Project Info
Lossy compression for climate model data
Dorit Hammerling | hammerling@mines.edu
Climate models are becoming ever more powerful due to increases in computational speed and scientific discoveries. The output from these models is huge and data storage abilities can’t keep up and often the output has to be discarded due to limited storage availability. This has led to a de facto bottleneck in further scientific advances to understand our climate, and its future. Lossy compression is one potential way to deal with this issue, but needs to be applied judiciously to avoid any negative consequences on the scientific integrity of the data, and the conclusions drawn from it.
Elucidate the interdisciplinary nature of the project
This project calls for interdisciplinary expertise from a variety of fields such as ethics, cultural studies, engineering education, and STS (science and technology studies). This project is novel as it compares different dimensions (personal, societal, and professional) of students’ ethics education experiences and examines their experiences in specific institutional and educational cultures. Undergraduate researchers will receive interdisciplinary research experience under the mentorship of faculty members from two departments: Humanities, Arts & Social Sciences (HASS) and Engineering, Design and Society (EDS).
More Information
This manuscript gives an overview of the subject of compression in the context of climate data: https://www.geosci-model-dev.net/9/4381/2016/
This is a tech note written by undergraduate students who worked on this project last summer: https://opensky.ucar.edu/islandora/object/technotes%3A565
Grand Engineering Challenge: Engineer the tools of scientific discovery
Student Preparation
Qualifications
Student #1 – Some programming skills and interest to learn more. Interest in statistical modeling and machine learning. Interest in working with large data on super computers.
Time Commitment
40 hours/month
Skills/Techniques Gained
Programming skills.
Skills working with large data on a super computer.
Knowledge about compression methods.
Knowledge about climate models.
Collaboration and team working skills.
Mentoring Plan
I will have a fixed meeting with them once a week, and be available on email to meet more often if needed.