Analysis scripts and dataset for Zhang et. al. (2023) ...
This archive contains post-processed data and scripts for analyses in Zhang et al. (2023) "A Machine Learning Bias Correction of Large-scale Environment of Extreme Weather Events in E3SM Atmosphere Model". These data are derived from the model outputs from the simulations conducted with DO...
| Main Authors: | , |
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| Format: | Dataset |
| Language: | unknown |
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Zenodo
2023
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.10106706 https://zenodo.org/doi/10.5281/zenodo.10106706 |
| Summary: | This archive contains post-processed data and scripts for analyses in Zhang et al. (2023) "A Machine Learning Bias Correction of Large-scale Environment of Extreme Weather Events 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. (2023). ... |
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