Publications

The Weather Systems Processor (WSP) is an add-on system to the Airport Surveillance Radar-9 (ASR-9) that generates wind shear detection and storm tracking products for the terminal airspace. As the original system ages and pre-purchased replacement parts in the depot are used up, it becomes increasingly problematic to procure hardware components for repairs. Thus, a technical refresh is needed to sustain WSP operations into the future. This phase of the project targets the intermediate frequency digital receiver, the radar interface module, and the digital signal processor for replacement by updated hardware platforms. At the same time, the increase in computational capability allows for an upgrade in the signal processing algorithm, which will lead to data quality improvements.

One of the largest sources of error in the current automated convective weather forecast systems is due to its inability to accurately account for new convective storm development. In many situations the initiation of new convection is preceded by low altitude convergence in the horizontal winds. These regions of low altitude convergence, often referred to as boundaries, are typically associated with synoptic scale fronts, drylines, and thunderstorm outflows. Gridded wind analyses that utilize Doppler weather radar, surface, and aircraft measurements are one of the best sources of low altitude winds that can be used to identify wind boundaries over large domains. This study summarizes the preliminary results of a study which examined the feasibility of using gridded wind analyses from operational wind analysis systems to make automated detections of wind boundaries. The analysis focused on two operational wind analysis systems both capable of producing high update, and high spatial resolution wind analyses over a domain that covers the eastern half of the Continental United Sates (CONUS), the Space Time Mesoscale Analysis System (STMAS) and the Corridor Boundary layer wind analysis system (CBOUND). Wind analyses from both systems were first processed with a Lagrangian temporal filter and then passed through an automated boundary detection algorithm based on the Terminal Doppler Weather Radar (TDWR) Machine Intelligent Gust Front Algorithm (MIGFA). The results indicate that the temporal filter improves the boundary signal to noise ratio such that it is technically feasible to make fully automated boundary detections with image processing techniques.

The CSKETCH image processing library is a collection of C++ classes and global functions which comprise a development environment for meteorological algorithms. The library is best thought of as a 'tool-kit' which contains many standard mathematical and signal processing functions often employed in the analysis of weather radar data. A tutorial-style introduction to the library is given, complete with many examples of class and global function usage. Included is an in-depth look at the main class of the library, the SKArray class, which is a templatized and encapsulated class for storing numerical data arrays of one, two, or three dimensions. Following the tutorial is a complete reference for the library which describes all publicly-available class data members and class member functions, as well as all global functions included in the library.

Thunderstorms often generate gust fronts that can have significant impact on airport operations. Unanticipated changes in wind speed and direction are of concern from an air traffic safety viewpoint (hazardous wind shear) as well as from an airport planning point of view (runway configuration). Automated gust front detection is viewed by FAA and the air traffic community as an important component of current and future hazardous weather detection systems including the Terminal Doppler Weather Radar (TDWR), ASR-9 with Weather Systems Processor (ASR-9 WSP), and the Integrated Terminal Weather Systems (ITWS) for which TDWR is a principal sensor. In cooperation with the FAA, Lincoln Laboratory has successfully developed and tested a real-time Machine Intelligent Gust Front Algorithm (MIGFA) for use with Doppler weather radars. This algorithm resulted from the successful fusion of two complementing technologies developed at Lincoln Laboratory: computer vision/machine intelligence techniques originally developed for automated target recognition, and automated product-oriented weather radar data processing. Using these techniques, a version of MIGFA designed for use with TDWR has demonstrated substantial improvement over the existing TDWR gust front algorithm, detecting more and greater extents of gust fronts with fewer false alarms. MIGFA is slated to eventually replace the existing TDWR gust front algorithm and will be used as the gust front algorithm for the planned ITWS and ASR-9 WSP systems. A brief overview of techniques used by MIGFA to identify and track gust fronts will bre presented in this paper. More details, along with recent detection performance results, can be obtained from prior publications. However, detection and tracking of a gust front is only part of the task. Once the location of a gust front has been determined, the associated wind shear estimate and wind shift forecast must be computed. Several issues arises. For example, a gust front can be tens of kilometers in length, with outflow strength and contrasting environmental winds varying considerably along its length. Where along the front should the wind shear analysis be performed? Also, for airport planning purposes, air traffic controllers and managers need to plan runway configuration based on winds that may change suddenly when a gust front moves over the airport. Depending on the nature of the gust front, some of these winds are relatively transient while others are more persistent. How should the wind shift advisory produced by the algorithm take this into account? MIGFA uses a consensus derived from a variety of estimation techniques as a robust means of generating wind shear and wind shift estimates for detected gust fronts. These techniques, and some of their limitations, are discussed. Results of comparisons of MIGFA-generated wind shear and wind shift reports against observations are also presented. The paper concludes by outlining planned enhancements to incorporate additional information available under ITWS that should further improve the quality of MIGFA's wind shear and wind shift forecasts.

Good estimates of storm motion are essential to improved air traffic control operations during times of inclement weather. Automating such a service is a challenge, however, because meteorological phenomena exist as complex distributed systems that exhibit motion across a wide spectrum of scales. Even when viewed from a fixed perspective, these evolving dynamic systems can test the extent of our definition of motion, as well as any attempt at automated tracking of this motion. Image-based motion detection and processing appear to provide the best route toward robust performance of an automated tracking system.