Machine learning and satellites produce immediate carbon dioxide flux predictions
Researchers at the Finnish Meteorological Institute investigated a new approach using a random forest, a type of machine learning model, to predict future CO₂ fluxes. The model was trained with data from established CO₂ flux estimates and satellite observations of different greenhouse gases.
The study showed that this model can process extensive datasets quickly and offers a faster, more immediate alternative to traditional methods, providing insights that were previously not available as quickly. This provides a complementary, rapidly available resource for essential data on carbon emissions.
Accurate flux estimates support decision making
Carbon dioxide (CO₂) is a major greenhouse gas that contributes to global warming. Understanding its concentration and movement (fluxes) in the atmosphere is key to tackling climate change. This helps us know how much CO₂ is being absorbed by natural sinks like forest and ocean, and how much is being emitted to the atmosphere. Satellites orbiting the Earth measure concentrations of different gases, like CO₂, and these are crucial to understand global climate patterns.
Accurate and timely flux estimates are important for researchers and policymakers to make informed decisions to address climate change.
Further information:
Postdoctoral researcher Laia Amorós, Finnish Meteorological Institute, laia.amoros@fmi.fi
Scientific article is available in Proceedings of the 2023 conference on Big Data from Space (BiDS’23).
Reference: Amorós L., Hakkarainen J., Lindqvist H.: Towards a machine learning model to predict carbon dioxide fluxes from space. Proc. of the 2023 conference on Big Data from Space (BiDS’23), Publications Office of the European Union, pp. 177--180 (2023).