Data for: Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields

This dataset contains the training results (model parameters, outputs), datasets for generalization testing, and 2-D implementation used in the article "Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields." The article will be submitted to Artificial Intelligence for Earth Systems. The datasets are saved as CSV for 1-D time-series data and *netCDF for 2-D time series dataset. The model parameters are saved in every training epoch tested in the study.

本数据集汇集了训练结果（模型参数、输出数据）、用于泛化测试的数据集，以及文章《利用学习的一维被动标量对流加速化学传输建模：以GEOS-FP水平风场为案例研究》中采用的二维实现。该文章拟投稿至《地球系统人工智能》期刊。数据集以CSV格式保存一维时间序列数据，以*netCDF格式保存二维时间序列数据。模型参数按照研究中测试的每个训练周期进行保存。