Atmospheric boundary layer height monitoring using a kalman filter and backscatter lidar returns

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dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció
dc.contributor.author	Lange, Diego
dc.contributor.author	Tiana Alsina, Jordi
dc.contributor.author	Saeed, Umar
dc.contributor.author	Tomás Martinez, Sergio
dc.contributor.author	Rocadenbosch Burillo, Francisco
dc.date	2014-08-01
dc.identifier.citation	Lange, D. [et al.]. Atmospheric boundary layer height monitoring using a kalman filter and backscatter lidar returns. "IEEE transactions on geoscience and remote sensing", 01 Agost 2014, vol. 52, núm. 8, p. 4717-4728.
dc.identifier.citation	0196-2892
dc.identifier.citation	10.1109/TGRS.2013.2284110
dc.identifier.uri	http://hdl.handle.net/2117/23382
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://ieeeexplore.us/xpl/articleDetails.jsp?tp=&arnumber=6646215&searchWithin%3Dp_Authors%3A.QT.Saeed%2C+U..QT.%26pageNumber%3D2
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
dc.subject	Signal processing
dc.subject	Remote sensing
dc.subject	Kalman filtering
dc.subject	Adaptive kalman filtering
dc.subject	Laser radar
dc.subject	Remote sensing
dc.subject	Signal processing
dc.subject	Entrainment zone thickness
dc.subject	Mixed-layer
dc.subject	Depth
dc.subject	Top
dc.subject	Area
dc.subject	Tractament del senyal
dc.subject	Teledetecció
dc.subject	Kalman, Filtratge de
dc.title	Atmospheric boundary layer height monitoring using a kalman filter and backscatter lidar returns
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/article
dc.description.abstract	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.
dc.description.abstract	Peer Reviewed

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