Climate Change Data Portal
| DOI | 10.1038/s41560-024-01507-9 |
| High-resolution meteorology with climate change impacts from global climate model data using generative machine learning | |
| 发表日期 | 2024 |
| ISSN | 2058-7546 |
| 英文摘要 | As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States. Global climate models are challenging to integrate in energy system models because their output data resolution is too coarse. Buster et al. generate high-resolution meteorological data with climate change impacts from global climate model datasets using generative machine learning. |
| 语种 | 英语 |
| WOS研究方向 | Energy & Fuels ; Materials Science |
| WOS类目 | Energy & Fuels ; Materials Science, Multidisciplinary |
| WOS记录号 | WOS:001199169200001 |
| 来源期刊 | NATURE ENERGY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/287780 |
| 作者单位 | United States Department of Energy (DOE); National Renewable Energy Laboratory - USA |
| 推荐引用方式 GB/T 7714 | . High-resolution meteorology with climate change impacts from global climate model data using generative machine learning[J],2024. |
| APA | (2024).High-resolution meteorology with climate change impacts from global climate model data using generative machine learning.NATURE ENERGY. |
| MLA | "High-resolution meteorology with climate change impacts from global climate model data using generative machine learning".NATURE ENERGY (2024). |
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