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Kyle Hilburn

Kyle Hilburn

Research Scientist / Scholar III

Mailing Address:
Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523-1375
  • Office Location:
    CIRA 21
  • 970-491-8457
About Me:

Kyle grew-up in Minnesota, where his experiences with extreme weather inspired him to study meteorology. Kyle received his B.S. in Atmospheric Science from University of North Dakota (2000), a M.S. in Meteorology from Florida State University (2002), and a Ph.D. in Atmospheric Science from Colorado State University (2023) . At UND, he performed analysis of cloud microphysical data collected by the Citation aircraft. At FSU, he used ocean vector wind measurements from QuikSCAT to derive surface pressure fields. This work at FSU inspired his interest in satellite meteorology and data assimilation.

In 2002, Kyle joined Remote Sensing Systems in Santa Rosa, California where he worked as a Scientist and Lead Software Developer. His research initially focused on improving QuikSCAT wind retrievals in raining scenes, but this evolved into a broader pursuit of precipitation retrievals from passive microwave imagers. Kyle also studied the use of microwave satellite observations to better constrain the global water cycle.

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. Kyle is also involved in satellite data applications for monitoring and modeling wildfires.

In his free time, Kyle enjoys watching the weather in Colorado, playing the violin, and road cycling in the foothills around Fort Collins.