Estimation of atmospheric boundary layer using Kalman filter technique

Mukherjee, Abhik; Adhikari, Partha Pratim; Nandi, Prasanta K.; Pal, Pinakpani; Das, J.

doi:10.1016/s0165-1684(02)00338-9

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…Thus, the EKF was applied in [18] [19] to estimate the atmospheric optical extinctionand backscatter-coefficient profiles from backscatter lidar returns. In [20] the authors used a scalar Kalman filter to estimate the ABLH from sodar signals. Very recently, [4] has successfully applied the EKF to estimate the ABLH from backscatter lidar signals and by comparison to classic ABLH estimation methods.…”

Section: Introductionmentioning

confidence: 99%

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

Lange

Rocadenbosch

Tiana-Alsina

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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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 (FMCW) 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 scatter-

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Section: Introductionmentioning

confidence: 99%

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

Lange

Rocadenbosch

Tiana-Alsina

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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“…On the other hand, Mukherjee et al [25] have applied a scalar Kalman filter to estimate the ABL height from sodar signals.…”

Section: Abl Adaptive Detection Methodsmentioning

confidence: 99%

“…Measurement Model: In the EKF approach, at each successive time t k , the filter compares the actual observable z k formed from the measured normalized lidar signal [see (25)] with a linearized version of the observation model, H k . The latter is based on the partial derivatives of the measurement-model function h(R) [see (4)] evaluated at the "a priori" estimate of the state vector,…”

Section: Filter Modelsmentioning

confidence: 99%

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Atmospheric Boundary Layer Height Monitoring Using a Kalman Filter and Backscatter Lidar Returns

Lange

Tiana-Alsina

Saeed

et al. 2014

IEEE Trans. Geosci. Remote Sensing

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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.Index Terms-Adaptive kalman filtering, laser radar, remote sensing, signal processing.

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“…Pattern recognition techniques have been used [9] to interpret the meteorological conditions from this boundary information. The time evolution of mixing height has been modeled and Kalman filter (KF) has been designed to estimate the PBL from extracted data [10]. Filter has been designed to adapt to relevant system model [11].…”

Section: Previous Workmentioning

confidence: 99%

Exploring the Dynamics of Capped Inversion From Sodar Data

Roy

Mukherjee

2012

Fluct. Noise Lett.

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The time evolution of planetary boundary layer can be studied from sodar data. Normally most of the sound wave transmitted above gets backscattered from a single inversion layer. However, during turbulent atmospheric conditions, there are multiple layers from where the signal gets backscattered with varying echo strengths. The present work describes a methodology for computer analysis of multilayered structures. Two distinct backscatter heights are extracted from observed echograms. This noisy measurement is filtered to estimate the layer heights using an a priori system model. The process and measurement noise covariances used by the filter algorithm are tuned from physical considerations to get good filter performance. Data from sodar installed at Maitree station of Antarctica with capping over an inversion layer is used to prove the concept.

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Estimation of atmospheric boundary layer using Kalman filter technique

Cited by 6 publications

References 12 publications

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

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

Atmospheric Boundary Layer Height Monitoring Using a Kalman Filter and Backscatter Lidar Returns

Exploring the Dynamics of Capped Inversion From Sodar Data

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