Quantum Parametric Mode Sorting Lidar for Measurement of Snow Properties
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 (Dawson, 2016).
In response to these science needs, we propose 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. Similar to our eyes, traditional backscatter lidars cannot penetrate deep into clouds, fog, rain, snow, tree leaves, and turbid water, even if there are a significant number of photons backscattered from objects behind these turbid media. QPMS lidar detects objects through these turbid media, demonstrating a major advantage over existing space-based lidars (CALIPSO, GEDI, ICESat2). Under a NASA ESTO funded IIP, we will build and test a QPMS lidar at 515 nm to measure snow and water scenes. We will present the status of lidar hardware design, build, and performance characterization, as well as the plan for testing and verification in snow and turbid water in the lab.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.C41D..01L