Statistical-uncertainty-based adaptive filtering of lidar signals

An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-va por density to the dry-air density) with a highly variable statistical unce rtainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied t o horizontal scans, assumes only statistical horizontal homogeneity. The re sult is a variable spatial resolution water-vapor signal with a constant va riance out to a range limit set by a specified signal-to-noise ratio. The t echnique 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. T he micrometeorological humidity data were used to calibrate the ratio of th e lidar gains of the H2O and the N-2 photomultiplier tubes and set the wate r-vapor mixing ratio variance for the adaptive filter. For the coastal expe riment the! effective limit of the lidar range was found to be approximatel y 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implem ented data-reduction procedure. The technique can be adapted to off-horizon tal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-depend ent signal-to-noise ratio problem. (C) 2000 Optical Society of America.