Analysis scripts and dataset for Zhang et. al. (2024)

This archive contains post-processed data and scripts for analyses in Zhang et al. (2024) "A Machine Learning Bias Correction on Large-Scale Environment of High-Impact Weather Systems in E3SM Atmosphere Model". These data are derived from the model outputs from the simulations conducted with DOE's E3SM Atmosphere Model Version 2 (EAMv2). There are two groups of simulations. The first group consists of three model simulations were conducted with EAMv2, including one preset-day and two pseudo-global warming simulations with prescribed perturbations on sea surface temperature (SST) and sea ice concentrations (SICs). The second group contains the three same simulations that were post-processed with a machine learning bias correction model. A detailed description of the model and simulations can be found in Zhang et. al. (2024).本归档文件包含Zhang等人2024年发表的论文《E3SM大气模式中强影响天气系统大尺度环境场的机器学习偏差校正》所使用的后处理数据与分析脚本。本数据集源自采用美国能源部（Department of Energy, DOE）E3SM大气模式第2版（E3SM Atmosphere Model Version 2, EAMv2）开展的模拟试验的模式输出结果。 本次研究包含两组模拟试验。第一组为3组采用EAMv2开展的模式模拟，其中1组为当代基准模拟，另外2组为伪全球变暖模拟，二者均施加了海表温度（sea surface temperature, SST）与海冰浓度（sea ice concentrations, SICs）的强迫扰动。 第二组则是对上述3组模拟结果采用机器学习偏差校正（Machine Learning Bias Correction）模型进行后处理得到的数据集。 关于模式与模拟试验的详细说明，请参见Zhang等人2024年的相关研究。