Weather has a strong impact on Air Traffic Management (ATM). Inefficient weather avoidance procedures and inaccurate prognosis lead to longer aircraft routes and, as a result, to fuel waste and increased negative environmental impact. A better integration of weather information into the operational ATM-system will ultimately improve the overall air traffic safety and efficiency. Covid-19 pandemics affected aviation severely, resulting in an unprecedented reduction of air traffic, and gave the opportunity to study the flight performance in non-congested scenarios. We investigated the historical flight and weather data from Stockholm Arlanda and Gothenburg Landvetter airports for the period of two years 2019 and 2020 and discovered noticeable inefficiencies and environmental performance degradation, which persisted despite significant reduction of traffic intensity in March 2020. This thesis proposes a methodology that allows to distinguish which factors have the highest impact on which aspects of arrival performance in horizontal and vertical dimensions.

Academic Excellence in ATM and UTM Research (AEAR) group operating within the Communications and Transport Systems (KTS) division in Linköping University (LIU), together with the Research and Development at Luftfartsverket (LFV, Swedish Air Navigation Service Provider (ANSP)) develops optimization techniques to support efficient decision-making for aviation authorities. In this thesis, we design probabilistic models, which take into account the influence of bad weather conditions on the solutions developed in the related project and integrate them into the corresponding optimization framework. Probabilistic models were applied to account for weather impact on Air Traffic Controller (ATCO) work in remote and conventional towers. The probabilistic weather products were used to obtain an ensemble of staffing solutions, from which the probability distributions of the number of necessary ATCOs were derived. The modelling is based on the techniques recently developed within several Single European Sky ATM Research (SESAR) projects addressing weather uncertainty challenges. The proposed solution was successfully tested using the historical flight and weather data from five airports in Sweden planned for remote operation in the future.

The paper focuses on the performance assessment of the arrival operations in Oslo Gardermoen airport implementing point merge (PM) procedures. We take a data-driven approach based on the open-source ADS-B data, and conduct a detailed performance assessment utilizing a diverse set of performance indicators, including newly developed metrics for better understanding of the PM specifics. The results of the performance evaluation indicate that the PM systems are currently underutilized in Oslo airport, and their increased usage may lead to the improved arrival performance, especially during the peak time periods.

Covid-19 pandemic affected aviation severely, resulting in unprecedented reduction of air traffic. While aviationis slowly re-gaining traffic volumes, we use the opportunity to study the arrival performance in the Terminal Maneuvering Area (TMA) in non-congested scenarios. Applying flight efficiency and environmental performance indicators (PIs) to the historical data of arrivals to Stockholm Arlanda airport, we discover noticeable inefficiencies, despite significant reduction of traffic intensity. We analyse the impact of such factors as weather and traffic intensity on arrival efficiency in isolated scenarios when only one factor dominates. Our analysis uncovers that weather has a stronger influence than congestion on vertical efficiency, while congestion affects both, but mostly lateral efficiency.

An important step towards improving the flight performance within Terminal Maneuvering Area (TMA) is the identification of the factors causing inefficiencies. Without knowing which exact factors have high impact on which performance indicators, it is difficult to identify which areas could be improved. In this work, we quantify the flight efficiency using average additional time in TMA, average time flown level and additional fuel consumption associated with the inefficient flight profiles. We apply statistical learning methods to assess the impact of different weather phenomena on the arrival flight efficiency, taking into account the current traffic situation. We utilize multiple data sources for obtaining both historical flight trajectories and historical weather measurements, which facilitates a comprehensive analysis of the variety of factors influencing TMA performance. We demonstrate our approach by identifying that wind gust and snow had the most significant impact on Stockholm Arlanda airport arrivals in 2018

We present an optimization framework for computing STARs that slowly change over time, while always avoiding a set of moving obstacles in TMA. The framework is applied to two types of obstacles: a drone intruder and hazardous weather. We demonstrate the output of our algorithms on synthesized drone intrusion incidents and real storm cells in Stockholm Arlanda terminal area.