Atmospheric boundary-layer height estimation by adaptive Kalman filtering of lidar data

Sergio Tomás; Francesc Rocadenbosch; Michaël Sicard

doi:10.1117/12.866477

11 October 2010 Atmospheric boundary-layer height estimation by adaptive Kalman filtering of lidar data

Sergio Tomás, Francesc Rocadenbosch, Michaël Sicard

Author Affiliations +

Proceedings Volume 7827, Remote Sensing of Clouds and the Atmosphere XV; 782704 (2010) https://doi.org/10.1117/12.866477
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

A solution based on a Kalman filter to trace the evolution of the atmospheric boundary layer (ABL) sensed by an elastic backscatter 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 without need to resort to long time averages or rangesmoothing techniques, as well as to pave the way for an automated detection method. First EKF results based on synthetic lidar profiles are presented and compared with a typical least-squares inversion for different SNR scenarios.

Citation Download Citation

Sergio Tomás, Francesc Rocadenbosch, and Michaël Sicard "Atmospheric boundary-layer height estimation by adaptive Kalman filtering of lidar data", Proc. SPIE 7827, Remote Sensing of Clouds and the Atmosphere XV, 782704 (11 October 2010); https://doi.org/10.1117/12.866477

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE