贝叶斯负二项回归模型与未观察到的共变量用于预测北大西洋热带风暴的频率
Xun Li1, Joyee Ghosh2, Gabriele Villarini3
1Discover Financial Services, Riverwood, IL, USA.
Journal of applied statistics
|June 28, 2023
概括
准确的热带风暴预测有助于准备. 这项研究引入了使用海面温度 (SST) 预测的贝叶斯模型,以改善年度热带气旋活动预测,即使缺少数据.
科学领域:
- 气象学和气候科学 气象学和气候科学
- 统计建模 统计建模
- 贝叶斯的推理是贝叶斯的推理.
背景情况:
- 预测每年热带气旋活动对于灾难准备至关重要.
- 海洋表面温度 (SST) 是强大的预测因素,但只有在风季节之后才知道.
- 气候模型提供SST预测,可用于早期预测.
研究的目的:
- 开发一个统计模型,使用SST预报来预测热带风暴的频率.
- 解决缺少预测数据和变量选择不确定性的挑战.
- 评估将预测的SST纳入模型的预测收益.
主要方法:
- 开发了一个贝叶斯负二项式回归模型.
- 在模型中区分真正的SST和它们的预测.
- 同时处理缺失的SST预测数据和变量选择不确定性.
- 从其后预测分布中采集的未观察到的预测因素中对待真实SSTs.
主要成果:
- 拟议的模型有效地将SST预测作为预测器.
- 该模型成功地处理了缺失的预测数据和变量选择不确定性.
- 在热带风暴频率中提高预测准确度的证明潜力.
结论:
- 贝叶斯模型为使用气候模型输出进行热带风暴预测提供了一个强大的框架.
- 对预测不确定性和缺失数据的计算提高了预测性能.
- 这种方法可以导致对热带风暴季节的更好准备.
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