An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with <i>in situ</i> instruments located 320 m from the lidar. The micrometeorological humidity data were used to calibrate the ratio of the lidar gains of the H<sub>2</sub>O and the N<sub>2</sub> photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem.
© 2000 Optical Society of America
(070.0070) Fourier optics and signal processing : Fourier optics and signal processing
(070.6110) Fourier optics and signal processing : Spatial filtering
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(280.3640) Remote sensing and sensors : Lidar
P. L. Fuehrer, C. A. Friehe, T. S. Hristov, D. I. Cooper, and W. E. Eichinger, "Statistical-Uncertainty-Based Adaptive Filtering of Lidar Signals," Appl. Opt. 39, 850-859 (2000)