1- 1University of Southern California, Viterbi School of Engineering, Civil and Environmental Engineering, Los Angeles, United States of America (kayleybu@usc.edu)
- 2University of Southern California, Department of Earth Sciences, United States of America
Image analysis is integral in understanding the increasing abundance of atmospheric science imagery
data. Whereas time-consuming analysis of individual images or simplified image processing techniques
were previously necessary, machine learning can now quickly learn trends in and distinctions between
images. However, training deep learning models on large datasets can be computationally expensive.
Leveraging the architectures and weights of pre-trained neural networks can alleviate some expense. In
this work, we apply pre-trained networks to back trajectory images generated for the NASA Aerosol
Cloud meTeorology Interactions oVer the western north ATlantic Experiment (ACTIVATE) campaign.
We find this method outperforms the principal component analysis baseline and results in four
geographically distinct clusters. Pairing these images with the host of measurements taken during the
ACTIVATE campaign, we find the regions to also be distinct in their bulk characteristics of chemical and
microphysical variables.
How to cite: Butler, K. and Silva, S. J.: Convolutions, clusters, and characterizations: Using pretrained networks for back trajectory analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15004, https://doi.org/10.5194/egusphere-egu26-15004, 2026.
