Contents

Course syllabus

Course syllabus

ATM 433/533: Advanced Geophysical Data Analysis & Visualization

Fall 2022 (Online / asynchronous)

Instructor

Kevin Tyle, ETEC-419 Phone: 518-442-4578

ktyle@albany.edu

Office hours

10:30-11:30 AM Mon. and 2-3 PM Thu. (or by appointment); in-person or via Zoom: 670 330 460 (ask Kevin for the password)

Class webpages

Blackboard (Announcements, Grades, Forum): https://blackboard.albany.edu

Jupyterbook (Course content, assignments): https://www.atmos.albany.edu/facstaff/ktyle/atm533

Jupyterhub servers (UAlbany and DAES computing account required):

Primary Server:

https://turing.atmos.albany.edu:8000

Backup Servers:

  1. https://lore.atmos.albany.edu:8000

  2. https://ash.atmos.albany.edu:8000

Git Classroom: https://github.com/DAES433533

Prerequisites

ATM433: ATM350; ATM533: Check with instructor

Required Textbook: None

Required external account:

GitHub; sign up for a free account here

Credits: 3

Grading: A-E

Objectives

This course prepares students to learn, develop, and refine best practices in geoscience-centric data analysis and visualzation using free- and open-source software written in Python.

Students will develop interactive, shareable software using what is often termed the Python Geoscience Software Ecosystem (PGSE), a continuously evolving set of Python software libraries with particular application to atmospheric and climate science-related data access, analysis, and visualization tasks.

Additionally, students will learn and practice version control using Git and GitHub.

Learning Outcomes

By the end of the class, students will have accomplished the following:

  1. Accessed, analyzed and visualized geoscience datasets using the PGSE demonstrated in class and with online resources such as https://projectpythia.org/

  2. Minimized the need to download copies of datasets from remote servers in favor of using PGSE client-access methods

  3. Developed publication/presentation-ready visualizations, both static and interactive

  4. Learned software-debugging strategies via the use of online resources such as StackOverflow

  5. Contributed example workflows and notebooks to Project Pythia’s Cookbook Gallery

  6. Graduate students only: Created a public repository on GitHub containing code, figures, and documentation of a short but data-intensive research study that could be accessed and reproduced by the community at-large

Course Delivery Plan

With the understanding that this class is intended as an asynchronous, online-based experience, the following content methods will be used, although each week may vary in the use of each method:

  1. Several concise pre-recorded video lectures by the instructor, often involving “live” coding on the department’s Jupyterhub servers

  2. Interactive, self-paced tutorials, such as Project Pythia

  3. Jupyter notebook-served content, using the department’s Jupyterhub servers

  4. Third-party videos, such as the Unidata’s Metpy Monday weekly YouTube video series

5.Readings from published material, such as journal articles or online-accessible textbooks

Course schedule (subject to change)

Week

Period

Topics

Python Libraries

Assignment Milestones

1

Aug. 22-28

Introduction; Python and Jupyter

Jupyterlab

Survey, Set up GitHub account

2

Aug. 29-Sep. 4

Git & Github; Plotting

Matplotlib

3

Sep. 5-11

Git and Github; Mapping

Cartopy

4

Sep. 12-18

Tabular datasets

Pandas

HW1 due

5

Sep. 19-25

Georeferenced tabular datasets

Geopandas

6

Sep. 26-Oct 2

Gridded datasets

Xarray, MetPy

Set up RDA account

7

Oct. 3-9

Cloud-served Gridded datasets

Xarray, MetPy, Zarr

HW2 due

8

Oct. 10-16

Rasters and Shapefiles

Rasterio, Contextily, Ipyleaflet

9

Oct. 17-23

Satellite and Radar datasets

Satpy, Py-Art

Midterm assignment due

10

Oct. 24-30

Cloud-optimized imagery

Intake

11

Oct. 31-Nov. 6

GPU-optimized data analysis/visualization

RAPIDS-AI environment, Datashader

HW 3 due

12

Nov. 7-13

Interactive visualization

Holoviz suite

13

Nov. 14-20

Interactive visualization

Holoviz suite

HW 4 due

14

Nov. 21-27

3D-viz

PyVista

15

Nov.28-Dec. 4

TBD

16

Dec. 5-11

Project presentations

Final Project due

Grading and assessment

  1. Weekly class participation, which may include one or more of the following each week (15%):
    1. Version control and repository syncing, via git and GitHub
    2. Execution / completion of example Jupyter Python notebooks
    3. Participation in weekly class forums (Blackboard)
    4. Participation in surveys
  2. 4 homework assignments (30%)
  3. Mid-semester assignment: Produce a visualization for the DAES Science-on-a-Sphere (25%, teams of two)
  4. Final project: Contribute a reproducible workflow to Project Pythia's Cookbooks repository (30%, teams of two) (Grad students: Create a 15-minute oral presentation to accompany your project)

Homework assignments will be distributed and discussed by noon Eastern time on Mondays and will be due at noon Eastern the following Monday, unless otherwise directed.

Lateness penalties are as follows:

  1. Up to 24 hours late: 10% penalty

  2. 24-48 hours late: 20% penalty

  3. 48-120 hours late: 50% penalty

  4. More than 120 hours late: 100% penalty

Since assignments will typically be submitted electronically, each file will automatically have a timestamp, to avoid any questions of the time that the student completed the homework. The instructor reserves the right to reduce the penalty if the situation warrants.

Communication is key: if you are having difficulty meeting the deadline, do not hesitate to reach out; please avoid waiting until the due date to get in touch with me!

UAlbany policies and procedures

COVID-19 resources for students: The University at Albany is committed to providing an excellent education for every student in an environment that maintains the health, safety and well-being of our entire campus community. For more info, see https://www.albany.edu/information-students

Religious observances: New York State Education Law Section 224-A excuses absences due to religious beliefs. Students must notify the instructors in a timely manner prior to the absence.

Academic grievance policy: Students who seek to challenge an academic grade or evaluation of their work in a course or seminar, or in research or another educational activity may request a review of the evaluation by filing an academic grievance. For more info, see https://www.albany.edu/graduatebulletin/requirements_degree.htm#academic_grievance .

Campus workplace violence prevention policy and program: UAlbany is committed to providing a safe learning and work environment for the University’s community. For more info, see https://www.albany.edu/hr/assets/Campus_Violence_Prevention.pdf

Accommodating Disabilities Policy: Please visit Albany’s Disability Resource Center for more info: https://www.albany.edu/disability/

Standards of academic integrity: Please refer to https://www.albany.edu/graduatebulletin/requirements_degree.htm#standards_integrity

Policy on allegations of unlawful discrimination and sexual harassment: Please refer to https://www.albany.edu/general-counsel/assets/Sexual_Harassment_Policy_and_Procedures_Revised_6-20014.pdf.

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