This deliverable provides a report on the assessment and optimization of MIPA (Morphological Image Processing Approach) algorithm1 for the retrieval of Atmospheric Boundary Layer Height (ABLH) from High Power Lidar (HPL) data. MIPA has been developed at CNR-IMAA in the framework of ACTRIS and it has been already tested on several HPL datasets showing, in general, quite good performance with respect to the traditional ABLH retrieval techniques. The main MIPA novelty consists in operating on timeseries of lidar profiles and not on single lidar profile (as most of lidar-based ABLH retrievals available in literature do). Consequently, the ABLHs retrieved by MIPA consider not only the vertical dynamic of single lidar profiles, but also the correlation among contiguous (in time) lidar observations. Moreover, as MIPA works mainly on vertical and time correlations of the input dataset, the results do not rely on absolute intensities but on the corresponding relative variations. This feature makes MIPA a robust algorithm and opens the possibility to extend its applicability to a large number of lidar systems with different characteristics in terms of Signal to Noise Ratio (SNR), detection and acquisition type.
Report on the optimization of morphological image processing approach algorithm for ABLH determination
Giuseppe D’Amico;Aldo Amodeo;Davide Amodio;Francesco Cardellicchio;Benedetto De Rosa;Nicola Gianluca Di Fiore;Ilaria Gandolfi;Aldo Giunta;Emilio Lapenna;Teresa Laurita;Fabrizio Marra;Michail Mytilinaios;Lucia Mona;Nikolaos Papagiannopoulos;Serena Trippetta;Marco Rosoldi;Donato Summa;Gemine Vivone;Canio Colangelo
2024
Abstract
This deliverable provides a report on the assessment and optimization of MIPA (Morphological Image Processing Approach) algorithm1 for the retrieval of Atmospheric Boundary Layer Height (ABLH) from High Power Lidar (HPL) data. MIPA has been developed at CNR-IMAA in the framework of ACTRIS and it has been already tested on several HPL datasets showing, in general, quite good performance with respect to the traditional ABLH retrieval techniques. The main MIPA novelty consists in operating on timeseries of lidar profiles and not on single lidar profile (as most of lidar-based ABLH retrievals available in literature do). Consequently, the ABLHs retrieved by MIPA consider not only the vertical dynamic of single lidar profiles, but also the correlation among contiguous (in time) lidar observations. Moreover, as MIPA works mainly on vertical and time correlations of the input dataset, the results do not rely on absolute intensities but on the corresponding relative variations. This feature makes MIPA a robust algorithm and opens the possibility to extend its applicability to a large number of lidar systems with different characteristics in terms of Signal to Noise Ratio (SNR), detection and acquisition type.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
