Computer Methods in Meteorological Forecasting and Analysis
Spring Semester 2016
Description. This course explores the computationally intensive statistical methods used in the development of automated weather analysis and forecasting systems. The focus of the course is on learning to develop and use artificially intelligent automated systems to perform data quality control, quantitative analysis of large meteorological data sets, and weather forecasting.
- Instructor: Dr. Jon Nese, 518 Walker, 863-4076, Twitter: @jmnese
- Office Hours: Mon 330-430PM, Thu 130-230PM, Fri 800-900 AM, and by appt
- Course meets: Wed & Fri 1:25 – 3:20 PM, 126 Walker
- Prerequisites: STAT 301 or STAT 401 or EBF 472
- Teaching Assistant: Qiang Sun. Office: 624 Walker Building
- Office Hours: Tue 900-1000AM, Wed 800-900AM
- Learn computer methods that can be used for statistical analysis and forecasting of weather, climate and other environmental phenomena.
- Learn to apply those methods to develop accurate and robust forecast systems.
By the time this class if over, you should be able to:
- conduct statistical verification of a weather or climate forecast
- demonstrate knowledge of methods for statistical prediction of atmospheric phenomena
- select, implement and evaluate appropriate statistical forecasting methods
- alter forecast metrics to account for various measures of forecast performance
Motivation: Advances in statistical forecasting techniques are making automated prediction systems ever more competitive with the human forecaster, enabled by statistical methods of analysis that are increasingly accessible. These statistical methods, including artificial intelligence systems, can evaluate situations where multiple variables contribute via multiple possible mechanisms. To succeed in this changing environment, the operational meteorologist must become a builder of forecast systems who uses meteorological insight and statistical knowledge to create ever more accurate forecast systems. Meteorology 474 provides hands-on experience building and testing such statistical forecast systems.
Materials: I usually place lecture materials on ANGEL prior to class. These lecture notes and information gleaned from the assignments will be the main resources for this course. Some course materials will be taken from two texts which are on reserve in the EMS library:
- Data Mining: Practical Machine Learning Tools and Techniques, by Witten, Frank & Hall
- Statistical Methods in the Atmospheric Sciences, an Introduction, by Wilks
In addition, a software package called WEKA, available on the department’s computing cluster, will be used. An overview of WEKA can be found at http://www.cs.waikato.ac.nz/~ml/ .
Enrollment policy. Students who do not meet the prerequisites may be dis-enrolled during the first 10-day free add-drop period after being informed in writing by the instructor (see: http://www.psu.edu/dept/oue/aappm/C-5.html). If you have not completed the listed prerequisites, then consult with the instructor. Students who re-enroll after being dis-enrolled according to this policy are in violation of the Student Code of Conduct (http://studentaffairs.psu.edu/conduct/codeofconduct/).
Assessment Tools and Grading. Assignments will focus on projects that illustrate the creation and testing of forecast systems using machine-learning algorithms. Assignments must be submitted ON TIME – there will be a 25% penalty for any assignment handed in late, and no credit will be given for assignments handed in more than 24 hours after the deadline. There will be a midterm (in class, date to be announced) and final exam (scheduled by the university) as well as very short quizzes every other Friday, starting on January 23 (so there will likely be a total of 7 quizzes – there will be no make-up quizzes, but I’ll drop your lowest quiz grade). In addition, participation and attendance will factor into your overall course grade. The weighting of the components of your course grade is as follows:
- Midterm: 20%
- Final Exam: 25%
- Quizzes: 10%
- Assignments: 40%
- Participation / Attendance: 5%
A standard grading scale will apply to the course, shown below:
- A 90-100
- B 80-90
- C 70-80
- D 60-70
- F <60
However, I will use the ‘-‘ and ‘+’ system as well. For example, a grade just below 90 may receive a B+. Also, I may curve the grades, so it is possible that the thresholds will go down (for example, the boundary between and A and a B may be less than 90).
Attendance. This course abides by the Penn State Class Attendance policy given at http://senate.psu.edu/policies/42-00.html#42-27. Informally, I assume you’re in class unless you have a really good excuse to miss, and you’re responsible for all that you miss.
Cancellations and delays. Campus emergencies, including weather delays, are announced on Penn State Live (http://live.psu.edu/) and communicated to cellphones, email, the Penn State Facebook page, and Twitter via PSUTXT (to sign up, please see http://live.psu.edu/psutxt).
Academic integrity. For information about the EMS Integrity Policy, which this course adopts, see: http://www.ems.psu.edu/current_undergrad_students/academics/integrity_policy.
Briefly, students are expected to do their own class projects, and to do the exams on their own. Students may not copy another person's work and present it as their own. Any students who present other people's work as their own and any students providing these answers to others, will receive no credit for the assignment and may fail the course as a whole. Students are allowed to share ideas and discuss the class projects, but each student’s projects must be completed individually.
Accommodations for students with disabilities. The Office of Disability Services (equity.psu.edu/ods/) requests and maintains disability-related documents; certifies eligibility for services, determines academic adjustments, auxiliary aids, and/or services, and develops plans for the provision of academic adjustments, auxiliary aids, and/or services as mandated under Title II of the ADA Amendments Act (ADAAA) of 2008 and Section 504 of the Rehabilitation Act of 1973. A list of these services is at equity.psu.edu/ods/current-students.
Course Outline (modifications possible, as needed):
- Course Syllabus
- Statistics Review
- Components of an Automated Analysis/Forecasting System
- Forecast Verification
- Purpose of Verification
- Statistical Basis of Verification
- Verification Techniques
- Forecast System Robustness
- Decision Trees
- Introduction and Applications
- CART Algorithm for Growing a Decision Tree
- Pruning a Decision Tree to Prevent Overfitting
- Data Quality Control
- Neural Nets
- Introduction and Applications
- Weighting, Objective Functions, and Iterative Improvement
- Training via Back Propagation
- Learning Rate versus Convergence
- Value of Forecasts
- Valuation of Forecasts
- Value of Probability Forecasts