QUANTITATIVE ANALYSIS IN EARTH SCIENCES (ENNEC 472, 3
credits)
Course Syllabus for Spring 2009
Instructor: Michael E. Mann, Department of Meteorology, 523 Walker
Building, mann@psu.edu
Teaching Assistant: Fangxing Fan, Department of Meteorology, 408 Walker
Building, fxf908@psu.edu
Meeting Time/Place: T R 01:00-2:15 PM (112 BUCKHOUT)
Office Hours:
You are welcome to visit the instructor or TA’s
office for questions during scheduled office hours or by appointment: Wed 1:30-2:45
PM (instructor); Tu/Th
2:30-3:30 PM (TA). You may also email the TA or instructor at the email
addresses indicated above. Responses may be delayed.
Motivation:
Many key questions regarding the behavior of the
atmosphere, ocean and climate, are fundamentally statistical in nature. Is the
character of tropical storms and hurricanes changing with time? Is the warming
of the globe consistent with natural variability or not? What is the influence
of El Nino on global weather patterns?
In this course, we will develop and apply various
tools of data analysis and statistics to addressing these, and other
fundamental questions in the atmospheric and related sciences. We will
emphasize the application of the tools to actual data.
Topics to be covered:
·
Distributions
(applications:
characterizing the occurrence of tropical storms and hurricanes; characterizing
tropical Atlantic sea surface temperature variations; characterizing El Nino
events)
·
Hypothesis
Testing (applications:
Are there distinct regimes of atmospheric circulation behavior during the 20th
century? Of Atlantic Hurricane activity?)
·
Linear
Regression and Trend Analysis (applications: Is the globe warming? Is El Nino
becoming more pronounced over time? Is the intensity of tropical storms and
hurricanes increasing over time?)
·
Multivariate
Regression (applications: What atmospheric and oceanic factors
control variations and trends in tropical storms and hurricane activity?)
·
Time
series methods (applications:
modeling the
behavior of El Nino; modeling the global temperature series)
·
Analyzing
spatial data (applications: determining the spatial pattern of
influence of El Nino and the North Atlantic Oscillation on atmospheric
circulation patterns)
Webpage
We will regularly draw upon the course homepage as a resource for the course:
http://www.meteo.psu.edu/~mann/Mann/courses/ENNEC472SPR09/index.html
Aside from links to the course syllabus, there will
be links to the readings, problem sets, and required MATLAB routines, slides
from the lectures, and other course-related materials.
Lectures
Attendance of all lectures is expected. You are
strongly encouraged to ask questions and participate constructively in class.
Copies of slides from the lectures will usually be made available
electronically through the course website (see above) the morning prior to the
lecture.
Textbook
The course textbook is:
Daniel S. Wilks (2005), Statistical Methods in the Atmospheric Sciences: An Introduction, 2nd Edition, Academic Press, 520pp.
Where appropriate, supplementary readings taken
from various sources will be posted on the course website.
Grading
Problem Sets
(30%): There will be 4 problem sets
assigned that will involve applications of topics covered in class. Your
analyses must be done using MATLAB on whatever platform you choose.
Quizzes (15%): There will be occasional short in-class quizzes to
help insure that you keep up with the course material.
Mid-term Exam
(20%):
Final Exam (35%)
LECTURE SCHEDULE (tentative and subject to change)
#
DATE
TOPIC Reading
(Wilks)/ Notes
1 |
T Jan 13 |
Introduction |
|
2 |
R Jan 15 |
Distributions |
4.1-4.2 |
3 |
T Jan 20 |
Poisson distribution; Gaussian distribution |
4.2-4.4 |
4 |
R Jan 22 |
Gaussian distribution(cont); |
4.2-4.4 |
5 |
T Jan 27 |
Gaussian dist(cont); Other Continuous Distributions |
4.2-4.4 |
|
R Jan 29 |
No class |
|
6 |
T Feb 3 |
Hypothesis Testing: Gaussian distribution |
5.1-5.1.6
Prob Set #1 Due |
7 |
R Feb 5 |
Central Limit Theorem; Maximum Likelihood |
4.4; 4.6.1 |
8 |
T Feb 10 |
Hypothesis Testing: t-test; |
5.2.1-5.2.4
|
9 |
R Feb 12 |
Hypothesis Testing: t-test(cont); F-test |
5.2.1-5.2.4
|
10 |
T Feb 17 |
Chi-squared; Goodness of fit |
5.2.5
|
11 |
R Feb 19 |
Chi-squared; Goodness of fit |
5.2.5
|
|
T Feb 24 |
No class |
|
12 |
R Feb 26 |
Linear Regression |
6.2-6.2.2
Prob Set #2 Due |
13 |
T Mar 3 |
Guest Lecture |
|
14 |
R Mar 5 |
Guest
Lecture |
|
|
T Mar 10 |
No Class
[spring break] |
|
|
R Mar 12 |
No Class
[spring break] |
|
15 |
T Mar 17 |
Mid-term |
|
16 |
R Mar 19 |
Linear Regression; Analysis of Variance (ANOVA) |
6.2.3-6.2.4
|
17 |
T Mar 24 |
Confidence Intervals; Prediction Intervals; Correlation |
6.2.5-6.2.7; 3.5-3.5.5 |
18 |
R Mar 26 |
Analysis of Residuals and Autocorrelation |
|
19 |
T Mar 31 |
Multivariate Regression |
6.2.8; 6.2.9; 9.3.2 |
20 |
R Apr 2 |
Multivariate Regression (cont) |
6.2.8; 6.2.9; 9.3.2 |
21 |
T Apr 7 |
Predictor Selection; Stepwise Multiple Regression |
6.3.3-6.3.4
Prob Set #3 Due |
22 |
R Apr 9 |
Stopping Rules; Cross-Validation |
6.3.5-6.3.6
|
23 |
T Apr 14 |
Principal Components Analysis (PCA) |
9.3-9.3.5 |
24 |
R Apr 16 |
PCA (continued) |
11.1-11.2 |
25 |
T Apr 21 |
Guest
Lecture |
|
26 |
R Apr 23 |
Guest
Lecture |
|
27 |
T Apr 28 |
PCA (continued) |
11.1-11.2 |
28 |
R Apr 30 |
PCA Selection Rules; Preisendorfer Rule N |
11.3
Prob Set #4 Due |