Dissertation Defense - Amy Tal Rose-Tejwani - Geography and Geoinformation Science

PhD Candidate: Amy Tal Rose-Tejwani
PhD of Science, Earth Systems and Geoinformation Sciences
Department of Geography and Geoinformation Science 

Location: Exploratory Hall, 2312 or email arose20@gmu.edu for the virtual meeting link

Dissertation Title: Validated Framework for the Forecast of Upper Tropospheric Ice Supersaturation

Dissertation Chair: Dr. Donglian Sun (GMU, Geography and Geoinformation Science) 

Committee Members: 
Dr. Edward Oughton (GMU, Geography and Geoinformation Science)
Dr. Ronald Resmini (GMU, Geography and Geoinformation Science)
Dr. Lance Sherry (GMU, Department of Systems Engineering and Operations Research) 

Abstract:
Contrails form as a result of water vapor bonding with soot emitted from jet engines at cruise altitudes, leading to ice crystal formation in specific atmospheric conditions known as Ice Supersaturated Regions (ISSRs). These regions, characterized by thin atmospheric layers averaging 3,000 feet in vertical depth, shift geographically with airmass movements. Contrails are estimated to contribute approximately 2% to the total anthropogenic warming of the Earth.

Researchers have developed simulation models to estimate the frequency, duration, and spatial distribution of contrails, as well as to estimate their impact on global warming. Additionally, some companies now provide services to forecast the location of ISSRs, offering guidance to airlines and operators on adjusting flight paths to avoid these regions and reduce contrail formation. Other researchers have identified issues with the accuracy of data for predicting the timing and precise geographic positioning of ISSRs. These areas are difficult to accurately depict due to the coarse resolution of current models.

Manuscript 1 reviews 131 peer-reviewed papers wherein 14 databases were identified in the study of ISSRs. Of these 14 data sources, seven of them provided in-situ measurements and another seven are physics-based models. The papers identified inconsistencies across forecast databases, limitations in weather databases for contrail forecasting, and uncertainties in the estimates of relative humidity with respect to ice (RHi) in reliable contrail prediction. These issues have been identified as gaps and challenges in the forecasting of ISSRs that will need to be addressed to enable navigational avoidance and reduce the formation of contrails.

Manuscript 2 presents three identified atmospheric data sources, four parameters, and two equations to calculate derived parameters. Further analysis revealed differences in the temperature and readings of relative humidity with respect to water across the three databases, resulting in differences in the calculations of RHi, and the identification of ISSRs. Over an 18-month period in Sterling, Virginia, USA, the radiosonde data and two atmospheric forecast databases identified ISSR conditions on 44%, 57%, and 77% of days, respectively. Broken down by flight level between 30,000 and 39,999 feet in altitude, these differences are highlighted further. The forecast databases overestimated the presence of ISSRs compared to the radiosonde data. These findings underscore the variability inherent in atmospheric datasets and conversion methods, highlighting potential areas for refinement in ISSR forecasting, notably in the development of ensemble forecasts based on several atmospheric databases. The implications of these results and limitations of the study are discussed.

Manuscript 3 describes the analysis of the accuracy of predicting the location of ISSRs using Schmidt-Appleman Criteria (SAC) data from the atmospheric databases, compared to ground-based sky images from a specific location. Of the 593 days in which imagery was collected hourly, 78.58% imagery available to be used for contrail identification. Half of the days had images with at least one contrail. Using IGRA atmospheric data, SAC predictions for contrails were accurate 59% of the days. SAC predictions for no contrails were accurate 86% of the days. Using RAP and ERA5 data, SAC predictions for contrails were accurate 84% and 76% of the days, respectively. The SAC predictions for no contrails were accurate 60% and 32% of the days, respectively. The implications of these results, recommendations, and limitations of the study are discussed.