Identifying Causes of Pyrocumulonimbus (PyroCb)

Abstract

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.

Cite

Text

Diaz et al. "Identifying Causes of Pyrocumulonimbus (PyroCb)." NeurIPS 2022 Workshops: CML4Impact, 2022.

Markdown

[Diaz et al. "Identifying Causes of Pyrocumulonimbus (PyroCb)." NeurIPS 2022 Workshops: CML4Impact, 2022.](https://mlanthology.org/neuripsw/2022/diaz2022neuripsw-identifying/)

BibTeX

@inproceedings{diaz2022neuripsw-identifying,
  title     = {{Identifying Causes of Pyrocumulonimbus (PyroCb)}},
  author    = {Diaz, Emiliano and Tazi, Kenza and Braude, Ashwin S. and Okoh, Daniel and Lamb, Kara and Watson-Parris, Duncan and Harder, Paula and Meinert, Nis},
  booktitle = {NeurIPS 2022 Workshops: CML4Impact},
  year      = {2022},
  url       = {https://mlanthology.org/neuripsw/2022/diaz2022neuripsw-identifying/}
}