Teaching Computation in Earth Science Laboratories
Jim Boyle, Physics, Meteorology and Physical Oceanography, Western Connecticut State UniversityThe pillars of science are: observation, theory and numerical simulation. These pillars drive understanding and forecasting in the earth sciences, especially meteorology and physical oceanography—the disciplines I teach.
I emphasize to my students that quantitative computation skills are critical for two of these scientific pillars, observation (measurement) and numerical simulation. In observation, computation is crucial to the data reduction process. Computation is the mechanism used to implement simulation models which forecast the future state of the atmosphere, oceans and climate system. Therefore, developing skill in quantitative computation is critically important for them as college students and in their future professional career.
I have developed a sequential set of laboratory courses for teaching MATLAB – a sophomore course in analytical meteorology, a junior course in physical oceanography and a senior course in meteorological instrumentation. This sequence brings students from simple introductory-type assignments to more advanced data analysis, numerical simulation and visualization projects.
I incorporate a variety of different MATLAB-based computation exercises into the laboratory sessions for these courses. My goals for MATLAB are to:
1. teach students a specific set of coding skills and the methods for data processing, analysis and visualization used in the atmospheric and oceanic sciences,
2. introduce students to the overall capabilities of MATLAB and the knowledge embedded in its documentation, for their benefit as potential future scientists.
In addition, I hope to engender in the students a healthy, science-based skepticism in their belief of results from complex models (e.g., numerical forecasts of weather and climate) and complex measurement platforms (e.g., satellite-based remote sensing data products). Students should be aware of inaccuracies and limitations associated with these data sources. To address this issue, I expose students to as much hands-on, instrument-based data collection as possible. Naturally, these data are evaluated using MATLAB. Keep it basic, keep it simple so students have a clear, intuitive understanding of the physical processes involved and can interpret MATLAB results.
A previous essay from a 2018 MATLAB Workshop (Ali Reza Payandeh, LSU) identifies a number of challenges for teaching oceanography, among these:
- there are not enough computational courses in oceanography majors.
- most of oceanography courses do not cover programming and programming is beyond the scope of most of these courses.
- most students have problems with basic graphics and visualizations including two-dimensional and three-dimensional graphing, contouring and movies.
I am encouraged to know we have the same concerns and I am affirmed to know I have been addressing these challenges these last 20 years. Unfortunately, my success has been marginal and sporadic. During and since the COVID pandemic success teaching computation has been elusive altogether. I continue to search for more effective teaching methods.