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基于过程的分样采集改善了中美洲的季后降雨预测.
Katherine M Kowal1, Louise J Slater1, Sihan Li2
1Department of Geography and the Environment University of Oxford Oxford UK.
概括
为了改善中美洲的月度降雨预报,本研究使用了基于过程的评估. 通过基于风和海面温度 (SST) 预测器的气候模型成员的亚抽样,提高了对降雨极端情况的预测技能.
科学领域:
- 气候科学 气候科学
- 气象学 天气学
- 水文学的水文学
背景情况:
- 对于管理水气天气极端情况而言,季后降雨预报技能至关重要.
- 准确的每月降雨预测对于中美洲的准备工作至关重要.
研究的目的:
- 评估一个过程知情评估是否可以提高中美洲的每月降雨预报.
- 通过根据关键预测因素对气候模型成员进行亚抽样来提高降雨预测的技能.
主要方法:
- 通过从五个动态预测模型 (C3S多模型组合) 中对130个成员进行亚抽样,生成一个受约束的组合平均值.
- 亚样本采集是基于模型对区域风向和初始化时太平洋/大西洋海面温度 (SST) 的表示.
- 评估重点是改善哥斯达黎加和危地马拉的预测.
主要成果:
- 在多个月和地点观察到,平均平方误差技能增加了0.4.
- 降雨极端情况的检测率得到了提高.
- 该方法在识别准确表示降雨分布的模型成员方面取得了成功.
结论:
- 基于过程的部分采样显著提高了中美洲的每月降雨预报技能.
- 该方法可应用于其他受缓慢变化的气候过程影响的地区.
- 该方法有效地通过计算风力和SST预测器的错误来完善预测.
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