Statistical-uncertainty-based adaptive filtering of lidar signals
2000
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 H2O and the N2
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.
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