

Review of objective analysis techniques commonly applied to atmospheric problems; examples from the meteorological literature and class projects. Superposed epoch analysis, cross-spectrum analysis, filtering, eigenvector analysis, and optimum interpolation techniques. Offered: W.

**Class description**

Objective analysis is here defined to be the extraction of information from data using objective, as opposed to subjective, methods, most often via computer analysis. The goals of this course are to provide a working knowledge of the basic methods of objective analysis of meteorological, oceanographic, hydrologic or any other field data data, and to provide a critical facility for evaluating published studies utilizing these techniques. The topics concentrate on techniques for extracting information from data directly, such as compositing, time series analysis, singular value decomposition, principal component analysis, and filtering. If time allows, we may touch on topics, such as wavelet analysis or more general statistical modeling procedures that are being used in dynamical systems analysis..

**Student learning goals**

**General method of instruction**

• Class Lectures: About three classes out of four will consist of lecture/discussion led by me. I will provide notes in advance for most of this material.

• Reading assignments: In addition to class notes, students may wish to consult a standard statistics textbook, when appropriate. Mathematical and computer books about the techniques are also available. Additonal references to textbooks and published papers are given in the class notes.

• Class Presentations by Students: We will read and critique papers from the literature that use the techniques we are studying. Discussions will be led by the graduate students, who will take about 20 minutes to introduce the main points of the paper, followed by general discussion.

• Homeworks: There will be approximately weekly homeworks. Many of these will require you to use a workstation running Matlab or some similar canned package.

**Recommended preparation**

While the course is mostly self-contained, it is helpful if students should have some background in statistics and linear algebra.

**Class assignments and grading**

• Reading assignments: In addition to class notes, students may wish to consult a standard statistics textbook, when appropriate. Mathematical and computer books about the techniques are also available. Additonal references to textbooks and published papers are given in the class notes.

• Class Presentations by Students: We will read and critique papers from the literature that use the techniques we are studying. Discussions will be led by the graduate students, who will take about 20 minutes to introduce the main points of the paper, followed by general discussion.

• Homeworks: There will be approximately weekly homeworks. Many of these will require you to use a workstation running Matlab or some similar canned package. The purpose of these exercises is to give the students familiarity with actually using a wide variety of data analysis techniques. This is a hands-on course.

Homeworks 35% Class Presentations/Project 15% Mid-Term 20% Final 30%

Last Update by Dennis L Hartmann

Date: 11/23/2004