Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.

This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.

Specifically, this repository contains the following items: 

(1) The codes needed for assessing the representation and  prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. 

(2) Original and normalized data to run these codes.

(3)  Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.

[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.

[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.

[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.