Announcements of Opportunity
SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program.
Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor. Click here for more tips on finding a mentor.
Announcements for external summer programs are listed here.
New for 2021: Students applying for JPL projects should complete a SURF@JPL application instead of a "regular" SURF application.
Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.
|Project:||Improving greenhouse gas plume detection using imaging spectrometers|
|Disciplines:||Earth Science, Computer Science|
Methane and carbon dioxide are important greenhouse gases, atmospheric concentrations are rising, and the majority of emissions are from anthropogenic source categories with high uncertainties. A number of bottom-up emissions inventories for natural gas production/distribution are underestimated (Kort et al., 2008; Miller et al., 2013; Brandt et al., 2014), indicating methane emissions from this sector are considerably larger than previously thought. Global fossil fuel carbon dioxide (FFCO2) uncertainty is also significant (GCP, 2015), with around 50% of emissions attributed to large, stationary point sources like power plants and industrial facilities. A small percentage of methane and carbon dioxide point sources dominate emissions, so identifying and quantifying emissions from these sources will increase understanding of anthropogenic emissions and offers the potential to mitigating global warming. Further, finding and characterizing methane point sources provides efficient, cost-effective and impactful mitigation opportunities.
We have been using airborne imaging spectrometers like the Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) and the Global Airborne Observatory (GAO) to identify methane sources and estimate emission rates from the energy, waste management, and agricultural sectors (Duren et al. 2019, Thorpe et al. 2020, Cusworth et al. 2020). Our prior work demonstrated rapid automated detection of methane plumes using a Machine Learning (ML) based detection system. However, operational deployment demands comprehensive downstream quality control to ensure accurate automated detections to refine the ML based detection system.
The overall goal for this project is to support improvements to the ML detection system and the student will provide contributions to the following efforts:
Visually inspect images of methane and carbon dioxide concentrations derived from AVIRIS-NG and GAO images captured in recent imaging campaigns to distinguish credible methane plumes from false positives
Evaluate plumes detected by the ML based detection system to improve future performance
Run a convolutional neural network ML model with various subsets of plumes to assess model sensitivity
PBS NewsHour story: https://www.pbs.org/newshour/show/nasa-scientists-track-climate-changing-methane-leaks-from-the-air
California Methane Survey: https://www.nature.com/articles/s41586-019-1720-3
AVIRIS-NG greenhouse gas mapping: https://avirisng.jpl.nasa.gov/greenhouse_gas_mapping.html
|Student Requirements:||Required: Remote sensing; Suggested: Machine learning experience, Python programming|
|Location / Safety:||Project building and/or room locations: . Student will need special safety training: .|
This AO can be done under the following programs:
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