Publications

We consider the phenomenon of afterpulsing in avalanche photodiodes (APDs) operating in gated and free-running Geiger mode. An operational model of afterpulsing and other noise characteristics of APDs predicts the noise behavior observed in the free-running mode. We also use gated-mode data to investigate possible sources of afterpulsing in these devices. For 30-um-diam, 1.06-um-wavelength InGaAsP/InP APDs operated at 290 K and 4 V overbias, we obtained a dominant trap lifetime of td=0.32 us, a trap energy of 0.11 eV, and a baseline dark count rate 245 kHz.

The intelligibility of speech transmitted through low-rate coders is severely degraded when high levels of acoustic noise are present in the acoustic environment. Recent advances in nonacoustic sensors, including microwave radar, skin vibration, and bone conduction sensors, provide the exciting possibility of both glottal excitation and, more generally, vocal tract measurements that are relatively immune to acoustic disturbances and can supplement the acoustic speech waveform. We are currently investigating methods of combining the output of these sensors for use in low-rate encoding according to their capability in representing specific speech characteristics in different frequency bands. Nonacoustic sensors have the ability to reveal certain speech attributes lost in the noisy acoustic signal; for example, low-energy consonant voice bars, nasality, and glottalized excitation. By fusing nonacoustic low-frequency and pitch content with acoustic-microphone content, we have achieved significant intelligibility performance gains using the DRT across a variety of environments over the government standard 2400-bps MELPe coder. By fusing quantized high-band 4-to-8-kHz speech, requiring only an additional 116 bps, we obtain further DRT performance gains by exploiting the ear's insensitivity to fine spectral detail in this frequency region.

We have developed focal-plane arrays and laser-radar (ladar) imaging systems based on Geiger-mode avalanche photodiodes (APDs) integrated with high-speed all-digital CMOS timing circuits. A Geiger-mode APD produces a digital pulse upon detection of a single photon. This pulse is used to stop a fast digital counter in the pixel circuit, thereby measuring photon arrival time. This "photon-to-digital conversion" yields quantum-limited sensitivity and noiseless readout, enabling high-performance ladar systems. Previously reported focal planes, based on bump bonding or epoxy bonding the APDs to foundry chips, had coarse (100um) pixel spacing and 0.5ns timing quantization.

The optimization of traffic flows in highly congested airspace with rapidly varying convective weather is an extremely complex problem. Aviation weather systems such as the Corridor Integrated Weather System (CIWS) provide weather products and forecasts that aid en route traffic managers in making tactical routing decisions in convective weather, but traffic managers need automated decision support systems that integrate flight information, trajectory models and convective weather products to assist in developing and executing convective weather mitigation plans. A key element of an integrated ATM/wx decision support system is the ability to predict automatically when pilots in en route airspace will choose to deviate around convective weather and how far they will deviate from their planned path. The FAA Aeronautical Information Manual suggests that pilots avoid thunderstorms characterized by intense radar echo in en route airspace by at least 20 nautical miles (40 km). However, a recent study (Rhoda, et. al., 2002) of pilot behavior in both terminal and en route airspace near Memphis, TN suggested that pilots fly over high reflectivity cells in en route airspace and penetrate lower cells whose reflectivity is less than VIP level 3. Recent operational experience with CIWS supports the Rhoda findings (Robinson, et. al., 2004). This study presents initial results of research to develop a quantitative model that would predict when a pilot will deviate around convective weather in en route airspace. It also presents statistics that characterize hazard avoidance distances and weather penetrations. The results are based on the analysis of more than 800 flight trajectories through two Air Traffic Control (ATC) en route super-sectors (geographical regions that include several adjacent ATC en route sectors) on five days in the summer of 2003. One supersector from the Indianapolis Air Route Traffic Control Center (ZID ARTCC) encompassed southern Indiana, southwestern Ohio and northern Kentucky (ZID); the other, located in the Cleveland ARTCC (ZOB), included northern Ohio, along the southern shore of Lake Erie (ZOB). The weather encountered along the flight trajectories was characterized by the CIWS high-resolution precipitation (VIL) and radar echo tops mosaic (Klingle-Wilson and Evans, 2005) and NLDN lightning products. Flight trajectories were taken from the Enhanced Traffic Management System (ETMS).

We discuss two specific short term aviation weather forecasts - convection and ceiling - to illustrate the issues that arise in thinking about the overall decision support system, key users, and training needed to generate benefits. We also consider reducing weather-related fatal accidents. Second, what is the preexisting "baseline" of aviation forecasts/decision processes that already exists to address the user needs? In most cases, there are already various weather information sources that can be viewed as providing a short term forecast (e.g., a Center Weather Service Unit (CWSU) meteorologist, persistence, or animation loops of the past weather). How well do we understand how the "baseline" forecast and the associated user decision support system operate? How will the new forecast and its decision support compare? What are the training implications if the new forecast is rather different than the "baseline"? Third, how will we measure the change in system performance? For example, if the new forecast claims to help reduce delays and/or accidents, how will one address differences in the weather between the "before" and "after" time periods? How will one determine whether the new forecast is in fact the key factor, if there was a change? The paper concludes with some suggestions for development and testing of new aviation forecasts to improve safety and reduce delays.

The local airspace surrounding the San Francisco International Airport (SFO) is prone to regular occurrences of low ceiling conditions from May through October due to the intrusion of marine stratus along the Pacific coast. The low cloud conditions prohibit dual parallel landings of aircraft to the airport's closely spaced parallel runways, thus effectively reducing the arrival capacity by a factor of two. The behavior of marine stratus evolves on a daily cycle, filling the San Francisco Bay region overnight, and dissipating during the morning. 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. The result is a substantial number of delayed flights into the airport and a negative impact on the National Air Space (NAS). Air traffic managers face a continual challenge of anticipating available operating capacity so that the demand of incoming planes can be metered to match the availability of arrival slots.

The Federal Aviation Administration (FAA) is sponsoring a Terminal Ceiling and Visibility (C&V) initiative to provide automated C&V guidance to the air traffic managers for both tactical (0-2 hour) and strategic (3-12 hour) decision making. To meet these requirements, particularly in the strategic time frame, it will most likely be necessary for the C&V system to incorporate guidance from an explicit numerical weather prediction (NWP) model. If NWP forecasts are found to be suitable for this application, they will be used as the backbone of the terminal C&V forecast system. More details on the terminal area C&V forecast product development for the FAA can be found in Allan et al. (2004). Before these NWP forecast products can be used, it is necessary to first characterize their accuracy relative to operational air traffic control (ATC) requirements. This makes it possible to exploit observed strengths, avoid weaknesses, and facilitate a better utilization of NWP forecast products. This study provides an assessment tailored specifically to address the terminal C&V application. Consequently, the results represent forecast performance for relatively small geographic locations that for practical purposes can be considered point forecasts. It is our intention to answer four questions with this preliminary analysis: 1. How accurate are the NWP forecasts relative to the observational truth and a human generated forecast? 2. For the terminals of interest to this study (i.e. New York City Airports), are there any advantages to utilizing a non-hydrostatic mesoscale model run at horizontal resolutions of 3 km or less? 3. Do the NWP models exhibit forecast skill for non-traditional forecast metrics such as trends in C&V parameters and timings of threshold crossings associated with the onset and clearing of low ceiling and visibility conditions? 4. Are there obvious situations/conditions during which the NWP forecasts have more/less skill? In addition to a report on the NWP terminal ceiling and visibility forecast accuracy, we provide preliminary recommendations on the direction we feel this line of research should pursue, and where we see opportunities to utilize NWP forecasts in an automated terminal C&V decision guidance system. An ancillary goal of this study is to assemble the analysis software infrastructure required to quantitatively evaluate numerical forecast accuracy. We envision using these tools to develop and test modifications to the translation algorithms and techniques that will be necessary to integrate the NWP forecasts into the C&V guidance system. They will be instrumental in reducing the time required to make engineering turns during the upcoming development and implementation stages of this research.

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.

Air traffic congestion in the United States (US) National Airspace System (NAS) has increased significantly in the past ten years. This congestion has resulted in a rise of air traffic delays, which can cause massive monetary and human costs. When convective weather impacts jet routes and airport terminals, particularly within the most congested airspace sectors, it causes a reduction in traffic capacity that can lead to significant delays. In an effort to increase airspace capacity and reduce air traffic delays, the Massachusetts Institute of Technology Lincoln Laboratory (MIT LL), in collaboration with the National Center for Atmospheric Research (NCAR) and the National Oceanic and Atmospheric Administration (NOAA) are tasked by the Federal Aviation Administration (FAA) to provide aviation weather decision support tools for the air traffic management (ATM) community. To determine which weather products and data dissemination approaches will provide the greatest benefit in terms of increasing airspace capacity, MIT LL is performing ongoing marketing research analyses. The method consists of three primary steps (Ballentine 1994; Evans and Robinson 2005; Evans et al. 2003): 1) Study the system 2) Identify benefits 3) Prioritize opportunities In practice, the execution of these three steps is an iterative process. It is critical to understand how the air traffic system operates to assess the benefits of a weather product. For this reason many studies have been conducted by MIT LL where ATM users have been interviewed, use of decision support tools has been observed, and flight track data have been analyzed to extract the behavior of the pilot (Evans et al. 2003; Evans and Robinson 2005; Robinson et al. 2004; Rhoda et al. 2002; Allan et al. 2001; Bieringer et al. 1999; Rhoda and Pawlak 1999; Forman et al. 1999). These studies have provided valuable insight into how the NAS operates during weather impacts. Weather impacts on the air traffic system can be classified into three basic types: 1) Terminal impacts (≤ 5 nm) 2) En route impacts 3) Transition impacts (between Air Route Traffic Control Centers (ARTCC) sectors and terminal operations) Terminal impacts are those that occur in and around the airport, and are generally less than 5 nm from the runways. These impacts are small in dimension and occur at low altitude, and have been shown to be relatively insignificant to the overall delay problem (Evans et al. 2005). En route impacts occur within ARTCC's jet routes and sectors. These impacts can result in a route being totally or partially blocked and lead to a reduction in capacity. The en route impacts generally occur at high altitude. Transition impacts are those that occur within the zone between the ARTCC sectors and the terminal approaches. By understanding the system one can then identify elements or areas of opportunities that can be exploited to help solve the airspace capacity problem. Weber et al. (2005) identifies four key elements for maintaining capacity during convective weather events: 1) Forecasts of convective weather 2) Capacity models where weather is an input 3) Strategy tools for ATM with weather as an input 4) Airspace capacity enhancements Forman et al. (1999) studied the terminal impact problem and found that the ATM users required precipitation forecasts that were reliable, updated rapidly (5-6 minutes), had high resolution (1 km), short lead times (1-2 hours), and were issued with fine time steps (10-15 minutes). MIT LL used this information to refine its terminal convective weather forecast (TCWF) and received very positive feedback from ATM personnel (Hallowell et al. 1999). Subsequently, the precipitation forecast was extended out to 2-hourtime horizons and was provided to the traffic managers working in busy Midwest and Northeast ARTCCs as part of the Corridor Integrated Weather System (CIWS) (See Klingle-Wilson and Evans (2005) for a description of the CIWS product). However, it was quickly determined that the precipitation product alone was not sufficient for identifying usable en route airspace, since occasionally significant precipitation (≥ level 3)1 had relatively low storm tops (≤ 30 kft). Due to feedback from ATM users, MIT LL produced a high resolution (1 km) enhanced Echo Tops Mosaic weather product (Evans et al. 2003) that is used as a proxy for the cloud top height. Since the operational inception of the CIWS enhanced Echo Tops Mosaic product in August 2002, FAA and airline traffic managers have become acutely aware of the benefits of high-resolution storm top information for efficient en route air traffic control (ATC) operations. CIWS field use assessment campaigns in 2003 revealed significant benefits attributed to use of the Echo Tops Mosaic product (Robinson et al. 2004). During interviews, traffic managers explained that in the past, if an aircraft deviated around a storm in high-traffic airspace, jet routes were closed by default, since pilot behavior was the only easily assessable information available about three-dimensional storm structure. After the CIWS Echo Tops Mosaic was introduced, traffic managers were able to differentiate between isolated storm top concerns, which are easily handled by keeping routes open and absorbing occasional, local deviations, and significant high-topped storm events, which legitimately require route closures and reroutes. Post-event interviews in 2003 revealed that though FAA and airline users were very pleased with the availability and quality of the CIWS Echo Tops Mosaic product, they also needed to know both the past trend and predicted behavior of storm top heights. The CIWS Echo Tops Forecast (ETF) was introduced in May 2005 to meet some of the traffic management requests. This paper discusses the ETF product currently operational in CIWS. We will discuss the generation of the forecast algorithm and provide an initial assessment of the use of the ETF in the field.

A major concern in contemporary traffic flow management (TFM) is improving decision making when severe convective weather (Wx) impacts en route sectors throughout the National Airspace System (NAS). The FAA is currently seeking to reduce these convective weather delays through the use of multi-hour (e.g. 4 and 6 hour) Wx forecasts coupled with strategic planning by the FAA traffic flow managers and airline personnel to determine how en route traffic should be rerouted so as to avoid sector overloads and minimize the magnitude of the delays that occur [Huberdeau and Gentry (2004)]. One of the major challenges in the strategic planning process is the difficulty in converting the convective weather forecasts into forecasts of en route sector capacity. In this study, we explore the development of a model that can be combined with forecast validation data to translate probabilistic convective weather (Wx) forecasts into forecasts of a surrogate for sector capacity - the fraction of jet routes that would be blocked- within an en route sector.