  Pyrocumulonimbus (pyroCb) clouds are storm clouds generated by extreme
wildfires. PyroCbs are associated with unpredictable, and therefore dangerous,
wildfire spread. They can also inject smoke particles and trace gases into the
upper troposphere and lower stratosphere, affecting the Earth's climate. As
global temperatures increase, these previously rare events are becoming more
common. Being able to predict which fires are likely to generate pyroCb is
therefore key to climate adaptation in wildfire-prone areas. This paper
introduces Pyrocast, a pipeline for pyroCb analysis and forecasting. The
pipeline's first two components, a pyroCb database and a pyroCb forecast model,
are presented. The database brings together geostationary imagery and
environmental data for over 148 pyroCb events across North America, Australia,
and Russia between 2018 and 2022. Random Forests, Convolutional Neural Networks
(CNNs), and CNNs pretrained with Auto-Encoders were tested to predict the
generation of pyroCb for a given fire six hours in advance. The best model
predicted pyroCb with an AUC of $0.90 \pm 0.04$.
