Convective Weather Avoidance Modeling in Low-Altitude Airspace

Thunderstorms are a leading cause of delay in the National Airspace System (NAS), and significant research has been conducted to predict the areas pilots will avoid during a storm. An example of such research is the Convective Weather Avoidance Model (CWAM), which provides the likelihood of pilot deviation due to convective weather in a given area. This paper extends the scope of CWAM to include low-altitude flights, which typically occur below the tops of convective weather and have slightly different operational constraints. In general, the set of low-altitude flights includes short-hop routes and low-altitude escape routes used to reduce the impact of convective weather in the terminal area. This paper will discuss the classification procedure, present the performance of low-altitude CWAM on observed and forecasted weather, analyze areas of poor performance, and suggest potential improvements to the model. I. Introduction ONVECTIVE weather is a significant impediment to effective and efficient Air Traffic Management (ATM) decisions, and sometimes results in unnecessary delays to the National Airspace System (NAS). In the NAS, 70% of delays are caused by weather, and of those delays, 60% are specifically accounted for by convective weather [1]. Currently, rerouting decisions made by air traffic managers are aided by weather products such as the Corridor Integrated Weather System (CIWS) and the National Convective Weather Forecast (NCWF) [2, 3]. In a Next Generation ATM system, decision support tools such as the Route Availability Planning Tool (RAPT) will mitigate weather-induced delays by supplementing the situational awareness of an air traffic manager with a forecast of the availability of specific flight routes [4]. RAPT is based on the Convective Weather Avoidance Model (CWAM), which is a probabilistic model of pilot decision making in the presence of convective weather [5]. CWAM is a tool originally developed for the en route flight regime to predict pilot deviation decisions by correlating in-flight deviations of aircraft to the weather features they encounter. The model is based on a database comprised of the deviation decision of each flight and weather statistics along each route, which are obtained from CIWS. Pattern classification experiments on the en route CWAM database show that the most descriptive predictors for deviation are related to echo top height, where the most descriptive is the difference in altitude between the aircraft and the echo top height [5]. In the terminal area, deviations are predicted with a different set of features. Several studies of the Dallas and Memphis areas using weather information from the Integrated Terminal Weather System (ITWS) show that deviation decisions are closely related to the radar intensity of the storm and the proximity of the aircraft to the airport [6, 7]. This paper presents the development of a low-altitude version of CWAM which is based on a database composed of weather encounters that occur during level flight between FL100 and FL240. This model is applicable to jet traffic that uses low altitude air routes to „escape‟ from terminal areas when weather or volume congestion impacts lead to constraints on high-altitude airspace, or to low-altitude flight by regional jets on „short hop‟ routes. Such traffic is common in major metroplex airspaces. In this analysis, flight trajectories are obtained from the Enhanced Traffic Management System (ETMS) database, and weather data are acquired from CIWS for 23 convective weather days across two geographical regions (Chicago and New York). A Gaussian classifier is used to determine a set of deviation predictors and the results are tested on observed and forecasted data. The predictor performance is compared to the existing terminal departure CWAM used in RAPT, and the differences are discussed

An Approach to Verify a Model for Translating Convective Weather Information to Air Traffic Management Impact

‡This paper describes a method to determine the accuracy of the Convective Weather Avoidance Model 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

Evaluation of the Convective Weather Avoidance Model for Arrival Traffic

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. I. Introduction future air traffic system capable of predicting convective weather impacts and proactively issuing TMIs will more effectively use the available airspace, and in turn mitigate the effect of convective weather on the system. The Convective Weather Avoidance Model (CWAM) is a probabilistic model of pilot decision making in the presence of convective weather. CWAM is based on the correlation of spatially filtered weather observations with trajectories of aircraft that penetrated or avoided areas of convective weather in the en route flight regime [1]. The output of the en route CWAM is a three-dimensional {cloud tops, flight altitude, precipitation intensity} Weather Avoidance Field (WAF) that provides the likelihood that a pilot will deviate at a specific position and time given the current and forecasted weather. Outside of the en route phase (e.g. during departure and arrival), aircraft are commonly below the tops of most convection and are subject to different decision mechanisms, both of which are not modeled in the original CWAM. Therefore, in order to model impacts over an entire flight trajectory, CWAM should be adapted to include low-altitude flight phases such as arrival and departure [2]. This paper presents an evaluation of the adaptation of CWAM for arrival operations. Arrival CWAM is trained on approximately 11,000 flights and 1,900 terminal weather encounters over five convective weather days [3]. The training database includes multiple types of weather avoidance decisions that occur during arrival operations to four major metroplex areas (ORD, DFW, CLT, DEN). The decisions types distinguish between strategic and tactical time horizons and encompass both pilot and air traffic management decisions. Additionally, unlike pilots in en route airspace who may have an option to fly at higher altitudes over storms, pilots in arrival airspace are constrained to follow descending trajectories that are typically below the cloud tops. For this reason, the output of the arrival CWAM is a two-dimensional WAF {precipitation intensity, cloud tops}. The performance of arrival CWAM is evaluated by an independent dataset, where the sensitivity of the model to terminal airspace structure and weather forecast horizon are investigated. The independent dataset contains weather decisions from six convective weather days in a variety of terminal areas (ORD, DFW, DEN, CLT, BOS, JFK/LGA/EWR, DCA/IAD). The most descriptive features of pilot avoidance of convective weather are precipitation intensity and storm height, where a 4 km spatial filter on the 90 th percentile value of each feature corresponds to the best tradeoff between probability of detection and false alarm rate.The performance of the mode

Air Traffic Decision Analysis During Convective Weather Events in Arrival Airspace

Decision making during convective weather events in the terminal area is shared among pilots and air traffic management, where uninformed decisions can result in wide-spread cascading delays with high-level impacts. Future traffic management systems capable of predicting terminal impacts will mitigate these unnecessary delays; however in order to realize this vision, it is important to understand the decision mechanisms behind convective weather avoidance. This paper utilizes an arrival adaptation of the Convective Weather Avoidance Model (CWAM) to investigate the catalysts for arrival traffic management decision making. The analysis is broken down by category of terminal airspace structure in addition to the type of decision. The results show that pilot behavior in convective weather is heavily dependent on the terminal airspace structure. In addition, pilot and air traffic management decisions in convective weather can be discriminated with large-scale weather features. I. Introductio