Code and Data for 'Machine Learning Prediction of Global Surface Eddy Mixing Ellipses' By Jing et al. Submitted to Geophysical Research Letters. ...

التفاصيل البيبلوغرافية
العنوان: Code and Data for 'Machine Learning Prediction of Global Surface Eddy Mixing Ellipses' By Jing et al. Submitted to Geophysical Research Letters. ...
المؤلفون: Tian, Jing, Ru, Chen, Chuanyu, Liu, Cuicui, Zhang, Mei, Hong
بيانات النشر: Zenodo
سنة النشر: 2024
المجموعة: DataCite Metadata Store (German National Library of Science and Technology)
الوصف: This repository contains the code and data for the study of "Machine Learning Prediction of Global Surface Eddy Mixing Ellipses” By Jing et al. Submitted to Geophysical Research Letters. Specifically, this repository contains the following items: (1) The codes needed for assessing the representation and prediction skills of Random Forest (RF) and Convolutional Neural Network (CNN) models. (2) Original and normalized data to run these codes. (3) Code here is built on early work from our laboratory (Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question. [1] 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. [2] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning ...
نوع الوثيقة: dataset
اللغة: unknown
العلاقة: https://dx.doi.org/10.5281/zenodo.11311631Test
DOI: 10.5281/zenodo.11311632
الإتاحة: https://doi.org/10.5281/zenodo.1131163210.5281/zenodo.11311631Test
حقوق: Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/legalcodeTest ; cc-by-4.0
رقم الانضمام: edsbas.EE8232F7
قاعدة البيانات: BASE