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气候模型组合的动态生成缩小规模
Ignacio Lopez-Gomez1, Zhong Yi Wan1, Leonardo Zepeda-Núñez1
1Google Research, Mountain View, CA 94043.
概括
我们引入了一种新的动态生成缩放方法,以高效地创建高分辨率的气候预测. 这种方法提高了不确定性估计和区域气候建模的准确性,使得大型合奏成为可能.
科学领域:
- 气候科学 气候科学
- 人工智能的人工智能
- 计算建模 计算建模
背景情况:
- 高分辨率的区域气候预测对于农业和水文等行业至关重要.
- 目前使用由地球系统模型 (ESM) 驱动的区域气候模型 (RCM) 的动态缩放方法对于大型合奏来说在计算上是不可接受的.
- 现有的统计降级方法在捕捉复杂的气象场特征时往往缺乏准确性.
研究的目的:
- 开发一种具有成本效益的方法,用于从大型集合中生成高分辨率的气候预测.
- 提高缩小规模气候数据中不确定性估计的准确性.
- 将基于物理的建模的优势与生成AI的效率相结合.
主要方法:
- 一个两阶段的缩小过程:首先,使用由ESM输出驱动的RCM进行动态缩小,以达到中间分辨率.
- 其次,生成扩散模型将分辨率改进到目标尺度.
- 评估与合模型相互比较项目6 (CMIP6) 动态缩小和统计缩小方法的评估.
主要成果:
- 动态生成方法与较小的集合或传统的统计方法相比,提供了更准确的不确定性边界.
- 与流行的统计缩小技术相比,观察到的错误明显较低.
- 该方法准确地捕捉了气象场的光谱,尾部依赖性和多变量相关性.
结论:
- 拟议的动态生成框架为缩小大型气候预测组件提供了灵活,准确和高效的解决方案.
- 这种方法使得广泛的数据集的高分辨率气候预测可以通过计算获得.
- 它代表了与纯动态缩放和区域气候分析的传统统计方法相比的重大进步.
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