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在巴西东北部计算降水数据
Daniele T Rodrigues1, Weber A Gonçalves2, Cláudio Moisés S E Silva2
1Graduação em Estatística, Universidade Federal do Piauí, Departamento de Estatística, Av. Campus Universitário Ministro Petrônio Portella, s/n, Ininga, Teresina, PI, Brazil.
Anais da Academia Brasileira de Ciencias
|June 7, 2023
概括
启动期望最大化算法 (BootEM) 有效地重建了巴西东北部缺少的每日降水数据. 这种归算方法显示最小偏差和高相关性,使其适合历史气候数据.
科学领域:
- 气候学 气候学 气候学
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
背景情况:
- 每日降水记录中缺少的数据可能会阻碍气候分析.
- 准确的归算对于了解巴西东北部 (NEB) 的降水模式至关重要.
研究的目的:
- 评估四种统计多重归算方法,以填补NEB中缺少的每日降水数据.
- 确定最有效的方法来重建历史降水记录.
主要方法:
- 四种归算技术的比较:随机抽样,预测平均值匹配,贝叶斯线性回归和引导期望最大化算法 (BootEM).
- 模拟缺失数据场景,删除10%,20%和30%的数据.
- 使用偏差和皮尔森相关度指标进行统计评估.
主要成果:
- 布特EM方法在归因缺失的降水数据方面表现出卓越的性能.
- 在所有缺失数据百分比中,BootEM的平均偏差很低 (-0.91到1.30毫米/天) 和高的皮尔森相关性 (0.86到0.96).
- 与BootEM相比,其他方法的结果不太好.
结论:
- 启动期望最大化算法 (BootEM) 是一个非常合适的方法,用于重建NEB的历史每日降水数据.
- 准确地计算降水数据对于该地区可靠的气候研究至关重要.
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