Publications

Details of a technical study that was part of a larger investigation assessing terminal weather services impacts of decommissioning the Terminal Doppler Weather Radar (TDWR) are presented. Effects on two key areas for safety and delay-reduction benefits are examined: low-altitude wind shear visibility and the Integrated Terminal Weather System (ITWS) Terminals Winds (TWINS) product. It is concluded that the information conted provided by the TDWR cannot, in general, be effectively replaced by other candidate radar systems such as the Airport Surveillance Radar (ASR-9) equipped with a Weather Systems Processor (WSP) or the Next Generation Weather Radar (NEXRAD).

Current U.S. weather and aircraft surveillance radar networks vary in age from 10 to more than 40 years. Ongoing sustainment and upgrade programs can keep these operating in the near to mid term, but the responsible agencies (FAA, NWS and DoD/DHS) recognize that large-scale replacement activities must begin during the next decade. In addition, these agencies are re-evaluating their operational requirements for radar surveillance. FAA has announced that next generation air traffic control (ATC) will be based on Automatic Dependent Surveillance - Broadcast (ADS-B) (Scardina, 2002) rather than current primary and secondary radars. ADS-B, however, requires verification and back-up services which could be provided by retaining or replacing primary ATC radars.

San Francisco International Airport (SFO) experiences frequent low ceiling conditions during the summer season due to marine stratus clouds. Stratus in the approach zone prevents dual approaches to the airport??s closely spaced parallel runways, effectively reducing arrival capacity by half. The stratus typically behaves on a daily cycle, with dissipation occurring during the hours following sunrise. Often the low ceiling conditions persist throughout the morning hours and interfere with the high rate of air traffic scheduled into SFO from mid-morning to early afternoon. Air traffic managers require accurate forecasts of clearing time to efficiently administer Ground Delay Programs (GDPs) to match the rate of arriving aircraft with expected capacity. The San Francisco Marine Stratus Forecast System was developed as a tool for anticipating the time of stratus clearing. The system relies on field-deployed sensors as well as routinely available regional surface observations and satellite data from the Geostationary Operational Environmental Satellite (GOES-West). Data are collected, processed, and input to a suite of forecast models to predict the time that the approach zone will be sufficiently clear to perform dual approaches. Data observations and model forecasts are delivered to users on an interactive display accessible via the Internet. The system prototype was developed under the sponsorship of the FAA Aviation Weather Research Program (AWRP). MIT Lincoln Laboratory served as technical lead for the project, in collaboration with San Jose State University, the University of Quebec at Montreal, and the Center Weather Service Unit (CWSU) at the Oakland Air Route Traffic Control Center (ARTCC). The National Weather Service (NWS), under the direction of the NWS Forecast Office in Monterey, assumed responsibility for operation and maintenance of the system following technical transfer in 2004. This document was compiled as a resource to support continuing system operation and maintenance.

The Air Traffic Control (ATC) productivity benefits attributed to the Corridor Integrated Weather System (CIWS) were assessed using real-time observations of CIWS product usage during three multi-day thunderstorm events in 2005 at eight U.S. Air Route Traffic Control Centers (ARTCCs). CIWS improved ATC productivity by: reducing the time required to develop, coordinate, and implement weather impact mitigation plans; increasing the number of safety and capacity-enhancing plans that were executed (e.g., more efficient, proactive rerouting and greater ability to keep routes open; [and] assisting with FAA staffing decisions. Time savings per consecutive weather day for Traffic Management Coordinators (TMCs) in an ARTCC typically were 20-95 minutes. The overall frequency of capacity-enhancing decisions increased by 177% relative to the CIWS benefits study conducted in 2003. The annual CIWS delay savings are in excess of 92,000 hours. Corresponding airline direct operations cost (DOC) savings exceeded $94M and passenger value of time (PVT) savings exceeded $201M. Annual jet fuel savings exceeded 11M gallons. The ability of the Cleveland ARTCC to develop and execute weather impact mitigation plans improved significantly (e.g., by 50-80%) when CIWS products were available to Area Supervisors as well as to the TMCs.

In an era of significant federal government budget austerity for civil aviation operations, it has become essential to improve Air Traffic Control (ATC) productivity. This report summarizes the results of an exploratory field measurement program conducted during summer 2005 to assess ATC productivity benefits of the Corridor Integrated Weather System (CIWS). Real-time observations of CIWS product usage during multi-day thunderstorm events were carried out at eight U.S. Air Route Traffic Control Centers (ARTCC). The real time observations data were used in conjunction with specific in-depth case study analyses to assess the CIWS productivity enhancements associated with convective weather impact mitigation plan development and implementation. Comparisons of ARTCC operations between facilities with and without access to CIWS were alos made to further identify CIWS contributions to improved ATC productivity.

This paper is a concept study for possible future utilization of active electronically scanned radars to provide weather and aircraft surveillance functions in U.S. airspace. If critical technology costs decrease sufficiently, multi-function phased array radars might prove to be a cost effective alternative to current surveillance radars, since the number of required radars would be reduced, and maintenance and logistics infrastructure would be consolidated. A radar configuration that provides terminal-area and long-range aircraft surveillance and weather measurement capability is described and a radar network design that replicates or exceeds current airspace coverage is presented. Key technology issues are examined, including transmit-receive elements, overlapped sub-arrays, the digital beamformer, and weather and aircraft post-processing algorithms. We conclude by discussing implications relative to future national weather and non-cooperative aircraft target surveillance needs. The U.S. Government currently operates four separate ground based surveillance radar networks supporting public and aviation-specific weather warnings and advisories, and primary or "skin paint" aircraft surveillance. The separate networks are: (i) The 10-cm wavelength NEXRAD or WSR88-D (Serafin and Wilson, 2000) national-scale weather radar network. This is managed jointly by the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the Department of Defense (DoD). (ii) The 5-cm wavelength Terminal Doppler Weather Radars (TDWR) (Evans and Turnbull, 1989) deployed at large airports to detect low-altitude wind-shear phenomena. (iii) The 10-cm wavelength Airport Surveillance Radars (ASR-9 and ASR-11) (Taylor and Brunins, 1985) providing terminal area primary aircraft surveillance and vertically averaged precipitation reflectivity measurements. (iv) The 30-cm wavelength Air Route Surveillance Radars (ARSR-1, 2, 3 and 4) (Weber, 2005) that provide national-scale primary aircraft surveillance. The latter three networks are managed primarily by the FAA, although the DoD operates a limited number of ASRs and has partial responsibility for maintenance of the ARSR network. In total there are 513 of these radars in the contiguous United States (CONUS), Alaska, and Hawaii. The agencies that maintain these radars conduct various "life extension" activities that are projected to extend their operational life to approximately 2020. At this time, there are no defined programs to acquire replacement radars. The NWS and FAA have recently begun exploratory research on the capabilities and technology issues related to the use of multi-function phased array radar (MPAR) as a possible replacement approach. A key concept is that the MPAR network could provide both weather and primary aircraft surveillance, thereby reducing the total number of ground-based radars. In addition, MPAR surveillance capabilities would likely exceed those of current operational radars, for example, by providing more frequent weather volume scans and by providing vertical resolution and height estimates for primary aircraft targets. Table 1 summarizes the capabilities of current U.S. surveillance radars. These are approximations and do not fully capture variations in capability as a function, for example, of range or operating mode. A key observation is that significant variation in update rates between the aircraft and weather surveillance functions are currently achieved by using fundamentally different antenna patterns--low-gain vertical "fan beams" for aircraft surveillance that are scanned in azimuth only, versus high-gain weather radar "pencil beams" that are scanned volumetrically at much lower update rates. Note also that, if expressed in consistent units, the power-aperture products of the weather radars significantly exceed those of the ASRs and ARSRs. In the next section, we present a concept design for MPAR and demonstrate that it can simultaneously provide the measurement capabilities summarized in Table 1. In Section 3 we present an MPAR network concept that duplicates the airspace coverage provided by the current multiple radar networks. Section 4 discusses technology issues and associated cost considerations. We conclude in Section 5 by discussing implications relative to future national weather and non-cooperative aircraft target surveillance needs.

A radar data quality control (QC) system is being developed for the real-time, continuously updateable NOWCAST system at the Naval Research Laboratory (NRL-NOWCAST) in Monterey, California. NRL has developed its own new radar QC algorithms, and is also working with the MIT Lincoln Laboratory (MIT LL), the National Center for Atmospheric Research (NCAR), the National Severe Storms Laboratory and the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma (NSSL-OU) to obtain, adapt, integrate, test and install various types of recently-developed radar QC algorithms for use with NRL-NOWCAST. These algorithms work with volume scans of full-resolution Doppler radar data. Radar data QC can be divided into two categories: echo classification (EC) and calibration. New EC algorithms have recently demonstrated substantial success at separating the radar echoes of precipitation from other echo types, such as noise, normal propagation (NP) and anomalous propagation (AP) ground clutter, sea clutter, insects/clear-air, birds, second-trip echoes, and constant power function (CPF) artifacts. Radar data calibration methods assess the accuracy of both the data values and data coordinates. One calibration issue is aliased radial velocity data from precipitation and insect/clear-air returns, which if correctly de-aliased, afford the opportunity to estimate winds. Another calibration issue of concern to NRL is the processing of radar data from mobile platforms, such as US Navy ships. This processing requires corrections to the radial velocity data and the data-coordinates for the motion of the platform, as well as corrections for the altitude of the data coordinates due to the AP of the radar beam that frequently occurs within surface and evaporation ducts of the marine atmosphere. The goal of this work is to test the performance of the most current and promising radar data QC algorithms on archived data sets, both from ground- and sea-based radars, in order to determine the optimal combination for future real-time use within NRL-NOWCAST. NRL-NOWCAST currently ingests full-resolution Doppler radar data from both the Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the US Department of Defense (DoD) Supplemental Weather Radar (SWR) at the Naval Air Station (NAS) in Fallon, NV. Various products are then created from these data for NRL-NOWCAST display. The radar data are also ingested into the COAMPS-0S (R) (Geiszler et al. 2004) data assimilation system at NRL. Figure 1 shows a flow chart that summarizes the processing stages and uses of radar data at NRL. Figure 2 shows an example of the NRL-NOWCAST demonstration site currently set up at Fallon, where the specific products displayed are only a few from a large list that may be chosen by the forecasters at the NAS. This paper presents a brief overview of the concepts behind the various EC and radial velocity de-aliasing algorithms under consideration. Test results from an NRL algorithm-testing platform will also be presented along with some previously published test results from the authors. Additional test results from the platform will be presented at the conference. Methods to address data-value and data coordinate calibration problems associated with Doppler radars onboard US Navy ships are currently being studied; a discussion on future work in this area will be outlined.

Multiple pulse-repetition interval (multi-PRI) transmission is part of an adaptive signal transmission and processing algorithm being developed to combat range-velocity (RV) ambiguity for the Terminal Doppler Weather Radar (TDWR). In Part I of this two-part paper, an adaptive clutter filtering procedure that yields low biases in the moments estimates was presented. In this part, algorithms for simultaneously providing range-overlay protection and velocity dealiasing using multi-PRI signal transmission and processing are presented. The effectiveness of the multi-PRI RV ambiguity mitigation scheme is demonstrated using simulated and real weather radar data, with excellent results. Combined with the adaptive clutter filter, this technique will be used within the larger context of an adaptive signal transmission and processing scheme in which phase-code processing will be a complementary alternative.

This paper describes the Tactical 0-2 hour Convective Weather Forecast (CWF) algorithm developed by the MIT LL for the FAA. We will address the algorithm and focus on the key scientific developments. Future directions will also be discussed.

Thousands of traffic and safety monitoring cameras are deployed or are being deployed all across the country and throughout the world. These cameras serve a wide range of uses from monitoring building access to adjusting timing cycles of traffic lights at clogged intersections. Currently, these images are typically viewed on a wall of monitors in a traffic operations or security center where observers manually monitor potentially hazardous or congested conditions and notify the appropriate authorities. However, the proliferation of camera imagery taxes the ability of the manual observer to track and respond to all incidents. In addition, the images contain a wealth of information, including visibility, precipitation type, road conditions, camera outages, etc., that often goes unreported because these variables are not always critical or go undetected. Camera deployments continue to expand and the corresponding rapid increases in both the volume and complexity of camera imagery demand that automated algorithms be developed to condense the discernable information into a form that can be easily used operationally by users. MIT Lincoln Laboratory (MIT/LL) under funding from the Federal Highway Administration (FHWA) is investigating new techniques to extract weather and road condition parameters from standard traffic camera imagery. To date, work has focused on developing an algorithm to measure atmospheric visibility and prove the algorithm concept. The initial algorithm examines the natural edges within the image (the horizon, tree lines, roadways, permanent buildings, etc) and performs a comparison of each image with a historical composite image. This comparison enables the system to determine the visibility in the direction of the sensor by detecting which edges are visible and which are not. A primary goal of the automated camera imagery feature extraction system is to ingest digital imagery with limited specific site information such as location, height, angle, and visual extent, thereby making the system easier for users to implement. There are, of course, many challenges in providing a reliable automated estimate of the visibility under all conditions (camera blockage/movement, dirt/raindrops on lens, etc) and the system attempts to compensate for these situations. This paper details the work-to-date on the visibility algorithm and defines a path for further development of the overall system.