Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash

Abstract

Atmospheric natural hazards pose a risk to people, aircraft and infrastructure. Automated algorithms can detect these hazards from satellite imagery so that the relevant advice can be issued. The transparency and adaptability of these automated algorithms is important to cater to the needs of the end user, who should be able to readily interpret the hazard warning. This means avoiding heuristic techniques. Decision theory is a statistical tool that transparently considers the risk of false positives and negatives when detecting the hazard. By assigning losses to incorrect actions, ownership of the hazard warning is shared between the scientists and risk managers. These losses are readily adaptable depending on the perceived threat of the hazard. This study demonstrates how decision theory can be applied to the detection of atmospheric natural hazards using the example of volcanic ash during an ongoing eruption. The only observations are the difference in brightness temperature between two channels on the SEVIRI sensor. We apply the method to two volcanic eruptions: the 2010 eruption of Eyjafjallajökull, Iceland, and the 2011 eruption of Puyehue‐Cordón Caulle, Chile. The simple probabilistic method appears to work well and is able to distinguish volcanic ash from desert dust, which is a common false positive for volcanic ash. As is made clear, decision theory is a tool for decision support, providing transparency and adaptability, but it still requires careful input from scientists and risk managers. Effectively it provides a space where these groups of experts can meet and convert their shared understanding of a hazard into a choice of action.