אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Atmospheric lidars are important remote sensing tools in aerosols and climate research. A pulsed time-of-flight lidar continuously samples vertical atmospheric profiles, through day and night, yielding a spatiotemporal atmospheric map. However, lidar analysis is challenged by low signal-to-noise ratios, sunlight interference, and need for frequent calibration. We advance lidar analysis. We develop a framework for simulating realistic spatiotemporal lidar data under diverse atmospheric and system conditions. This reveals limitations and flaws in standard processing pipelines. To counter that, we develop maximum likelihood estimation tailored to lidar. This significantly enhances lidar analysis, particularly during the daytime, enabling more frequent and accurate retrievals. Additionally, we develop lidar calibration based on learning, based on spatiotemporal meteorological and lidar data. This helps address low signal-to-noise ratios and yield keeps calibration more updated than the current operational approach. Ph.D. Under the supervision of Prof. Yoav Schechner.