改进了南亚季风降雨的季后预测,使用数据驱动的振荡模式预测
Eviatar Bach1,2, V Krishnamurthy3, Safa Mote4,5
1Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA 91125.
概括
这项研究通过将大气模型与数据驱动的季风季内振荡 (MISO) 预测相结合,改善了南亚季风降雨预测. 这种方法提高了在关键的10-30天延长范围内的降雨预测技能.
科学领域:
- 气象学 天气学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 准确预测南亚季风降雨对于农业和水资源管理至关重要.
- 季风季内振荡 (MISO) 显著影响区域降雨模式,但传统模型对其的预测很差.
- 现有的数据驱动方法仅预测与MISO相关的降雨量,而不是完整的信号.
研究的目的:
- 为了改善南亚季风的延长范围降雨预报.
- 提高季风季内振荡 (MISO) 和它们对降雨的影响的预测.
- 为了弥合10-30天扩展预测范围中的可预测性差距.
主要方法:
- 结合高分辨率大气模型组合降水预测与数据驱动的MISO预测.
- 预测大气模型组合成员在MISO动态子空间上.
- 根据其与数据驱动的MISO预测相近的权重组合成员.
主要成果:
- 在印度和更广泛的季风地区的降雨预报中取得了显著的改善.
- 在10-30天的预测时间内,降雨预测的增强时间相关性高达0.28.
- 在以前具有挑战性的可预测性差距中表现出改进的预测技能.
结论:
- 利用MISO可预测性可以大大改善长距离季风降雨预报.
- 结合动态和数据驱动的方法,为地球系统预测提供了一个有希望的未来.
- 混合方法提高了季风降雨预测的准确性,使气候敏感部门受益.
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