Statistical-uncertainty-based adaptive filtering of lidar signals.
P L Fuehrer1, C A Friehe, T S Hristov
1Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, California 92697-3975, USA. perry@wave.eng.uci.edu
Applied Optics
|March 14, 2008
Summary
An adaptive filter improves Raman lidar water-vapor mixing ratio measurements by reducing statistical uncertainty. This technique enhances data accuracy, especially at greater distances, by assuming horizontal homogeneity.
Area of Science:
- Atmospheric science and remote sensing.
- Signal processing and data analysis.
Background:
- Raman lidar measurements of water-vapor mixing ratio suffer from increasing statistical uncertainty with distance.
- This uncertainty masks true atmospheric water-vapor structures, limiting data utility.
Purpose of the Study:
- To develop and validate an adaptive filter signal processing technique.
- To overcome range-dependent signal-to-noise ratio issues in Raman lidar water-vapor measurements.
Main Methods:
- Applied an adaptive filter assuming statistical horizontal homogeneity to Raman lidar data.
- Utilized in situ micrometeorological data for calibration and filter parameter setting.
- Evaluated the technique on horizontal scans at a coastal site.
Main Results:
- Achieved a water-vapor signal with constant variance and variable spatial resolution.
- Determined an effective lidar range limit of approximately 200 m for the coastal experiment.
- Demonstrated the technique's applicability to other remote-sensing devices with similar SNR challenges.
Conclusions:
- The adaptive filter effectively reduces uncertainty in Raman lidar water-vapor measurements.
- The method enhances the reliability of atmospheric humidity profiling.
- The technique shows promise for broader application in remote sensing.
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