A solution based on a Kalman filter to trace the evolution of the atmospheric boundary layer (ABL) sensed by a ground-based elastic-backscatter tropospheric lidar is presented. An erf-like profile is used to model the mixing-layer top and the entrainment-zone thickness. The extended Kalman filter (EKF) enables to retrieve and track the ABL parameters based on simplified statistics of the ABL dynamics and of the observation noise present in the lidar signal. This adaptive feature permits to analyze atmospheric scenes with low signal-to-noise ratios (SNRs) without the need to resort to long-time averages or range-smoothing techniques, as well as to pave the way for future automated detection solutions. First, EKF results based on oversimplified synthetic and experimental lidar profiles are presented and compared with classic ABL estimation quantifiers for a case study with different SNR scenarios.

Peer Reviewed