teaching

METR4603/5603: Advanced Observations for Lower Atmospheric Research

Fall 2024 University of Oklahoma, School of Meteorology This content reflects the 2024 offering of the course.

Instructor Team

Meeting Time and Location

• Format: In-person
• Location: NWC 5302 - Measurements Lab
• Time: MWF 2:30–3:20 pm

Land Acknowledgement Statement

Long before the University of Oklahoma was established, the land on which the University now resides was the traditional home of the “Hasinais” Caddo Nation and “Kirikirʔi:s” Wichita & Affiliated Tribes.

We acknowledge this territory once also served as a hunting ground, trade exchange point, and migration route for the Apache, Comanche, Kiowa and Osage nations.

Today, 39 tribal nations dwell in the state of Oklahoma as a result of settler and colonial policies that were designed to assimilate Native people.

The University of Oklahoma recognizes the historical connection our university has with its indigenous community. We acknowledge, honor and respect the diverse Indigenous peoples connected to this land. We fully recognize, support and advocate for the sovereign rights of all of Oklahoma’s 39 tribal nations. This acknowledgement is aligned with our university’s core value of creating a diverse and inclusive community. It is an institutional responsibility to recognize and acknowledge the people, culture and history that make up our entire OU Community.

Belonging Statement

Why You Belong at the University of Oklahoma: The University of Oklahoma fosters an inclusive culture of respect and civility, belonging, and access, which are essential to our collective pursuit of excellence and our determination to change lives. The unique talents, perspectives, and experiences of our community enrich the learning, and working environment at OU, inspiring us to harness our innovation, creativity, and collaboration for the advancement of people everywhere.

Course Prerequisites

• METR4603: METR2213, METR2613, or instructor permission.
• METR5603: METR5004 (concurrent enrollment accepted) or instructor permission.

Course Description

Building upon foundational lessons from METR2613 (or similar), this course examines the observation and operation principles behind various research-grade instruments. Students learn to analyze the data they provide through a mix of group instruction, guest lectures, instrument demonstrations, and data-focused projects. The focus is on modern, state-of-the-art instruments applied to current research problems, emphasizing lower-atmospheric observations. Students will use Python for processing, analysis, and visualization of real observed datasets. 🐍

Course Goals & Outcomes

Goals: Students will become familiar with several research-grade observation platforms and gain experience synthesizing observations to address research problems. They will also gain transferable skills in interrogating and quality-assuring observed datasets, preparing them for research or data analysis careers using modern meteorological observations.

Outcomes: Upon completion, students will be able to:

• Describe the benefits and limitations of various observation platforms.
• Interpret, quality-assure, and extract relevant information from observed data.
• Develop and modify code (Python) to visualize observed data.
• Analyze and interpret data to provide insight on atmospheric processes.
• Write a journal-article-style research paper outlining a research question, methods, and results.

Text and Materials

• Readings (including scientific literature) will be provided as needed.
• Python: Students should install the free Anaconda distribution or ensure access via SoM Student Lab computers. 💻

Teaching Philosophy

This course is team-taught by experienced observationalists aiming to expose students to modern research, literature, and instruments. We focus on building a foundational understanding of observation principles transferable to other platforms. We encourage an interactive environment with questions and discussions. Sessions will mix traditional lectures with hands-on activities like instrument demos. Assignments provide practical experience with real atmospheric data. We support students with diverse backgrounds, especially in Python coding, aiming to develop skills from their current level.

Expectations

Attendance & Preparation: Attend all sessions and complete readings beforehand. Seeking Help: Seek assistance on assignments at least 2 days before the due date. Coding help sessions/office hours will be available. Collaboration & Integrity: Work together, but submit individual, original work (including code). Adhere to OU’s academic integrity policies. Communication: Instructors and students should communicate clearly and respond within 72 working hours. Inform instructors about known absences or accommodation needs promptly.

Learning Activities, Assignments, and Assessment

No Exams: Focus is on data-driven homework and research projects. Homework (4 total): Involve data quality assurance, analysis, and visualization using Python. Reports (potentially using LaTeX) and code required. ~2 weeks per assignment. Class Projects: Students (groups for undergrads, potentially independent or leading groups for grads) identify a research focus, find appropriate datasets (options provided or sourced independently), and develop a research question.

• Proposal + Literature Review: Concise (~5 pages + timeline/references) outlining goals, background, methods.
• Mid-point Progress Report: Formal report for feedback and planning.
• Final Project Materials: Journal-style paper (up to 12 pages) and a presentation (conference talk, lightning talk, or poster session). 📄🎤

Assigning Grades

This course uses an ungrading approach, focusing on learning experiences rather than arbitrary metrics. Assessment is tailored to different activities, giving students agency. Success requires students’ honest reflection on their learning goals and progress. More information about grading is included below. (Self-assessment and feedback play a key role)

Topics Covered

• Overview of BL Processes
• BL Thermodynamics
• Basics of Turbulence
• Turbulence in the Atmosphere
• Sonic Anemometry, Observing Turbulence, and Sampling Frequency
• Turbulence Quantities
• Observations of the Radiative Budget Near the Surface
• DOE/ARM Resources
• Instrumentation Concepts
• Surface Energy Budget
• Energy Budget Closure
• UAS General
• UAS Atmospheric Methods
• Today’s UAS Research
• Kinematic Profilers
• Thermodynamic Profilers
• Thermodynamic Retrievals
• Other Retrievals and Algorithms
• Lower Atmosphere Applications of Radar

Support for Learning

OU Resources: NWC Library, Disability Resource Center (DRC), OU Libraries Software/Data Carpentry, Writing Center. Accessibility: Communicate any needs for making instrument visits outside the classroom more accessible. Optional Sessions: Topics like coding, writing, presenting may be offered. External: UCAR MetEd Comet Program modules (search “measurement”).

Policies

Attendance, Late, and Make-up: Attendance expected; communicate known absences beforehand. Late work generally not accepted; make-ups only in special circumstances.

Civility: Mutual respect is required. Disruptive behavior (phones, lateness, unrelated work, sleeping, belittling others) is not acceptable.

Academic Misconduct: OU’s Academic Misconduct Code applies. Plagiarism, fabrication, etc., will be investigated. Ask instructors if unsure about collaboration boundaries.

Reasonable Accommodation: Register with the Disability Resource Center (DRC) (730 College Ave, 405-325-3825, adrc@ou.edu) and discuss needs with instructors early.

Pregnancy/Childbirth: Contact instructors for necessary modifications. See OU EOO FAQs.

Title IX: Report gender-based discrimination, sexual misconduct, etc., to the Sexual Misconduct Office (405-325-2215) or SART (405-615-0013, 24/7).

Mental Health: Contact the University Counseling Center (UCC) (Goddard 2nd floor, 405-325-2911) or use TELUS Health (24/7).

Religious Holidays: Absences excused; rescheduling provided.

Final Exam Prep Period: Policy applies; new material may be covered. See OU Policy.

Emergency Protocols: Follow OU Alerts and building procedures for severe weather (NWC refuge: 1313/1350 auditoriums), armed subjects (Run, Hide, Fight), or fire (evacuate to assembly area north of NWC).

Grading Approach

“Learners need freedom to make mistakes so they can learn from those mistakes, and they should not be punished for making mistakes.” – Chapter ‘Getting Rid of Grades’ by Laura Gibbs in Ungrading

Based on student feedback from offering this course previously, we have evaluated the education goals of the class and determined that traditional grading approaches had negative effects on student outcomes. Instead of placing focus and value on punitive, demerit-based metrics that are not necessarily well applied to all students or all scenarios, we took a step back to reexamine our approach to ‘assessment’ and tailor it toward the experiences, learning opportunities and outcomes, and the skills development we hope to cultivate during our time together.

The way grades are applied in this course will likely be different than many courses you have taken in the past. However, for this grading approach to be successful, we need students to buy in. The approach relies on honest reflections from students on their learning goals and their own progress throughout the course. On this page, we will document the ‘grading’ approach we are applying in this course. With feedback and collaboration from the students in the class, this approach is subject to change throughout the semester. It will not change without notice, and any and all changes will be documented here. Below the documentation, we offer some more discussion of the why and additional resources for the curious among us.

Approach The approach we have opted to use is not perfectly described as ‘ungrading,’ but it falls under that general umbrella. Some other approaches that we borrowed from are ‘contract grading’ and ‘specification grading,’ but like ‘ungrading’ they are not a perfect fit for our work together. As we went through the review and development process for this class, we found that our scientific research class which includes coding and data assignments alongside longer form project writing tasks made elements from each of these approaches both useful and difficult to apply. The result we landed with is closest to ‘specification grading’ with elements from other types of non-standard grading approaches.

In the end, our motivation is to focus on getting the learning and the doing, not to focus on getting the exact ‘right’ answer every time. We aim for this approach to reduce pressure to achieve possibly arbitrary measures of success and instead allow space for taking risk, trying things, and learning in the process.

Single-Point Rubrics The single point rubric is a main feature of this approach. All evaluated assignments are ‘graded’ against a rubric that defines the ‘satisfactory’ or ‘meets expectations’ level of success for the work. This level of success and the criteria required to achieve it describe the criteria for proficiency. No more. No less. This [website] (https://www.cultofpedagogy.com/holistic-analytic-single-point-rubrics/) offers a good example based on rubrics for scoring breakfast in bed. The rubrics you may be more used to seeing are called ‘analytic rubrics’ and are compared in the linked example.

Outcomes Throughout the semester, rubrics for different tasks or assignments will be connected to the course’s learning outcomes. There are numerical values connected to the level of ‘achievement’ within these outcomes. This is the first time we are using this cross-cutting outcome connection approach, so consider it “in beta”. These are not directly related to your ‘grade’ or how well you are doing in the class. These are intended to help show how different components of the work we do connect to the overall course outcome and help identify any patterns for areas where you may excel or may face more friction. These patterns are helpful for you to know where to invest time and attention and helpful for us to know what we are or are not succeeding to cover or convey in our course material.

Assessment The coursework is distributed between homework assignments and the semester-long group project. The project provides an opportunity for students to apply much of the what they are learning in near-real time in a collaborative setting, while the homework assignments are individual assignments establishing the skills and knowledge base we aim to develop. As such, the final outcome of student success is weighted a bit more by their efforts on the homework assignments.

Homeworks There are four homework assignments spaced throughout the semester. In general, each assignment provides about two weeks to complete with one in-class help session offered within that window. This course is adopting a Token Framework for homework.

All students will have two tokens to apply to a homework assignments during the semester. The tokens allow the student a ‘re-do’ on a homework assignment. A token must be exercised in time for the newly completed version of a homework assignment is complete and submitted by the submission deadline of the next homework (or other agreed on date in the case of HW 4). This token framework enables students that perform below their own expectations an opportunity to more effectively learn material from what a traditional grading scheme would otherwise deem a ‘failure’ and earn a positive outcome. Note that repeat homework assignments will be based on the same set of questions or tasks, but may require using a different dataset.

All completed and final homework assignments are assessed with single point rubrics and included together in a cumulative assessment of a Course ‘grade’ according to the criteria listed in the table included in the section below.

Project The project will be graded based on cumulative assessment of all project components. Each component will have a single-point rubric assigned to it. Group collaboration evaluations will be updated alongside each project component, however the final evaluation of group collaboration will address collaboration throughout the entire project. We reserve the right to evaluate different members of the groups differently based on their contributions. Except in the event of non-submission, proposals will not be evaluated below expectations. At the proposal stage, groups may be flagged for ‘below expectations’ status on group collaboration. This outcome requires students to closely evaluate their groups and take action to address the collaboration. Actions can include support from instructors to achieve collaborative group structures. In extreme cases where collaboration is not achievable through actions by group members, steps can be taken toward separating evaluation of group members performing ‘below expectations’ from the overall group assessment.

Reflections and Feedback Throughout the duration of the course, students will be asked to reflect on their progress, goals, and outcomes honestly. This will also serve as a check-in for feedback on the single-point rubric-based evaluations provided in the course and any corrections or adjustments needed on the part of the student or instructor in that process. Guidance will be provided for this exercise ahead of time. Some instances will likely happen asynchronously while others will likely be scheduled for in-person meetings. These are subject to adjustment. Students are also always welcome and encouraged to use office hours/appointments to engage in reflections and feedback. We have also added an anonymous survey where students can share any anxiety or concern you have about the class and grading approach. The preferred route for sharing these is through email and or meetings with instructors, but recognizing that barrier can be high the anonymous survey can be an alternative option.

Course Grade The overall grade in the course combines the assessment of all homework assignments with the final assessment of the project.

Tokens All students have two ‘tokens’ which can be used to repeat any homework assignment that does not meet their own evaluation goals. Tokens must be exercised such that a repeated homework can be completed by the submission date of the next homework (or other agreed upon date in the case of HW4). Repeat homework assignments will be based on the same set of questions or tasks, but may require using a different dataset.

Resources and Background “Research shows three reliable effects when students are graded: They tend to think less deeply, avoid taking risks, and lose interest in learning itself.” - Alfie Kohn, “The Trouble with Rubrics”

A general primer Jesse Stommel writes, speaks, and produces a variety of content on ‘ungrading,’ which you can access online. I recommend starting here.

An example from a Gen Chem course

Feedback Everyone needs feedback in order to learn and grow! _GIVING FEEDBACK_

• giving feedback to your peers about their writing
• giving feedback to your instructors about their teaching
• giving feedback to yourself about your own learning _RECEIVING FEEDBACK_
• evaluating the feedback to discover what is useful
• acting on the feedback to improve your work
• giving feedback to others about their feedback Former OU professor, Laura Gibbs, asked some of her students to help her brainstorm about how to do a better job with the feedback process her classes, and they had a lot of new ideas to try, and lots of good resources to use: Learn about Feedback.

What are grades good for anyway? Most of this is generally paraphrased from Laura Gibbs Chapter in ‘Ungrading’

Although grades are often seen as feedback (though not very effective if learning is the goal), their main role is actually coercion, which contradicts the idea of freedom. Schools use grades to force students to meet certain “expectations”. Grades are a tool of control – even if good intentioned.

Removing grades allows students to explore and find what is truly meaningful to them. Instead of setting fixed learning objectives and measuring all students the same way, each student can set their own goals and classroom efforts can instead focus on supporting their progress. By emphasizing supportive feedback over grades, educators can demonstrate care for their individual learning and help them engage with what matters to them. When feedback is given with a grade, students often see it as just justifying that grade. Without grades, feedback can be appreciated for its own sake and used constructively. Eliminating grades shifts the focus to the value of students’ work and the feedback to improve it.

“Punitive grading” penalizes students for making mistakes rather than using those mistakes as opportunities for growth and encourages students to avoid errors instead of learning from them. Although we might advise students to learn from their mistakes and provide feedback to help with this, grades often tell a different story. They penalize mistakes, causing students to feel regret and focus on past errors. In high-performing groups, an “A” might be the only grade that avoids negative feelings; anything less is viewed as failure. This creates a dangerous perfectionism that can harm all learners, whether they are struggling or performing well. Maybe especially those that are performing well. Taking it a step further, grading encourages students to adopt problematic habits such as focusing only on achieving high grades and doing the minimum required to get an A, rather than seeking deeper understanding. “Is this going to be on the test?” These habits can lead to extremes and uncharacteristic behaviors like a student being motivated to cheat, the dishonest behavior could be a lesser evil compared to receiving a poor grade. Removing grades eliminates this pressure, reducing the motivation for such rationalizations and increasing the space for actual learning and engagement. Experiencing hyper focus on avoiding failure and seeing anxious perfectionism play out in previous course offering motivated our shift in grading approach, specifically toward a proficiency-based specifications approach.

In all, learning isn’t about never making mistakes but about understanding and addressing them. Grades don’t distinguish between issues like a lack of skills or insufficient rest, while feedback supports improvement through revision. Traditional grading often undermines the value of revision, presenting it as punishment for poor performance rather than a valuable learning tool. Without grades, revision can become a normal part of the learning process, focused on continual improvement rather than rewards or penalties. This motivates the TOKEN FRAMEWORK we apply in this class.

Additional Resources As compiled by Jesse Stommel here.

“Grades are Dehumanizing; Upgrading is No Simple Solution.” by Jesse Stommel

“What if We Didn’t Grade? A Bibliography” by Jesse Stommel

“How to Ungrade” by Jesse Stommel

“Care is a Practice; Care is Pedagogical” by Jesse Stommel

Ungrading: Why Rating Students Undermines Learning (and What to Do Instead), Ed. Susan D. Blum

Teaching in Higher Ed Podcast: [How to Ungrade(https://teachinginhighered.com/podcast/how-to-ungrade/)

Designing for Care, Eds. Martha Burtis, Jerod Quinn, and Surita Jhangiani

“Dialectical Notebooks and the Audit of Meaning” by Ann Berthoff (The Journal Book, Chapter 1)

“Grading Student Writing: Making it Simpler, Fairer, Clearer” by Peter Elbow [on minimal grading]

“Ranking, Evaluating, Liking: Sorting Out Three Forms of Judgement” by Peter Elbow

“Using Small Multiples for Keeping Track of Student Work” by M. A. Syverson [on small multiples]

“The Trouble with Rubrics” by Alfie Kohn

“The ultimate goal of authentic assessment must be the elimination of grades. But rubrics actually help to legitimate grades by offering a new way to derive them.”

“The Case Against Grades by Alfie Kohn

“Teaching More by Grading Less (or Differently)” by Jeffrey Schinske and Kimberly Tanner

“(Un)Grading: It Can Be Done in College” by Laura Gibbs

“Because I put myself outside of the grading loop, I can focus all my efforts on feedback and encouragement – on teaching, not grading.” - Laura Gibbs

“A Fine is a Price” – Study about social contracts and policies

“The economics of the classroom -or- Why grades encourage bad habits” by Kris Shaffer

“All Teachers Should Be Trained To Overcome Their Hidden Biases” by Soraya Chemaly

“Pygmalion in the Classroom” by Robert Rosenthal and Lenore Jacobson (1968)

Pygmalion Effect: Higher expectations lead to higher performance. To what extent does bias factor into teacher expectations?

“How to Crowdsource Grading” by Cathy N. Davidson

Cathy N. Davidson writes “There is an extreme mismatch between what we value and how we count.”

Antiracist Writing Assessment Ecologies by Asao Inoue