Earth System Science Reading Club

NEXUS Earth System Science Reading Club (2020 Spring)

Host: Tien-Yiao Hsu

Description: As the amount of observational data in Earth System is increasing, there is a trend exploring these datasets using machine learning. This reading club aims to understand the current development and application of machine learning in Earth System science.

Weekly sessions:

• Week 1 - Paper discussion: Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., & Carvalhais, N. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204.
• Week 2 - Mini lecture (Chia-Chun Liang): Introduction to Earth System Science.
• Week 3 - Paper discussion: Mohajerani, Y., Wood, M., Velicogna, I., & Rignot, E. (2019). Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study. Remote Sensing, 11(1), 74.
• Week 4 - Mini Lecture (Daniel Frishberg): Introduction to math of machine learning.
• Week 5 - Invited talk (Shane Coffield): Coffield, S. R., Graff, C. A., Chen, Y., Smyth, P., Foufoula-Georgiou, E., & Randerson, J. T. (2019). Machine learning to predict final fire size at the time of ignition. International journal of wildland fire.
• Week 6 - Paper discussion: Nooteboom, P. D., Feng, Q. Y., López, C., Hernández-García, E., & Dijkstra, H. A. (2018). Using Network Theory and Machine Learning to predict El Nino. arXiv preprint arXiv:1803.10076.
• Week 7 - Paper discussion: Uusitalo, L., Tomczak, M. T., Müller-Karulis, B., Putnis, I., Trifonova, N., & Tucker, A. (2018). Hidden variables in a Dynamic Bayesian Network identify ecosystem level change. Ecological informatics, 45, 9-15.