Geography 249 Spatiotemporal
Data Analysis in the Climate Sciences Fall
2008
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Instructor: Professor
John Chiang Email: jchiang [AT]
atmos.berkeley.edu office phone: 642-3900 office: 547 McCone
Hall office hours: 10a-noon, Wednesdays |
Class Location: 55A
McCone (inside the Earth Science and Map Library) Class Time: Tuesdays
1-4 Course control number:
36776 Units: 3 |
!!! FIRST CLASS
MEETING IS ON TUESDAY SEP 2nd Noon at 55A McCone Hall
Acknowledgements. This
seminar is based on a course developed by Prof.
Michael Evans at the University
of Arizona, Tucson (GEOS 597e ÒSpatiotemporal Data Analysis WorkshopÓ). Thanks, Mike!
Class website: http://www.atmos.berkeley.edu/~jchiang/Geog249/geog249.htm
General
description: This course introduces techniques of
spatiotemporal data analysis in the climate sciences, focusing on empirical
orthogonal function (EOF) analysis and its derivatives. These techniques are standard in
climate research, and essential to any embarking in this field. The emphasis of this course is on
'how to' rather than theoretical. The goal is to get you comfortable with
the technique so that you can use it in your research. It includes practical
considerations like knowing your data, physical interpretation of the results,
pitfalls etc. The context will be
climate data analysis, but the technique is readily translatable to other
fields that deal with space-time data.
Prerequisites: I'm assuming you've seen some linear algebra, and that you have
access to MATLAB (I won't provide this for you. TSW provides a student version, here). We'll make heavy use of
MATLAB, so ideally you are comfortable programming on it, or at least be a
quick learner. As part of the course, you will analyze data of research
interest to you, and present it to the class.
Texts: There are no prescribed textbooks. The readings will be drawn from a number of books and
journal articles, and will be provided for you in class (either as PDF or
photocopies). A list of the
references can be found at the bottom of this website.
Outline: In
the first 3 weeks weÕll cover preliminaries, including getting you off the
ground in MATLAB, a review of linear algebra, and also understanding the
origins of a global sea surface temperature (SST) dataset that is the dataset
that many of the examples use. The
next few weeks will then get into EOF analysis – theory and use –
and in the spatial and temporal domains (the latter being called Ôsingular
spectrum analysisÕ or SSA). The final third of the time will be devoted to
reading papers of specific interest, and working on your projects.
Assignments: You
will be expected to carefully read the material and complete the homework
assigned for each week. We will
discuss both the readings and the homework during the weekly meeting time. In the latter half of the course, you
will work on a research project analyzing data of research interest to you, and
presenting the results at the end of the semester.
Course Schedule (color scheme: black is ÔfixedÕ, red is preliminary and subject to change)
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Week (m/d) and Topic |
Prior Reading |
Post-class homework |
Notes and posts |
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1: Class starts next week |
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2: (Tue 9/2) Introduction and logistics. |
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Matlab primers: |
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3: (Tue 9/9) Linear algebra review |
Wilks Ch9 |
Pencil exercises, and matrix manipulations in Matlab |
Netcdf Binaries for Windows (win32) (from http://www.unidata.ucar.edu/software/netcdf/binaries.html) |
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4: (Tue 9/16) Know your data;
covariance estimation |
Its not important to
get the details, but do get a sense of how the SST dataset is constructed. Also look at figure 5 of Woodruff et al 87. |
Covariance calculations: pencil and matlab |
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5: (Tue 9/23) No class – JC
away |
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6: (Tue 9/30) EOF basics |
Wilks Ch 11 p463-481
on PCA (aka EOF) analysis Also: read pp 56-58 of
Strang (Ch1) on roundoff error.
We used the Strang chapter for week 3Õs homework. |
EOF calculations – pencil and MATLAB |
INGRID resources Ingrid
Users Guide – WARNING:
may be a bit out of date. JennieÕs Ingrid
Examples - this is probably
the easiest way to learn Ingrid, through examples EOF example I showed in class. This applied the EOF to the GOSTA SST 12-month climatology. Click on ÔstructuresÕ to see the EOFs, and Ôtime seriesÕ to see the PCs. |
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7: (Tue 10/7) EOF example (space) |
Weare et al 76; and |
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8: (Tue 10/14) EOF: Truncation, sampling properties |
Wilks Ch 11 pp.
481-492 ; and |
Analysis of SST EOFs calculated last time |
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9: (Tue 10/21) Linear Regression |
Regression and Correlation (Hartmann); and short handout on computation forms of regression and correlation. Also, if you need it, a reference on the StudentÕs t-statistic. |
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10: (Tue 10/28) Rotated EOFs |
Wilks pp.492-500
(rotation); and |
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11: (Tue 11/4) Singular spectrum analysis |
Wilks p501-504; and |
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12: (Tue 11/11) No class –
university holiday |
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13: (Tue 11/18) SSA case studies |
Independent project |
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14: (Tue 11/25) Thanksgiving week
– no class |
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Independent project |
I will hold office hours during the usual class time Tue 11/25 1-4pm |
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15: (Tue 12/2) Canonical Correlation
Analysis |
Wilks Ch 12 sections
12.1 and 12.2 (the rest of the chapter is optional) |
Independent project |
IÕll email the reading
to you |
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16: (Tue 12/9) Class presentations: NOTE
special time and venue Time: 1-5pm Venue: 575 McCone |
Each student presents 15 minute presentations, and up to 5 min Q&A |
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NOTE on A/V. I
will provide projector and Mac laptop.
If you use a Mac, you can put your powerpoint slide on a memory
stick. If you use a PC,
recommend either converting your slides to PDF, or bring your own laptop. Order of
presentation: weÕll do it in
alphabetical order, using the first name. So: Alanna (aka Alex)
Spehr Aparna Bamzai Arthur Wiedmer Audric Collingnon Christopher Holleman BREAK Danielle Svehla Kyle Pressel Lauren Goodfriend Richard Wagner Yuwei Liu Eating is allowed, so bring snacks and drinks! |
References
(partial list)
Strang, G: Linear
Algebra and its Applications, 3rd Ed.
Wilks, DS: Statistical
Methods in the Atmospheric Sciences, 2nd Ed.
Dommenget and Latif
(2002), A Cautionary Note on the Interpretation of EOFs, Journal of Climate: Vol. 15, No. 2, pp. 216–225.
Houghton and Tourre
(1992), Characteristics of Low-Frequency Sea Surface Temperature Fluctuations
in the Tropical Atlantic, Journal of Climate: Vol. 5, No. 7, pp. 765–772.
Ghil et al. (2002),
Advanced spectral methods for climatic time series, Reviews of Geophysics, 40,
1 / March 2002
North et al. (1982),
Sampling Errors in the Estimation of Empirical Orthogonal Functions, Monthly
Weather Review: Vol. 110, No. 7,
pp. 699–706.
Overland and
Preisendorfer (1992), A Significance Test for Principal Components Applied to a
Cyclone Climatology, Monthly Weather Review: Vol. 110, No. 1, pp. 1–4.
Quadrelli et al. 2005,
On sampling errors in Empirical Orthogonal Functions, ???
Weare BC et al (1976),
Empirical Orthogonal Analysis of Pacific Sea Surface Temperatures, Journal
of Physical Oceanography: Vol.
6, No. 5, pp. 671–678.
Woodruff et al. (1987),
A Comprehensive Ocean-Atmosphere Data SetBulletin of the American
Meteorological Society: Vol. 68,
No. 10, pp. 1239–1250.
Zhang et al. (1997),
ENSO-like Interdecadal Variability: 1900–93, Journal of Climate: Vol. 10, No. 5, pp. 1004–1020.