HDR Masterclass: Spatiotemporal data analysis workshop, 28 June - 21 July 2016
last updated July 22, 2016 (check back for updates). 

Logistics Overview
Syllabus/Schedule Structure
Help
Feedback
Acknowledgements

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svd [ KAPLAN EXTENDED v2 ssta ] structures 1.0

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svd [ KAPLAN EXTENDED v2 ssta ] time series 1.0

Logistics

Meeting time and place: Tu/Th, 9:00a-12:30 pm, 28 June - July 26, 2016
Location: STF 43.G06.
Instructors: Michael N. Evans (University of Maryland, College Park, USA) and Helen V. McGregor, School of Earth & Environmental Sciences, SMAH, UOW.  
Tutor:
Ben Marwick (SEES, SMAH, UOW).
Office hours and location: 1:30-2:30p Tues/Thurs.

Reading and Course Materials:  Readings from selected topical, authoritative and/or timely sources will be linked in the Schedule/Syllabus below as password-protected Portable Document Format (pdf) files.  Lecture (preview) notes (may/may not include work and exercises performed in class) will be linked as html files and also as downloadable Microsoft PowerPoint-compatible files or pdfs.  They may, but not guaranteeably so, be available prior to class meetings, and are subject to change and correction.  We will make use of copyrighted materials under academic Fair Use Policy of applicable copyright law.

Assignments: weekly Readings, pre-session assignment (Prework), and problem sets to be started in class and completed as homework (Classwork).  Written and oral presentation of a final project applying the concepts and tools studied in this workshop is required.  Look for Reading and Prework to be posted by Thursday 5pm the week before it is discussed; Classwork to be posted the day we attack it in class.  If you need to work ahead of time due to prior commitments, please let me know well ahead of time so I can try to accommodate you.

Disability resources:  Do you have a physical or learning disability?   Please speak with us right away about accommodations so we can make arrangements.

Overview

This course will provide practical experience in the fundamental concepts and coding of two common tools used to identify the principal features in noisy spatiotemporal datasets: empirical orthogonal function analysis and singular spectrum analysis.  We will apply these tools to the analysis and interpretation of historical climate data sets.  In parallel we will critically evaluate similar analyses published in the climate dynamics literature.

By the conclusion of this course, you should be able to demonstrate the following skills in pursuit of the following goals, via the following assignments:

Goals

Skills

Assignments

Understand the strengths and weaknesses of tools which use empirically-derived basis functions.

Develop basic concepts in elementary linear/matrix algebra at the heart of empirical analysis techniques, and code scripts to perform these analyses in a high-level scripting language.

Prework, Classwork/Homework

Analyze and interpret the principal features resolvable in climate data (x,y,t)

Perform such analyses on historical gridded climate datasets.

Homework

Critically assess similar analyses in the meteorology and/or climate dynamics literature.

Practice elements of the peer-review process.

Prework, Discussion


Acknowledgements

Help

R:

Matrix algebra, Linear algebra:

Know a better source of help?  Let me know and I'll post it.  Thanks in advance.

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Course structure

Each week's work will consist of the following cycle: 

  1. Prework: summarize/evaluate assigned reading, to be completed prior to the weekly meeting and handed in during class following discussion.
  2. Discussion: main points and open questions from prior homework and present prework, during the weekly meeting.
  3. Preview: occasional short weekly lecture on fundamental principle(s) for the week.
  4. Homework: analytical (paper & pencil) and computational (simple, generalized) problem sets, analysis/interpretation of classwork results, to be begun during the weekly meeting, and finished outside of class prior to next class meeting.
You are strongly encouraged to complete and submit assignments in time for discussion and to keep up with the pace of the course, as s/he who does the work will do the learning.  I would like to (with your permission) anonymously post exemplary assignment solutions to the course webpages, to give you examples of the kind of work I expect.

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Feedback

This is a new course adapted for a short workshop format at UOW for the first time; there are likely to be many "bugs".  Your patience may be required often.  We will be looking for feedback from you as we go as to how to best tailor this course to your needs.  Consequently we may have pre- and post-course evaluations; informal, anonymous, occasional 1-minute teaching evaluations and end-of-course evaluations.  Your candid and constructive observations will be appreciated. 

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Schedule/Syllabus (subject to change)
Note: Scanned readings are password protected to meet copyright fair use guidelines.  If you need the userid and password, email the instructor.

Meeting
Topic/Preview
Reading
Prework
Homework
June 28
Introduction (the problem); the art of programming; introduction to the R/Rstudio environment.  Slides drawn during class are here.
Bottomley et al (1990); elements of coding style (with apologies to computer scientists everywhere); R Manual (for reference)
pw1
hw1.  Ben's code to get started with questions 1-4 is here.  Output plot is here.
June 30
Basic matrix and linear algebra in R. 
Slides drawn during class are here.
Elsner and Tsonis (1996), Ch1, pp 1-19; R Manual (for reference) pw2
hw2.  Paper and pencil exercise notes are here.  My hw2 solutions are here
July 5
EOF analysis (space).  Slides drawn during class are (revised 11/7/16) here.
Peixoto and Oort (1992), Appendix B
pw3
hw3. Paper and pencil exercise notes are (revised 11/7/16) here.  My hw3 solutions are here.
July 7
EOF analysis (space), continued.  Slides drawn during class are here. 
Weare et al (1976)  pw4
hw4.  Our hw4 solutions are here.  My hw4work.RData is here.
July 12
Significance testing.  My notes on Overland and Preisendorfer (1982) are  here. Overland and Preisendorfer (1982)

pw5
hw5.  My hw5 code is here.  RuleN output Rdata file is here.  My hw5work.RDdata is here.
July 14
Pattern robustness. 
Slides drawn during class are here. 
 no reading assignment no pw6
hw6.  My hw6 code is here. My hw6work.Rdata is here.
July 19
EOF analysis (time); sensitivity to parameters.  Slides drawn during class are here.
PW7: Vautard and Ghil (1991) (522 kb pdf)
HW7: Elsner and Tsonis (1991) (297kb pdf)
pw7
hw7.  My hw7 code is here.  My hw7work.Rdata is here.
July 21
Review and Summary; Go fishing.  Slides drawn during class are here.
individual meetings: advising on developing your own independent data analyses pw8


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