A study on the performances of different metrics of a machine learning cloud classificator

Donat, Federico (2023) A study on the performances of different metrics of a machine learning cloud classificator. [Laurea magistrale], Università di Bologna, Corso di Studio in Physics [LM-DM270]
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Detecting clouds and clouds properties is essential for meteorological research, climate modeling and weather forecasting. CIC (Cloud Identification and Classification) is a recently proposed, innovative machine learning algorithm adopted as the cloud identification code in the ESA Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) End2End simulator (FE2ES). CIC performs a classification by defining an eigenvectors-based Similarity Index that measures the information content brought into a Training Set when it is concatenated with a new observation. In this thesis work, a new metric called the eigenvalues-based Similarity Index is proposed and studied to quantify the change in information content. Additionally, a novel methodology is developed within the CIC algorithm framework, allowing the simultaneous utilization of multiple Similarity Indices. A cross-validation procedure is conducted using downwelling radiance spectra collected on the Antarctic Plateau to test and compare three configurations: classical eigenvectors-CIC, eigenvalues-CIC, and double-CIC. The double-Similarity-Index CIC demonstrates superior performance and is selected for the classification of a comprehensive dataset of spectra obtained by the REFIR-PAD instrument on the Antarctic Plateau from 2013 to 2020. The analysis yields statistically consistent results with previous studies. Finally, the double-CIC has been used for the challenging analysis of a large dataset, containing the simulated radiance of the Thermal Infra-Red Spectrometer (TIRS) of the PREFIRE mission. TIRS is characterized by only 64 wavenumber channels, and the synthetic data have global coverage. Very good cloud detection performances (more than 93% of clear and cloudy sky spectra correctly classified) are obtained in this study. This constitutes an important result that witnesses the classification power of CIC-like algorithms and their suitability for satellite missions.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Donat, Federico
Relatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Cloud detection,cloud classification,machine learning,forum,cic,infrared spectra
Data di discussione della Tesi
14 Luglio 2023

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