Machine learning

We are interested in applying machine learning algorithms to various problems in climate science and geoscience, such as dimensionality reduction, cluster analysis, forward and inverse modeling, and image recognition. So far we have mostly used relatively simple machine learning algorithms, including principal component analysis/empirical orthogonal function analysis (e.g., Chen et al., 2013), self-organizing maps (e.g., Chen et al., 2016; Lai et al., 2021), and random forests (e.g., Zhong et al., 2023).

People working on this topic


Hans Chen

Assistant Professor

We are keen on applying modern deep learning and image recognition techniques, for example to detect emission plumes for greenhouse gas monitoring, or to identify and track atmospheric rivers. Our group is collaborating with other groups that use such techniques, for example for air quality monitoring (e.g., Yan et al., 2023).

Other developments we follow closely include applications of machine learning in numerical weather prediction. Such machine learning-based models could be highly value not only for making deterministic forecasts, but also for generating large ensembles of forecasts that can be used in ensemble-based data assimilation. We are also interested in augmenting traditional data assimilation methods with machine learning-based algorithms, for example to overcome conventional limitations due to linear and Gaussian assumptions.

Related publications

Zhong, Z., B. He, H. W. Chen, D. Chen, T. Zhou, W. Dong, C. Xiao, L. Guo, R. Ding, L. Zhang, X. Song, L. Huang, W. Yuan, X. Hao, and X. Zhao, 2023: Reversed asymmetric warming of sub-diurnal temperature over land during recent decades. Nature Communications, 14, 7189,

Chen, H. W., Q. Zhang, H. Körnich, and D. Chen, 2013: A robust mode of climate variability in the Arctic: The Barents Oscillation. Geophysical Research Letters, 40, 2856–2861,