From Event: SPIE Optical Engineering + Applications, 2017
An accurate model and parameterization of aerosol concentration is needed to predict the performance of electro-optical
imaging systems. Current models have been shown to vary widely in their ability to accurately predict aerosol size
distributions and subsequent scattering properties of the atmosphere. One of the more prevalent methods for modeling
particle size spectra consists of fitting a modified gamma function to measurement data, however this limits the
distribution to a single mode. Machine learning models have been shown to predict complex multimodal aerosol particle
size spectra. Here we establish an empirical model for predicting aerosol size spectra using machine learning techniques.
This is accomplished through measurements of aerosols size distributions over the course of eight months. The machine
learning models are shown to extend the functionality of Advanced Navy Aerosol Model (ANAM), developed to model
the size distribution of aerosols in the maritime environment.
Joshua J. Rudiger, Kevin Book, John Stephen deGrassie, Stephen Hammel, and Brooke Baker, "A machine learning approach for predicting atmospheric aerosol size distributions," Proc. SPIE 10408, Laser Communication and Propagation through the Atmosphere and Oceans VI, 104080Q (Presented at SPIE Optical Engineering + Applications: August 09, 2017; Published: 18 September 2017); https://doi.org/10.1117/12.2276717.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the proceedings. They include the speaker's narration with video of the slides and animations. Most include full-text papers. Interactive, searchable transcripts and closed captioning are now available for 2018 presentations, with transcripts for prior recordings added daily.
Search our growing collection of more than 16,000 conference presentations, including many plenaries and keynotes.