Teaching

Current Courses

Spring 2026: ATOC 4815/5815 - Scientific Programming, Data Analysis and Visualization Laboratory

This course teaches Python programming alongside data analysis skills for atmospheric and oceanic sciences. Students learn to access, analyze, and visualize scientific datasets through hands-on code development. The curriculum covers curve fitting, re-gridding/aggregation of satellite observations and meteorological data for global climatologies, culminating in an independent final project.

Course Level: Undergraduate (4815) / Graduate (5815)

Prerequisites: Prior Python experience or basic programming course (CSCI 1300 equivalent), plus calculus and algebra

Course materials and announcements will be posted on Canvas. For questions about enrollment or course content, please email wchapman@colorado.edu.

Recent Courses

Fall 2025: ATOC 5860 - Objective Data Analysis Laboratory

An advanced graduate course focusing on extracting information from atmospheric and oceanic data through statistical analysis and computer programming. Topics include hypothesis testing, compositing, regression, principal component analysis, time series analysis, filtering, and data assimilation.

Format: Learning-by-doing approach designed for graduate students engaged in ATOC-related research

Prerequisites: ATOC 4810 or 5810; familiarity with linear algebra, basic calculus, GitHub, and Jupyter notebooks

Office Hours

Location: ATOC Building, CU Boulder Schedule: By appointment - please email to schedule

Teaching Philosophy

I believe in hands-on, project-based learning that bridges theoretical understanding with practical applications. My courses emphasize:

  • Reproducible research practices: Version control, documentation, and open science
  • Collaborative problem-solving: Group projects and peer review
  • Real-world applications: Working with actual climate model output and observational datasets
  • Computational literacy: High-performance computing, data management, and visualization

Resources for Students

Computing Resources

  • Access to NCAR’s computing facilities for course projects
  • Introduction to HPC systems and parallel computing
  • Best practices for working with large datasets

Programming and Tools

  • Python scientific stack (NumPy, SciPy, Pandas, Xarray)
  • PyTorch and TensorFlow for deep learning
  • Version control with Git and GitHub
  • Jupyter notebooks for reproducible analysis

Additional Support

Students in my courses and research group have access to mentoring on:

  • Publication and presentation skills
  • Conference participation and networking
  • Career development in atmospheric science and data science
  • Grant writing and proposal development

Past Teaching

Previously at NCAR, I have mentored:

  • ASP Postdoctoral Fellows
  • Graduate student collaborators from multiple institutions
  • Summer interns and visiting scholars

For Current Students

Course materials, assignments, and announcements are posted on Canvas. If you have questions about course content or logistics, please email me at wchapman@colorado.edu.