A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented. Invariant Causal Prediction was used to develop tools to understand the causal drivers of pyroCb formation. This includes a conditional independence test for testing $Y \indep E|X$ for binary variable $Y$ and multivariate, continuous variables $X$ and $E$, and a greedy-ICP search algorithm that relies on fewer conditional independence tests to obtain a smaller more manageable set of causal predictors. With these tools, we identified a subset of seven causal predictors which are plausible when contrasted with domain knowledge: surface sensible heat flux, relative humidity at $850$\,hPa, a component of wind at $250$\,hPa, $13.3$\,\textmu m thermal emissions, convective available potential energy, and altitude.
翻译:介绍了从热电联苯(极端野火产生的暴云)观测数据中得出的首次因果发现分析; 常态原因预测用于开发各种工具,以了解热电联苯形成的原因驱动因素; 其中包括对二进制变量Y$和多变量、连续变量X美元和E$X$进行测试的有条件独立测试; 持续变量X$和E$, 以及贪婪的ICP搜索算法,该算法依靠较少的有条件独立测试获得数量较少的、更易于控制的因果预测数。 通过这些工具,我们确定了7种与域知识相对比较的因果预测值:表面合理热通量、相对湿度为850美元,风力部分为250美元\,hPa,13.3美元\, textmu 热排放量,对流潜在能量和高度。