Optimizing Global Observations of Carbon Dioxide in the Surface Ocean Using Machine Learning
This work will assess how we can optimize observing resources from the global fleet to support improved, efficient, and cost-effective monitoring of the ocean carbon sink and minimize uncertainty. Researchers will use machine learning to determine how to best deploy observing assets like buoys, autonomous vehicles, and ships to measure the ocean’s uptake of carbon dioxide globally.