论文标题
Pycast：一条机器学习管道，以预测pyocumulonimbus（pyrocb）云
Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds
论文作者
论文摘要
丘陵（Pyrocumulonimbus）（Pyrocb）云是由极端野火产生的风暴云。 Pyrocb与不可预测的，因此危险的野火传播有关。它们还可以注入烟雾颗粒并将气体痕迹进入对流层和下层平流层，从而影响地球的气候。随着全球温度的升高，这些以前罕见的事件变得越来越普遍。因此，能够预测哪些火灾可能会产生pyocb是野火易发地区气候适应的关键。本文介绍了Pyocast，这是用于pyrocb分析和预测的管道。介绍了管道的前两个组件，即pyrocb数据库和Pyrocb预测模型。该数据库汇集了2018年至2022年之间整个北美，澳大利亚和俄罗斯超过148个Pyrocb事件的地理图像和环境数据。随机森林，卷积神经网络（CNN）以及用自动编码器预处理的CNN进行了测试，以预测给定六个小时的Pyrocb的生成。最佳型号预测了pyrocb，AUC为$ 0.90 \ pm 0.04 $。
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 $0.90 \pm 0.04$.
