适应性偏差纠正,以改善季后预测
Soukayna Mouatadid1, Paulo Orenstein2, Genevieve Flaspohler3,4,5
1Department of Computer Science, University of Toronto, Toronto, ON, Canada. soukayna@cs.toronto.edu.
Nature communications
|June 15, 2023
概括
适应性偏差校正 (ABC) 方法通过将动态模型与机器学习相结合,显著改善季后天气预报. 这一进步提高了对关键应用的2-6周前的温度和降水预测.
科学领域:
- 气象学 天气学
- 气候科学 气候科学
- 机器学习 机器学习
背景情况:
- 季后预报 (2-6周前) 对水资源管理,野火控制和减轻洪水/干旱至关重要.
- 当前的动态模型在预测温度和降水方面存在局限性,原因是大气动力学和物理学的错误.
研究的目的:
- 引入和评估一种适应性偏差校正 (ABC) 方法,以提高季后天气预报.
- 为了提高温度和降水预报的准确性,超出目前的运行模型能力.
主要方法:
- 开发了一种自适应偏差校正 (ABC) 方法,将机器学习与观察数据相结合.
- 将ABC方法应用于欧洲中期天气预报中心 (ECMWF) 的次季节模型.
主要成果:
- 在连续的美国,ABC提高了60-90%的温度预测技能.
- 在连续的美国,ABC提高了降水预报技能40-69%,在连续的美国,降水预报技能提高了40-69%.
- 开发了一种工作流程,以解释技能增长,并确定最佳预测窗口.
结论:
- 适应性偏差校正 (ABC) 方法比现有的季后预测技术提供了显著的改进.
- 这种方法为利用机器学习提供了一个实际的框架,以提高气候预测,并为关键资源管理决策提供信息.
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