ATSC 301#

Official course description#

Atmospheric Radiation and Remote Sensing: Energy transfer at infrared, visible, and microwave wavelengths, measurements of atmosphere and surface from satellite. Application of techniques from linear algebra and physics to atmospheric problems.

Links: >Home, >UBC Calendar, >ubcexplore, > EOAS curriculum map.

OCESE project objectives#

Apply lessons learned prior to start of OCESE by the instructor Prof. Phil Austin, who is also principle developer for OCESE.

Actions taken#

All resources and teaching tactics were introduced by Prof. Austin prior to the OCESE project as his “normal” approach to teaching this course and preparing its materials. These practices have served as precedent for actions and decisions throughout the OCESE project. All resources are open education resources (OER) unless indicated otherwise.

  • Python programming and Jupyter Notebooks are integral to learning throughout.

  • Principle resources include Jupyter books about Python, programming and course content. Some are used or adapted from open sources, others built by the instructor.

  • Students gain initial experiences with opensource version control practices (GitHub).

  • Questions or question sets are managed via the Canavas API.

  • The challenges related to having students use opensource - and sometimes very large - datasets (eg satellite imagery) are being explored.

Costs#

Running this course assumes the instructor has significant experience with Python, Jupyter notebooks, Jupyter Books and GitHub practices.

The OCESE project did not explicitly improve aspects of ATSC301 but it has served an important role as precedent for many resources and teaching or learning tactics introduced in other courses.

The “costs” have mostly been instructor time to maintain the existing resources and learning activities, and to document them sufficiently to transfer the course to a new instructor in the future. Some hired student time has been spent exploring ways of managing Canvas questions and question banks from Markdown files, via the Canvas API.

A custom Jupyter hub is still (2023) required because “standard” hubs provided at open.jupyter.ubc are not flexible enough to define and update environments regularly during the course, and they are unable to handle large remote sensing datasets reqauired by course.

Lessons Learned#

While the course is about remote sensing, a significant expectation is that students will develop their programing skills. Therefore, it is important to including explicit learning goals associated with the practicalities of programing and working in the open source domain.

Optimal solutions are still being established for a Jupyter hub that can efficiently sustain the requirements for flexibility, and manage access to large data sets.