Changing temperature differences between the poles and the equator, and the rate of the Earth’s spin, create unique atmospheric patterns. These movements help to transfer heat from the equator to the poles thus creating weather. Deep Learning is used to help predict the changes due to many variables that affect climatic patterns. The data to make these predictions comes from Copernicus and ECMWF detailing wind patterns, temperature, air pressure and precipitation over 30+ years. Upcoming climate patterns are predicted based on patterns perceived within the historical data using previously listed data within the artificial intelligence simulation. Using data analysis techniques, as well as, machine learning, the movement of atmospheric systems can be predicted to explain climate over long periods of time. This study will work to explain how the climate patterns are changing and evolving over time and can be easily predicted using machine learning.

Rice University,  Nano-Enabled Water Treatment National Science Foundation (NSF) award #EEC-1449500

Researchers should cite this work as follows:

• Chris Winkler, Rice University, NEWT Center (2022), "Adaptations to Convection Cells," https://nanohub.org/resources/36339.

  BibTex | EndNote

1. atmospheric systems
2. climate change
3. Copernicus
4. data analysis
5. deep learning
6. ECMWF
7. K-12
8. machine learning
9. NanoEnabled Water Treatment (NEWT) ERC