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Comparing causal techniques for rainfall variability analysis using causality algorithms in Iran

机译：使用伊朗因果算法比较降雨变异性的因果技术

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Causal analysis (CA) is a strong quantitative approach whose mechanisms have climatic predictions. In this study, we studied the patterns of causality (PC) on the effect of rainfall (ER) using climatic series collected from 170 stations for the period 1975–2014 in Iran. Next, we predicted the causal relationships of climatic variables using causal models, including first-generation techniques (FGT), second-generation techniques (SGT), third-generation techniques (TGT), and causal hybrid techniques (CHT). Then, we estimated the causal models using partial squares algorithms (PSA), mechanical equations modeling algorithms (MEMA) such as exploratory and confirmatory methods, and spatial variability methods such as geostatistics and spatial statistical methods. Finally, we evaluated the quality of the methods using the goodness of fit indices, including absolute fit indices (AFI), comparative fit indices (CFI), and parsimonious fit indices (PFI). The results showed that CHT algorithm more suitably predicted the climatic spatiotemporal effect variability (SEV) by extracting direct, indirect, and total effects of climatic variables. Based on the CHT algorithm, the highest and lowest effect values were observed in total effects of winter rainfall (0.98) and summer rainfall variables (0.1), respectively. The SEV ranged from 0.8 to 0.98 for the winter rainfall total effects of CHT in Iran. Using CHT, most of the predicted SEV, particularly the rainfall series, displayed SEV varying from 80% to 98% of the winter rainfall total effects to the annual rainfall in Iran. Similarly, based on the CHT, the highest and lowest SEV values were in western, eastern, and southern regions and in central regions, respectively. In addition, the SEV varied within the range of 0.6–0.74 (varying from 60% to 74% for the autumn rainfall total effects of the annual rainfall in Iran) for the autumn rainfall total effects in Iran. Finally, the SEV of this type of analytical pattern as well as designated subject of CA applications in the atmospheric science and environmental science are discussed.
机译：因果分析（CA）是一种强大的定量方法，其机制具有气候预测。在这项研究中，我们使用1975-2014年期间从170个站点收集的气候序列研究了因果关系（PC）对降雨（ER）的影响。接下来，我们使用因果模型预测了气候变量的因果关系，其中包括第一代技术（FGT），第二代技术（SGT），第三代技术（TGT）和因果混合技术（CHT）。然后，我们使用偏方算法（PSA），机械方程建模算法（MEMA）（例如探索性和确认性方法）以及空间变异性方法（例如地统计学和空间统计方法）来估计因果模型。最后，我们使用拟合指数的优劣来评估方法的质量，包括绝对拟合指数（AFI），比较拟合指数（CFI）和简约拟合指数（PFI）。结果表明，CHT算法通过提取气候变量的直接，间接和总效应，更适合预测气候时空效应变异性（SEV）。基于CHT算法，分别在冬季降雨（0.98）和夏季降雨变量（0.1）的总效应中观察到最高和最低效应值。对于伊朗CHT的冬季降雨总影响而言，SEV在0.8到0.98之间。使用CHT，大多数预测的SEV，尤其是降雨序列，显示出SEV的变化范围是冬季降雨总影响到伊朗年降雨量的80％至98％。同样，根据CHT，最高和最低SEV值分别位于西部，东部和南部地区以及中部地区。此外，SEV在伊朗的秋季降雨总影响范围在0.6-0.74之间（伊朗年度降雨的秋季降雨总影响范围从60％到74％）。最后，讨论了这种分析模式的SEV以及大气科学和环境科学中CA应用的指定主题。

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