Probabilistic Planning and Risk Evaluation Based on Ensemble Weather Forecasting

Abstract

Weather analysis and support plays an important role in mission planning and risk evaluation of aircraft and unmanned aerial vehicles. The impact of adverse weather on the aviation industry has been well documented. Although long-term efforts have made tremendous progress in this area, the current weather support tools rely on deterministic weather analysis that cannot meet the increasing requirements. A more flexible, accurate, and reliable probabilistic analysis is desirable for probabilistic aviation decision aid. To achieve this goal, this paper proposes an ensemble forecasting-based framework in which the probabilistic analysis of adverse weather, 3-D receding horizon field $\text{D}\ast $ mission planning, and mission risk evaluation are integrated for a robust and flexible decision aid. The innovations of the proposed system include advanced multiple parameter probabilistic data analysis for probabilities of adverse weather and its confidence analysis, planning, and risk evaluation. Real ensemble weather forecasting data are used to demonstrate the efficiency of the proposed approach. Note to Practitioners —This paper presents a comprehensive solution for integrating mesoscale ensemble weather forecasting in aviation aid, including probabilistic weather data analysis, mission (route) planning, and mission risk evaluation with simulations based on real data. This paper proposes a flexible and comprehensive solution that is based on real mesoscale ensemble weather forecasting data with two different variables, which can be easily extended and scaled to multiple variable cases. The analysis can integrate inputs from meteorologists, operators, and commanders, so that the knowledge of experts from different domains can be combined to arrive at the decision best for risk management of aircraft or systems. The proposed method is generic and can be applied to various applications, such as motion planning problems in the manufacturing process that are affected by multiple factors.