Skip to yearly menu bar Skip to main content

Workshop: Tackling Climate Change with Machine Learning

Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) clouds

Kenza Tazi


Pyrocumulonimbus (pyroCbs) are storm clouds generated by extreme wildfires. PyroCbs are associated with unpredictable wildfire spread. They can also inject smoke particles and trace gases into the upper troposphere and lower stratosphere. As the climate warms, these previously rare events are becoming more common. This paper presents Pyrocast, a pipeline for pyroCb analysis and forecasting. This paper presents the pipeline's two first components: a pyroCb database and a pyroCb forecasting model. The database brings together geostationary imagery and environmental data for over 148 pyroCb events across North America and Australia 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 6 hours in advance. The best model predicted pyroCb with an AUC of 0.90±0.04.

Chat is not available.