Title: Quantum Parametric Mode Sorting Lidar for Measurement of Snow Properties
Presenting Author: Carl Weimer
Organization: BAE Systems, Inc.
Co-Author(s): Jennifer Lee, Lyle Ruppert, Jeff Applegate, Yong Meng Sua, Yuping Huang, Knut Stamnes, Brandon Mitchell, Xubin Zeng, Hans Peter Marshall, Jason Stoker

Abstract:
Snowpack and glaciers provide essential water resources for a large fraction of Earth’s population; moreover, snowpack has significant impact on weather, climate, and ecosystem functioning through a variety of different mechanisms. While snow cover extent can be measured via satellite remote sensing (Dozier, 1989), snow water equivalent (SWE) and snow depth measurements are much more challenging. Several satellite remote sensing data products (e.g., AMSR-E passive microwave) inform global estimates of SWE and snow depth, but with large uncertainties. In response to these science needs, we proposed an innovative lidar technology to measure physical properties of shallow snow, including a direct measurement of snow depth, and measurements of snow grain size, snow density and SWE – a measurement specifically called out in the 2017 Earth Science Decadal Survey (National Academies Press, 2018). Quantum Parametric Mode Sorting (QPMS) Lidar uses quantum frequency conversion at the edge of phase matching in a nonlinear medium to selectively detect desired time-frequency modes. This enables effective rejection of background light as well as multiple scattered photons which interfere with the return signal from the target. Our three-channel system (1030nm (Classical), 515 nm (Classical) and 515 nm (Quantum)) allows direct comparison and synergy between the different scattered signals. We are demonstrating in the laboratory in natural snow and turbid water scenes the relative advantages and synergies. Additional theoretical work on the information content in the multiple scattering “tails” of the signals has already demonstrated snow depth measurement by the green classical channel. Snow properties are the critical next step.