
Research Scientist / Scholar III
Teams:
- MetSat
In 2016, Kyle’s passion for satellite retrievals and precipitation led him to join CIRA as a Research Associate. This provided him the exciting opportunity to analyze data from the new GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) instruments. His main focus has been on using GOES to better initialize convection in high-resolution weather models. Working with data from GLM sparked his interest in lightning.
Most recently, Kyle has become fascinated in the power of artificial intelligence / machine learning to extract spatio-temporal patterns in satellite imagery. His recent research has used convolutional neural networks to extract precipitation latent heating rates from GOES ABI+GLM to inform numerical weather prediction models. He has developed approaches for visualizing and interpreting what the machine has learned.
Living in California, Kyle witnessed the incredible growth rates that are possible with wildfires and experienced the smoke impacts from nearby wildfires. This has developed into a major research interest of his, and since 2018 Kyle’s research has used satellite data and data assimilation for improving coupled numerical modeling of wildfires.
In his free time, Kyle enjoys watching the weather in Colorado, playing the violin, running and road cycling in the foothills around Fort Collins.