title: Statistical-uncertainty-based adaptive filtering of lidar signals.
abstract: 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 in situ instruments located 320 m from the lidar. The micrometeorological humidity data were used to calibrate the ratio of the lidar gains of the H(2)O and the N(2) 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.
authors.affiliation: Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, California 92697-3975, USA. perry@wave.eng.uci.edu
authors.forename: P L
authors.initials: PL
authors.lastname: Fuehrer
authors.forename: C A
authors.initials: CA
authors.lastname: Friehe
authors.forename: T S
authors.initials: TS
authors.lastname: Hristov
authors.forename: D I
authors.initials: DI
authors.lastname: Cooper
authors.forename: W E
authors.initials: WE
authors.lastname: Eichinger