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Detection of isolated specular reflection for calibration of cloud thermodynamic phase estimation with quantum parametric mode sorting LIDAR

Richard J. Murchie, Dolf Huybrechts, Aaron Strangfeld, Mateusz P. Mrozowski

TL;DR

This work addresses the challenge of specular backscatter from horizontally-orientated ice crystals (HOICs) biasing cloud thermodynamic phase estimates in near-nadir space LIDARs. It introduces a quantum parametric mode sorting (QPMS) LIDAR that uses a nonlinear $\chi^{(2)}$ interaction to perform spatio-temporal mode selective up-conversion (sum-frequency generation), preferentially passing singly-scattered photons while suppressing background noise. The authors develop a comprehensive theoretical model of double atmospheric propagation, HOIC reflector geometry, QFC process, and detection statistics, including a strictly standardised mean difference criterion to determine the minimum signal required for reliable detection of isolated specular backscatter. They show how calibration with a conventional LIDAR at 1064 nm could quantify the HOIC/specular fraction within a beam footprint, enabling improved cloud phase classification in challenging scenes and offering a pathway to more accurate cloud thermodynamic-phase estimation. The study highlights feasibility under certain reflector and noise conditions, while outlining future work on turbulence, polarization, and more realistic reflector modelling to advance practical implementation.

Abstract

Specular reflection can be problematic for the determination of the cloud thermodynamic phase for near-nadir-pointing space LIDARs. A LIDAR system biased towards the specular contribution for backscatter, if near-concurrent to a conventional LIDAR, could calibrate the measurements required for cloud phase determination. One such system which shows promise for this is quantum parametric mode sorting (QPMS) LIDAR. Through a non-linear interaction and time-frequency mode selectivity, this system demonstrates in-band noise-rejection beyond what linear noise filtering can provide. This level of noise-rejection means the signal strength can be minimised, therefore biasing the specular contribution to the return signal. Here we provide a theoretical model of QPMS LIDAR applied to this scenario to instruct its feasibility.

Detection of isolated specular reflection for calibration of cloud thermodynamic phase estimation with quantum parametric mode sorting LIDAR

TL;DR

This work addresses the challenge of specular backscatter from horizontally-orientated ice crystals (HOICs) biasing cloud thermodynamic phase estimates in near-nadir space LIDARs. It introduces a quantum parametric mode sorting (QPMS) LIDAR that uses a nonlinear interaction to perform spatio-temporal mode selective up-conversion (sum-frequency generation), preferentially passing singly-scattered photons while suppressing background noise. The authors develop a comprehensive theoretical model of double atmospheric propagation, HOIC reflector geometry, QFC process, and detection statistics, including a strictly standardised mean difference criterion to determine the minimum signal required for reliable detection of isolated specular backscatter. They show how calibration with a conventional LIDAR at 1064 nm could quantify the HOIC/specular fraction within a beam footprint, enabling improved cloud phase classification in challenging scenes and offering a pathway to more accurate cloud thermodynamic-phase estimation. The study highlights feasibility under certain reflector and noise conditions, while outlining future work on turbulence, polarization, and more realistic reflector modelling to advance practical implementation.

Abstract

Specular reflection can be problematic for the determination of the cloud thermodynamic phase for near-nadir-pointing space LIDARs. A LIDAR system biased towards the specular contribution for backscatter, if near-concurrent to a conventional LIDAR, could calibrate the measurements required for cloud phase determination. One such system which shows promise for this is quantum parametric mode sorting (QPMS) LIDAR. Through a non-linear interaction and time-frequency mode selectivity, this system demonstrates in-band noise-rejection beyond what linear noise filtering can provide. This level of noise-rejection means the signal strength can be minimised, therefore biasing the specular contribution to the return signal. Here we provide a theoretical model of QPMS LIDAR applied to this scenario to instruct its feasibility.

Paper Structure

This paper contains 18 sections, 37 equations, 3 figures.

Table of Contents

  1. INTRODUCTION
  2. Spatio-temporal modes preliminary
  3. Atmospheric propagation and reflection
  4. Spatial
  5. Reflector characteristics
  6. Temporal
  7. Background noise
  8. Diffusive and multiple-scattering QPMS contribution
  9. Solar background noise
  10. Conventional LIDAR
  11. LIDAR system
  12. Detection statistics
  13. Calibration with conventional LIDAR
  14. Discussion
  15. Quantum state incident on the SFG waveguide
  16. ...and 3 more sections

Figures (3)

  • Figure 1: A schematic of the QPMS LIDAR system alongside the noise sources we consider.
  • Figure 2: The TM overlap coefficient as a function of the TM HG order, after double propagation through the atmosphere and reflection for the dispersed complex spectral amplitude.
  • Figure 3: Strength ratio $\frac{\vert\alpha\vert^2}{\vert\alpha_c\vert^2}$ on the y-axis with reflector radius $R$ on the x-axis, for different amplitudes of multiple-scattering contribution $\delta\alpha_c$.