Cloud identification and classification from ground-based and satellite sensors on the Antarctic plateau

Volonnino, Viviana (2023) Cloud identification and classification from ground-based and satellite sensors on the Antarctic plateau. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica del sistema terra [LM-DM270]
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Cloud identification from satellites is considerably challenging in polar environments due to the similar radiative properties of surface and ice clouds. CIC is a machine learning algorithm based on Principal Component Analysis that performs cloud detection and multi-scene classification. Assessment studies have already been conducted to evaluate the performances of the algorithm in multiple conditions. In Maestri et al. (2019b), CIC was applied to simulated radiance all over the globe, while Magurno et al. (2020) used measured airborne interferometric spectra and in Cossich et al. (2021) the algorithm was tested on downwelling radiance collected at Dome-C in Antarctica. CIC is applied to high spectrally resolved data taken from the ground and, for the first time, from satellites. Ground-based data are collected by the REFIR-PAD sensor, covering the far and mid-infrared part of the spectrum. Collocated satellite data are measured by IASI which collects upwelling radiance between 3.4 and 15.5 μm. The period under study spans from 2012 to 2020. CIC results applied to ground-measured spectra are compared to IASI and MODIS L2 cloud products. Large discrepancies between the classifications are observed, indicating an overestimation of the cloud occurrence in the case of IASI and an opposite result in MODIS. A verification is obtained using collocated ground-based LIDAR measurements, which are available for subsets of the REFIR-PAD radiances. Finally, the CIC algorithm is trained with a subset of IASI data collocated with REFIR-PAD measurements. The training sets are defined also with the help of the AVHRR on board of MetOp satellites. The AVHRR collocated measurements are used to evaluate the scene homogeneity in the satellite field of view. Statistical analyses are then performed on IASI spectra using the CIC classification. Results indicate a much better agreement with ground-based data, improving the cloud occurrence provided in IASI L2 products.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Volonnino, Viviana
Relatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Satellite data,Satellite products,machine learning,PCA,spectral radiance,radiative transfer,cloud detection,Antarctic cloud occurrence,remote sensing
Data di discussione della Tesi
16 Marzo 2023

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