This paper presents the results of a study to quantify the performance of Weather Avoidance Fields in predicting the operational impact of convective weather on en route airspace. The Convective Weather Avoidance Model identifies regions of convective weather that pilots are likely to avoid based upon an examination of the planned and actual flight trajectories in regions of weather impact. From this model and a forecast of convective weather from the Corridor Integrated Weather System a probabilistic Weather Avoidance Field can be provided to automated decision support systems of the future impact of weather on the air traffic control system. This paper will present three alternative spatial filters for the Convective Weather Avoidance Model, quantify their performance, address deficiencies in performance, and suggest potential improvements by looking at the ATC environment and common situational awareness between the cockpit and air traffic control.

This paper describes a method to determine the accuracy of the Convective Weather AvoidanceModel which predicts the likelihood that pilots will deviate away from specific areas of convective activity. Visual inspection with a reduced data set helped refine the algorithms used in the verification and offered some preliminary results of the model's accuracy in today's airspace. This model has some explanatory power in predicting regions of airspace where pilots are willing to deviate or fly through. In some instances, pilots appeared not to make an early decision to deviate around convective weather and continued on course as the region appeared more passable when they reached it. In other instances, pilots skirted the edges of regions where the model expected pilots avoid. This behavior suggests edge areas of those model regions were more passable and the convection in that region was not uniform in intensity.

The rapid advancement of commercial wearable sensing technologies provides an unprecedented opportunity to gather information that improves warfighter performance during military activities and to detect the onset of illness (such as COVID‐19) through surveillance. However, the promise of improved performance and illness prevention through these technologies remains unfulfilled because of the complexity of guaranteeing that technology development outside of the standard military acquisition cycle will meet military requirements. The key to meeting this challenge is to facilitate coordination among R&D efforts, commercially developed products, and military acquisition strategies. To address this, we developed an MBSE architecture and methodology for validating independently developed wearable system designs against military end‐user needs. This methodology includes developing a conceptual framework, a model library, and a capability needs matrix that maps defense mission characteristics to physiological states and product design implementations. This architecture allows military stakeholders to determine where capability gaps or opportunities for wider application of commercial technologies exist, thus providing a bridge between externally developed wearable sensing technologies and military acquisition strategies.

The effective management of traffic flows during convective weather events in congested air space requires decision support tools that can translate weather information into anticipated air traffic operational impact. In recent years, MIT Lincoln Laboratory has been maturing the Convective Weather Avoidance Model (CWAM) to correlate pilot behavior in the enroute airspace with observable weather parameters from convective weather forecast systems. This paper evaluates the adaptation of the CWAM to terminal airspace with a focus on arrival decision making. The model is trained on data from five days of terminal convective weather impacts. The performance of the model is evaluated on an independent dataset consisting of six days of convective weather over a variety of terminal areas. Model performance in different terminal areas is discussed and the sensitivity of prediction accuracy to weather forecast horizon is presented.