Atmospheric Boundary Layer Height Estimation Using a Kalman Filter and a Frequency‐Modulated Continuous‐Wave Radar

Abstract

An adaptive solution based on an extended Kalman filter (EKF) is proposed to estimate the atmospheric boundary-layer height (ABLH) from frequency-modulated continuous-wave S-band weather-radar returns. The EKF estimator departs from previous works, in which the transition interface between the mixing layer (ML) and the free troposphere (FT) is modeled by means of an erf-like parametric function. In contrast to lidar remote sensing, where aerosols give strong backscatter returns over the whole ML, clear-air radar reflectivity returns (Bragg scattering from refractive turbulence) shows strongest returns from the ML-FT interface. In addition, they are corrupted by “insect” noise (impulsive noise associated with Rayleigh scattering from insects and birds), all of which requires a specific treatment of the problem and the measurement noise for the clear-air radar case. The proposed radar-ABLH estimation method uses: 1) a first preprocessing of the reflectivity returns based on median filtering and threshold-limited decision to obtain “clean” reflectivity signal; 2) a modified EKF with adaptive range intervals as time tracking estimator; and 3) ad hoc modeling of the observation noise covariance. The method has successfully been implemented in clear-air, single-layer, and convective boundary-layer conditions. ABLH estimates from the proposed radar-EKF method have been cross examined with those from a collocated lidar ceilometer yielding a correlation coefficient as high as $\rho=0.93$ (mean signal-to-noise ratio, $\text{SNR} =18$ (linear units), at the ABLH) and in relation to the classic THM.