A probabilistic approach to measure aircraft conflict severity considering wind forecast uncertainty

An approach to statistically quantify the severity of aircraft conflicts subject to wind forecast uncertainty is presented. The conflicts are characterized by two indicators: conflict intensity and conflict probability, and the wind uncertainty is derived from Ensemble Weather Forecasts. The wind components are modeled as random variables, and are assumed to be distributed as four-parameter beta distributions. The conflict intensity is measured by the minimum distance between the aircraft, and the probability of conflict is obtained in terms of the probability density function of the minimum distance, which is obtained from the probability density functions of the wind components using the Probabilistic Transformation Method. The case of two en-route aircraft flying at constant altitude and subject to the same random wind is considered. Results are presented for different traffic configurations and a given wind forecast; in particular, a low-intensity and a high-intensity conflict scenarios are analyzed in detail. The results show that the conflict severity can be statistically quantified for any given scenario. The results are validated using the Monte Carlo method and a linear approximation. The applicability of the methodology presented is discussed. The context for this work is the development of decision support tools for conflict detection that integrate weather uncertainty information.