降雨增强试验的偏差调整归因估计
Bias-Adjusted Attribution Estimation for Rainfall Enhancement Trials
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中文总结 AI 辅助
研究降雨增强试验中对数降雨量反变换回原始尺度时的偏差问题，提出含偏差调整项的新归因估计器，改进现有方法，满足一致性属性，通过模拟研究和实际试验验证其优越的估计精度和推断性能。
中文摘要 AI 辅助
基于模型的降雨增强试验数据分析通常使用线性混合模型对对数转换后的降雨量进行建模，以评估增强方法在实际条件下的有效性。该方法通过明确控制可能影响降水量的气象和地形协变量的影响，改进了传统的基于平均值的分析。然而，这种分析的一个关键问题是，在将对数降雨量反变换回原始尺度以估计归因时会出现偏差，归因定义为增强方法导致的额外原始尺度降雨量。为了解决这个问题，我们提出了一种新的归因估计器，它纳入了理论上合理的、特定于观测的偏差调整项。所提出的估计器改进了现有的依赖于任意调整的估计器，并满足一种一致性属性，即确保对没有增强干预的观测的估计归因量为零。进一步使用比例随机效应块自举法对归因量进行推断。将所提出的估计器和现有的估计器应用于2013年至2018年阿曼降雨增强试验，我们发现在5%的显著性水平下，地面电离技术对下风方向降雨有统计学上显著的正效应，我们提出的估计器表明的效应比现有方法小。模拟研究进一步支持了基于所提出的估计器的结果，证明了其优越的估计精度和相关自举置信区间的改进推断性能。
英文摘要
Model-based analyses of rainfall enhancement trial data typically involve modelling log-transformed rainfall using linear mixed models to assess the effectiveness of enhancement methods under real-world conditions. This approach improves on traditional average-based analyses by allowing explicit control for the effects of meteorological and topographical covariates that may affect precipitation amounts. However, a key issue with such analyses is the bias that arises when back-transforming the log-rainfall to the original scale for estimating attribution, defined as the additional raw-scale rainfall attributable to the enhancement method. To address this issue, we propose a new attribution estimator that incorporates theoretically justified, observation-specific bias-adjustment terms. The proposed estimator improves upon existing estimators that rely on arbitrary adjustments, and satisfies a coherence property that ensures zero estimated attribution for observations without enhancement intervention. A proportional random effect block bootstrap is further used to conduct inference on the attribution quantities. Applying both the proposed estimator and an existing estimator to the Oman rainfall enhancement trial from 2013 to 2018, we find statistically significant positive effect of the ground-based ionization technology on downwind rainfall at the 5% significance level, with our proposed estimator indicating a smaller effect than the existing method. A simulation study further support the findings based on the proposed estimator, demonstrating its superior estimation accuracy and improved inferential performance of the associated bootstrap confidence intervals.
