Rob reviewed the Jupyter notebook Python code that created the interactive sealevel visualization tool seen below (using the ri.grd grid file available):



Rob then discussed the student analyses that were created similar to the examples from last (Tuesday's) class. He used them as a launching point for a discussion of the final class projects.

The emphasis on the project would be two-fold: demonstrating competence at creating Python code for an earth science dataset evaluation, and creating a companion report to present and discuss the analysis process, results, and significance.

Suggested steps include:

• Generate Python code to illustrate a temporal-spatial phenomenon in map & graphical form.
• Find and read a geo-referenced database with a time component.
• Plot geo-referenced data on a basemap derived from NetCDF or ASCII table.
• Represent magnitude of the geo-referenced data with color or shape or both.
• Plot the database in graphical form as well.
• Animate the representation.
• Perform some sort of statistical analysis on the data... preferably meaningful.
• Annotate code and provide references to data.
• Write companion report with requisite components:
  • motivation
  • code description (major steps)
  • interpretation of results

Rob answered questions students had regarding the process, sources of data, and grading mechanism.