Summary
The effective management of convective weather in congested air space requires decision support tools that can translate weather information available to air traffic managers into anticipated impact on air traffic operations. The Convective Weather Avoidance Model (CWAM) has been under development at Lincoln Lab under sponsorship of NASA to develop a correlation between pilot behavior and observable weather parameters. To date, the observable weather parameters have been the Corridor Integrated Weather System (CIWS) high resolution Vertically Integrated Liquid (VIL) precipitation map and the CIWS Echo Top product. The CWAM was dependent upon a crude model to define pilot deviations based upon finding weather encounters and then comparing the distance between the planned and actual flight trajectories. Due to a large number of false deviations from this crude model, a significant amount of hand editing was required to use the database. This paper will focus on two areas of work to improve the performance of the enroute convective weather avoidance models. First, an improved automated algorithm to detect weather-related deviations that significantly reduces the percentage of false deviation detections will be presented. This new model includes additional information on each deviation, including the location the decision was made to deviate. The additional information extracted from this algorithm can be used to evaluate the conditions at the decision time which may impact the severity of weather pilots are willing to penetrate. The new deviation detection algorithm has also reduced the amount of hand editing required by removing short cuts taken to reduce the flight time, deviations that occur well past the decision time, and non-weather related reroutes. The second focus of this paper will be the comparison of three different convective weather avoidance models that have been proposed, based upon the analysis of an expanded database of flight deviations. Six weather impact days from 2007 and 2008 have been added to the existing case set from 2006, tripling the number of flight trajectories that can be used in validating the models. In addition to validating the existing CWAM, we will look at additional parameters that may improve the performance of the CWAM.