Abstract
An approach is presented for smoothing and differentiating path-integrated concentration estimates provided by range-resolved differential absorption lidar that is based on a nonstationary implementation of the Wiener-Kolmogorov filtering theory. The primary advantage of the method lies in its ability to provide filtered estimates that are smoothed relative to the local uncertainty in the input data. The approach is derived and illustrated on both synthetic and actual lidar data.
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