Using Deep Learning to Develop a High Resolution Planetary Boundary Layer Model for Infrasound Propagation
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Nevada National Security Site/Mission Support and Test Services LLC (NNSS/MSTS), North Las Vegas, NV (United States)
Infrasound, with frequencies less than 20 Hz, is generated by both natural and anthropogenic sources. When one of these sources exerts a force on the atmosphere, infrasonic waves are generated. The propagation of these waves largely depends on temperature, wind speed, and wind direction. Previous work has used deep learning to accurately predict atmospheric specifications to altitudes of ~40 km. However, this model breaks down for local distances because it is too low resolution. Here we use a high-resolution meteorological dataset collected in Las Vegas, Nevada, USA to develop a deep learning model that can predict temperature, wind speed, and wind direction. Predictions are compared to ground truth observations to show that the model performs well at predicting temperature and wind direction but struggles with prediction wind speed. Model limitations and improvements are also discussed.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 2432153
- Report Number(s):
- SAND--2023-00660
- Country of Publication:
- United States
- Language:
- English
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