A foehn climatology of the McMurdo Dry Valleys of Antarctica using satellite remote sensing data

• Main Author: Datta, Rajasweta
• Format: Other/Unknown Material
• Language: English
• Published: University of Canterbury 2021
• Subjects: McMurdo Dry Valleys; Antarc*; Antarctica
• Online Access: https://hdl.handle.net/10092/102895; https://doi.org/10.26021/12029

This thesis investigates the McMurdo Dry Valley (MDV) foehn meteorology and its impact on the region's hydrology utilizing satellite remote sensing data and in-situ meteorological observations. It aims to establish Satellite Remote Sensing as a tool for studying the spatiotemporal variabilities in surface temperatures across MDV. The thesis utilizes Land Surface Temperature (LST) data from the MODIS sensor mounted atop Aqua and Terra satellites, along with the meteorological and stream discharge data from Automatic Weather Stations (AWS)s and stream gauges installed by Long Term Ecological Research (LTER) program. The research assesses the long-term (18 years: 2000-2017) summer meteorology recorded by the AWSs and investigates their inter and intra-valley variabilities across MDV. The thesis identifies the control of summer foehn-induced warming and incoming shortwave radiation (radiative warming) in influencing the valley floor temperatures along with the trends in the occurrence of strong foehn events that occur mostly during clear sky days. As the thesis works into understanding wider spatial scale changes in temperatures using satellite LST data, it checks the data for inconsistencies and evaluates its accuracy and sensitivity to register the day-to-day temperature changes in the valley floor. It recognizes satellite-derived LST to be a reliable dataset for studying the meteorology-induced temperature variations across the valleys. Over-dependence of foehn's study on in-situ measurement had been a major obstacle in comprehending the meteorological variations that are associated with foehn winds over a wider spatial scale. The study, therefore, developed a methodology that can not only detect foehn in MDV without AWS support but also identify the various degree of warming each region experienced during a foehn event. The methodology developed detected foehn events in MDV using long-term satellite LST data for summer and winter periods individually. Due to the independence of the methodology in using AWS data, ...