Enhancing observational capabilities of water vapor micro pulse differential absorption lidar through simultaneous denoising and inference
Abstract
The MicroPulse Differential Absorption Lidar (MPD) is developed by Montana State University and the National Center for Atmospheric Research to continuously measure high-vertical-resolution water vapor (WV) profiles in the lower atmosphere. These diode-laser-based instruments are accurate, low-cost, operate unattended, do not require external calibration, and eye-safe - all key features to enable larger "national-scale" networks needed to characterize atmospheric moisture variability, which influences important weather and climate processes.
For these photon counting instruments, photon detector noise and solar background limits the altitude and resolution of WV retrievals using the standard method (STDM) between 4 and 6 km at 5 minute resolution . Above these altitudes, WV measurements have proven difficult due to low signal to noise ratio (SNR). However, more advanced processing techniques could increase the altitude coverage of the MPD and further extend the scientific applications for MPD. We demonstrate the ability to extend the MPD through the Poisson Total Variation (PTV) method, which simultaneously denoises and infers WV from noisy MPD observations. PTV solves a Poisson inverse problem while promoting piecewise constant structure in the WV estimate; PTV has been previously applied on ground-based photon counting HSRL observations. Through radiosonde WV measurements we show that PTV provides more accurate WV measurements than STDM at higher and/or drier altitudes. This work has for the first time directly validated PTV with range resolved in situ measurements, which further demonstrates that signal processing methods can greatly improve the retrieval accuracy of lidar instruments. We also show that PTV can provide measurements at higher temporal resolutions, compared to STDM, while providing high fidelity measurements.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMA249...06M
- Keywords:
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- 3311 Clouds and aerosols;
- ATMOSPHERIC PROCESSES;
- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES