EOS754: Earth Science Data
and Advanced Data Analysis
(Formerly Earth Observing/Remote Sensing Data and Data Systems)
Instructor: Ruixin Yang
Research I (RSCHI) 226, Tel: 703-993-3615, E-mail: ryang@gmu.edu
Time & Place:
Wednesdays, 7:20-10:00pm, Research I
301
Office Hours: Wednesdays, 5:00 pm-7:00 pm or by appointment.
·
Text 1 (recommended): Hans Von Storch, Francis W. Zwiers, 1999: “Statistical
Analysis in Climate Research,” Cambridge
University Press.
Paperback: January 2001. (ISBN:
0521012309)
Hardback: January 1999, (ISBN:
0521450713)
· Text 2 (recommended): William J. Emery and Richard E. Thomson, 1998: “Data analysis Methods in Physical Oceanography” Pergamon, 1998 (ISBN: 0080314341). [2nd Revised Edition (April 1, 2001), Elsevier Health Sciences (ISBN: 0444507574 for paperback and ISBN: 0444507566 for hardcover)].
Class Email List: eos754@yang.gmu.edu
Description: This course covers
how to access and apply Earth sciences data including those from
observations/remote sensing and models for Earth systems science
research and
applications. Major topics of this course include data formats,
analysis and
visualization tools, advanced data analysis methods, and data
applications. The
course material also contains innovative information technology for
Earth
science data information dissemination including data discovery,
access, and
online analysis.
Prerequisite: EOS/CSI 753 or permission of the instructor. To prospective students
Tentative Course Content:
Week 1: Introduction
· Course Requirements
· NASA's Earth Observing Systems (EOS)
Week 2: Theoretical Background
· Satellite Orbit Theory
o
Basics:
o Circular orbits and geostationary orbits
o Concepts of orbit elements, inclinations
o Orbit perturbation and Sun-synchronous orbits
o Space-time samplings
· Radiation Transfer Theory
Week 3: Map Projections
· Basic concepts on distortions, projection planes and projection points
· Classifications
· Earth model and mathematical theory
· Mathematics of specific mappings
· Links
o Live Map
o JazPanel
Week 4: Data Formats
· ASCII
· Binary
· GRIB;
· Assignment #1 given
Week 5: Data Processing Procedures
· Measurements, Nyquist Frequency
· Data Representation
· Multi-variant data presentation
o Parallel Coordinate
o Grand Tour
o CrystalVision
Week 6: Student Presentation of Selected Tools (Midterm)
Week 7: Time Series
· Basic Concepts
· TS Components
· General Decompositions
· STL Decomposition
· Research Topics
· Assignment #1 due
Week 8: Time Series (Cont.)-Integral Transforms
· Fourier Analysis
· Wavelet Analysis
· Assignment #2 given
Week 9: Time Series (Cont.)-Integral Transforms
· Wavelet Analysis (Cont.)
· The 2nd Generation Wavelets
· Research Topics
Week 10: Time Series (Cont.)
· Autocorrelation
· Correlations
· Regression
· Research Topics
· Assignment #2 due
· Assignment #3 given
Week 11: Principal Component Analysis
Week 12: Nonlinear Principal Component Analysis
· Assignment #3 due
· Assignment #4 given
Week 13: Hilbert-Huang Transformations, EMD Software; HOC
Week 14: Introductions on Data Systems
· OPeNDAPS
· SIESIP and GDS
· LAS
· Assignment #4 due
Grading: Homework: 40%; Mid-Term: 20%; Project: 40%
Other Info: Old version of the homework