Signal processing to denoise and retrieve water vapor from multi-pulse-length lidar data

Hayman, Matthew; Stillwell, Robert A.; Karboski, Adam; Spuler, Scott M.

doi:10.5194/amt-19-405-2026

Articles | Volume 19, issue 2

https://doi.org/10.5194/amt-19-405-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-19-405-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 2

Research article

|

20 Jan 2026

Research article |

| 20 Jan 2026

Signal processing to denoise and retrieve water vapor from multi-pulse-length lidar data

Matthew Hayman, Robert A. Stillwell, Adam Karboski, and Scott M. Spuler

Data sets

NCAR MPD data. Version 1.0 (Version 1.0) NSF NCAR/EOL-MPD-Team https://doi.org/10.26023/MX0D-Z722-M406

Model code and software

SpiralTorch M. Hayman et al. https://github.com/NCAR/SpiralTorch

Short summary

A new processing method for lidar data obtained from rapidly changing laser pulse lengths enables measurement of atmospheric water vapor from the ground up to 6 km. The technique blends all captured data to reveal hidden water vapor structures, especially near the surface. This solution offers continuous, high-resolution insights, key for improving weather forecasts. It showcases how flexible laser technology can enhance atmospheric observation.