NOTE: The September 5th class will be held at the Archie K. Davis Conference Center (RTP Headquarters, 12 Davis Dr. RTP, NC)
Tuesdays 4:30 pm-7:00pm at SAMSI, Research Triangle Park, NC, beginning Tuesday, August 29, 2017
No class during week of Thanksgiving (November 21, 2017)
Last class: Tuesday, November 28, 2017
Final Presentation: December 5, 2017
NOTE: Below are listed tentative dates of classes and instructors for this course.
- Lead instructor is Richard Smith, Dept. of Statistics and Operations Research (STOR), UNC-Chapel Hill; email: [email protected]
- Additional Instructors are: Brian Reich, Dept. of Statistics, N.C. State; email: [email protected] and Doug Nychka, National Center for Atmospheric Research (NCAR); email at: [email protected]
Course Outline: This course is being offered in conjunction with the SAMSI year-long research program on Mathematical and Statistical Methods for Climate and the Earth System. The course will cover statistical and computational methods for the analysis of data arising in climate research. Specific topics will include:
- Time series methods and assessments of trends in climatological data
- Analysis of large spatial datasets in climate research
- Methods based on Empirical Orthogonal Functions
- Climate Informatics: the application of machine learning methods and high-performance computing in climate research
- Statistics for climate extremes
- Climate and Health
Prerequisites: The course is intended for graduate students (MS and PhD) in Statistics, Mathematics or other mathematically related disciplines. Students in fields such as Environmental Sciences or Geography are welcome to attend provided they have exposure to Mathematics and Statistics courses at an appropriate level, such as a standard undergraduate calculus sequence, a course in linear algebra, and a graduate-level course in statistical topics such as Linear Models or Regression Analysis. Additional courses in topics such as time series or spatial statistics will be very helpful but are not required. Apart from the statistical prerequisites, participants are expected to have sufficient computing expertise to allow them to run statistical analyses in R. In cases of doubt, feel free to contact one of the instructors.
Assessment: Each student will be expected to conduct a small project applying the techniques of the course to some relevant dataset developed in consultation with the instructors. Depending on the number of students in the course, students may be grouped into teams of two or three students. Each student or team will present its findings at the final class session on Tuesday, December 5, and should also prepare a short written report of its findings.
Grading: Final grades will be based primarily on course projects. There will be no regular homeworks or exams.
Registration: (processed through the respective university)
- UNC-CH: STOR 893 and MATH 793
- Duke: STA 790.01
- NCSU: MA 810.001 and ST 810.001
Questions: email [email protected]