The subject of this Diploma Thesis is the calibration process of the water vapor profile of the upgraded lidar system of the Laboratory of Atmospheric Physics (LAP), at the Department of Physics of the Aristotle University of Thessaloniki (AUTH), using meteorological radiosonde data and observations from the CIMEL sun photometer, as well as the comparison of these two calibration methods. The first chapter provides a brief overview of the lidar technique, the optical properties measured with a Raman lidar and the importance of water vapor (wv) detection. Furthermore, the different calibration techniques that are applied in THELISYS are analyzed, such as the Δ90° method and a calibration procedure for the water vapor channel. The second chapter presents a detailed description of Thessaloniki’s lidar system (THELISYS), whose measurements were used for this study, as well as the data processing procedure performed using the SULA algorithm. In addition, the datasets used to retrieve the water vapor profiles are described. Specifically, the radiosonde launched by the National Meteorological Service (HMS) at Thessaloniki Airport and the observations provided by the AERONET network. The third chapter outlines the study methodology, focusing on the calibration of the Raman lidar using radiosonde data and AERONET measurements. Finally, the fourth chapter, presents the results. Initially, the application of the calibration method based on radiosonde data is examined, with an analysis of the calibration factor at different ranges and then at ranges with the highest correlation. Then, the calibration method using AERONET is analyzed, with an estimation of the calibration factor derived from precipitable water vapor (PWV) measurements. Finally, a comparative evaluation of the two methods is carried out, in order to highlight their advantages and limitations.
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Ελένη Βασιλική Α. Μπουγδάνη
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Ελένη Βασιλική Α. Μπουγδάνη (Wed,) studied this question.
www.synapsesocial.com/papers/69d8946e6c1944d70ce055f2 — DOI: https://doi.org/10.26262/heal.auth.ir.371678