Publications

Detecting low-altitude wind shear in support of aviation safety and efficiency is the primary mission of the Terminal Doppler Weather Radar (TDWR). The wind-shear detection performance depends directly on the quality of the data produced by the TDWR. At times the data quality suffers from the presence of clutter. Al-though stationary ground clutter signals can be removed by a high-pass filter, moving clutter such as birds and roadway traffic cannot be attenuated using the same technique because their signal power can exist any-where in the Doppler velocity spectrum. Furthermore, because the TDWR is a single-polarization radar, polarimetry cannot be used to discriminate these types of clutter from atmospheric signals. The moving clutter problem is exacerbated at Western sites with dry microbursts, because their low signal-to-noise ratios (SNRs) are more easily masked by un-wanted moving clutter. For Las Vegas (LAS), Nevada, the offending clutter is traffic on roads that are oriented along the radar line of sight near the airport. The radar is located at a significantly higher altitude than the town, improving the visibility to the roads, and giving LAS the worst road clutter problem of all TDWR sites. The Salt Lake City (SLC), Utah, airport is located near the Great Salt Lake, which is the biggest inland staging area for migrating seabirds in the country. It, therefore, suffers from bird clutter, which not only can obscure wind shear signatures but can also mimic them to trigger false alarms. The TDWR "dry" site issues are discussed in more detail by Cho (2008). In order to mitigate these problems, we developed a moving clutter spectral filter (MCSF). In this paper we describe the algorithm and present preliminary test results.

Weather radar products from the United States' NEXRAD network are used as key components in FAA weather systems such as CIWS, ITWS, and WARP. The key products, High Resolution VIL (HRVIL) and High Resolution Enhanced Echo Tops (HREET), provide primary information about precipitation location and intensity. The NEXRAD network will become dual polarization capable beginning in late 2010 adding the ability to classify hydrometeors. This new aspect from radar remote sensing of the weather offers opportunity to provide new aviation weather products and augment existing ones. This paper will detail the dual polarization product development program at MIT Lincoln Laboratory (LL) in support of FAA system needs. Current development efforts focus on four products. Two new products will provide volumetric analysis seeking aviation hazards (icing and hail). Two existing products, HRVIL and HREET, will be invigorated by dual polarization data to yield improved data quality and mitigation of partial beam blockage. The LL program has partnered with NCAR and NSSL subject matter experts to bring their most advanced research results into these new and improved products. The LL program also has partnered with Valparaiso University for them to provide dual polarization and local sonde sounding data especially during suspected icing conditions. The new Icing Hazards Level (IHL) product is expected to provide the most benefit to the FAA. Its development also poses the greatest challenge both in scope and in the ability of S-band radar to sense the phenomena of interest. Icing phenomena include supercooled drops/droplets and ice crystals and the associated aviation hazard could be aloft or near/at the surface. Graupel is an indication that supercooled water has accreted to ice particles. The initial NEXRAD hydrometeor classifier will not have an explicit supercooled water class or the benefit of supporting data. It will have ice crystal and graupel classes. The LL approach will utilize at least some additional data (vertical thermodynamic profiles). Techniques applied to the development of IHL will likely have applicability to the other products as well. Aspects of the IHL development will also be discussed in the paper.

Severe weather avoidance programs (SWAP) due to convective weather are common in many of the busiest terminal areas in the US National Airspace System (NAS). In order to make efficient use of available airspace in rapidly evolving convective weather, it is necessary to predict the impacts of the weather on key resources (e.g., departure and arrival routes and fixes), with frequent updates as the weather changes. Currently, this prediction is a mental process that imposes a significant cognitive burden on air traffic managers. As a result, air traffic management in SWAP is often inconsistent and decisions result in less than optimal performance. The Route Availability Planning Tool (RAPT) is a prototype automated decision support tool, intended to help air traffic managers in convective weather SWAP, by predicting the impacts of convective weather on departure routes. Originally deployed in New York in August, 2002, RAPT has recently undergone two field evaluations (2007 and 2008) in order to test and refine its concept of operations, evaluate the accuracy and usefulness of its decision guidance, and estimate observed and potential delay reduction benefits that may be achieved as a result of its use. This paper presents the results of the 2008 performance evaluation, focusing on the concept of operations and the quality of decision support guidance. A second paper [1] presents analyses of delay reduction benefits and the operational decision making environment in which RAPT is deployed.

This paper will discuss the progress the Multi-function Phased Array Radar (MPAR) research program has made over the last 18 months as well as insight into the program strategy for moving forward. Current research activities include evaluating the impact of MPAR's faster scanning rates to aviation weather algorithms (e.g., how it will help in predicting storm growth and decay) and exploring dual polarization for phased array radars. Additionally, the Department of Homeland Security (DHS) has expanded the MPAR multi-agency partnership and is sponsoring research into the mitigation of wind-farm interference on weather sensing. Significant research in semi-conductor technology and advances in transmit/receive module design and phased array architectures are beginning to create a pathway towards system affordability. The MPAR program plan calls for a technology demonstration phase followed by the initiation of a prototype development effort within the next five years. This paper will provide the updates on these and other program activities.

Dozens of Ground Delay Programs (GDPs) are implemented each summer for San Francisco International Airport (SFO) in order to cope with reduced capacity caused by the presence of warm-season stratus in the approach zone. The stratus prevents the use of dual approaches to SFO's closely-spaced parallel runways, which essentially reduces the arrival capacity by half. In 2004, a prototype system for providing probabilistic stratus forecast guidance was transitioned from the research community to NWS Monterey. This system was intended to be used as a tool for improving the daily forecast of stratus clearing time from the approach zone, and correspondingly improve the efficiency of GDP implementation strategy. Since its transition to the NWS in 2004, the automated forecast guidance system has continued to produce reliable forecasts of daily stratus clearing time. However, this success has not adequately translated to a marked improvement in GDP efficiency. Analysis by the NWS indicates that the existing mechanisms for introducing the forecast guidance information into the GDP decision process, as well as the GDP implementation strategy itself, are not suited for taking full advantage of the forecast skill demonstrated by the system. A historical examination of SFO GDP implementation based on the probabilistic forecasts provided by the automated forecast guidance system is currently in process, with the objective being a recommendation for a more effective GDP strategy. An important consideration is understanding the risk/reward associated with the decision process. In this instance, the reward is increased efficiency seen as reduced aircraft delays, at the risk of creating increased delay, aircraft diversions, and controller workload in the event that an incorrect optimistic forecast results in the premature release of ground-held aircraft. This investigation is being performed in concert with the weather-integration objectives of the current FAA modernization program, particularly the integration of weather information that is delivered in a probabilistic format. Shortcomings within the current GDP strategy are described to provide context for potential improvements that exploit the probabilistic forecasts currently emerging from the research community.

Air traffic congestion caused by convective weather in the US has become a serious national problem. Several studies have shown that there is a critical need for timely, reliable and high quality forecasts of precipitation and echo tops with forecast time horizons of up to 12 hours in order to predict airspace capacity (Robinson et al. 2008, Evans et al. 2006 and FAA REDAC Report 2007). Yet, there are currently several forecast systems available to strategic planners across the National Airspace System (NAS) that are not fully meeting Air Traffic Management (ATM) needs. Furthermore, the use of many forecasting systems increases the potential for conflicting information in the planning process, which can cause situational awareness problems between operational facilities. One of the goals of the Next Generation Air Transportation System (NextGen) is to consolidate these redundant and sometimes conflicting forecast systems into a Single Authoritative Source (SAS) for aviation uses. The FAA initiated an effort to begin consolidating these systems in 2006, which led to the establishment of a collaboration between MIT Lincoln Laboratory (MIT LL), the National Center for Atmospheric Research (NCAR) Research Applications Laboratory (RAL), the NOAA Earth Systems Research Laboratory (ESRL) Global Systems Division (GSD) and NASA, called the Consolidated Storm Prediction for Aviation (CoSPA; Wolfson et al. 2008). The on-going collaboration is structured to leverage the expertise and technologies of each laboratory to build a CoSPA forecast capability that not only exceeds all current operational forecast capabilities and skill, but that provides enough resolution and skill to meet the demands of the envisioned NextGen decision support technology. The current CoSPA prototype for 0-6 hour forecasts is planned for operation as part of the NextGen Initial Operational Capability (IOC) in 2013. CoSPA is funded under the FAA's Aviation Weather Research Program (AWRP). The first CoSPA research prototype demonstration was conducted during the summer of 2008. Technologies from the Corridor Integrated Weather System (CIWS; Evans and Ducot 2006), National Convective Weather Forecast (NCWF; Megenhardt et al. 2004), and NOAA’s Rapid Update Cycle (RUC; Benjamin et al. 2004) and High Resolution Rapid Refresh (HRRR; Benjamin et al. 2009) models were consolidated along with new technologies into a single high-resolution forecast and display system. Historically, forecasts based on heuristics and extrapolation have performed well in the 0-2 hour window, whereas forecasts based on Numerical Weather Prediction (NWP) models have shown better performance than heuristics past 3-4 hours (Figure 1). One of the goals of CoSPA is to optimally blend heuristics and NWP models into a unified set of aviation-specific storm forecast products with the best overall performance possible. The CoSPA prototype demonstration began in July 2008 with 2-6 hr forecasts of Vertically-Integrated Liquid water (VIL) that seamlessly matched with the 0-2 hr VIL forecasts available in CIWS. These real-time forecasts have been made available to the research team and FAA management only through a web-based interface. This paper discusses the system infrastructure, the forecast display, the forecast technology and performance of the 2-6 hr VIL forecast. Our early assessment based on the 2008 demonstration is that CoSPA is showing tremendous promise for greatly improving strategic storm forecasts for the NAS. Early user feedback during CoSPA briefings suggested that the 6 hr forecast time horizon be extended to 8 hours to better meet their planning functions, and that forecasts of Echo Tops must also be included.

An oceanic convection diagnosis and nowcasting system is described whose domain of interest is the region between the southern continental United States and the northern extent of South America. In this system, geostationary satellite imagery are used to define the locations of deep convective clouds through the weighted combination of three independent algorithms. The resultant output, called the Convective Diagnosis Oceanic (CDO) product, is independently validated against space-borne radar and lightning products from the Tropical Rainfall Measuring Mission (TRMM) satellite to ascertain the ability of the CDO to discriminate hazardous convection. The CDO performed well in this preliminary investigation with some limitations noted. Short-term, 1-hr and 2-hr nowcasts of convection location are performed within the Convective Nowcasting Oceanic (CNO) system using a storm tracker. The CNO was found to have good statistical performance at extrapolating existing storm positions. Current work includes the development and implementation of additional atmospheric features for nowcasting convection initiation and to improve nowcasting of mature convection evolution.

The Convective Weather Avoidance Model (CWAM), developed in collaboration with NASA, translates convective weather information into a Weather Avoidance Field (WAF), to determine if pilots will route around convective regions. The WAF provides an estimate of the probability of pilot deviation around convective weather in en route airspace as a function of time, horizontal location, and flight altitude [1][2]. The results of the WAF can used to create reroutes around regions of convective weather where pilots are more likely to deviate. If reliable WAF information is provided to the cockpit and ground, pilot decisions may become more predictable, simplifying the task of air traffic control in convective weather. The improvement and validation of CWAM requires inference of pilot intent from flight trajectory data, which is challenging. The process currently involves laborious human review of the results of automated deviation detection algorithms. Both previous CWAM studies and a recent validation study [3] illustrate the difficulties and limitations of attempting to infer pilot intent from flight trajectory data. Furthermore, observed flight tracks may not correctly represent pilot preference. In some instances, pilots may have penetrated airspace that they would rather have avoided or they may have avoided airspace that was easily passable. In order to improve and assess the accuracy of the WAF, it is desirable to compare WAF predictions of pilot intent with direct evidence of the airborne experience during weather encounters in en route airspace, such as normal acceleration. To achieve this, a series of flights using a research aircraft was conducted. In the summer of 2008, four research flights (three on 17 July and one on 14 August) were flown in and around convective activity in the upper Midwestern United States to gather aircraft data that could be correlated to the WAF and other remotely-sensed weather data. The aircraft, a Rockwell Sabreliner Model 50 research aircraft (similar to the Sabreliner Model 40 production model) owned by Rockwell-Collins, flew through and around convective activity while recording on-board accelerations for comparison to the WAF deviation probabilities encountered along the flight trajectory. Aircraft state data, on-board weather radar images, video, photographs and pilot narrative from the cockpit were also collected. This paper briefly describes the CWAM model and WAF. Description of the data collection methodology is then presented. Following that section are descriptions of the flights comparing radar data from the flight deck with ground-based weather radar and the WAF. Visual observations and pilot narrative from the flight deck are also presented. Next, the normal acceleration data from on-board accelerometer data are compared with WAF. Finally, conclusions and suggestions for further study are presented.

In this paper, we develop a novel way to measure the accuracy of weather forecasts based upon the impact on air traffic flows. This method uses new techniques developed as part of the CWAM that consider the complicated interaction between pilots, air traffic controllers and weather. This technique, known as the blockage model (Martin et al., 2006), differentiates between minor deviations performed by pilots around convective weather and their larger deviations due to fully blocked air routes that require air traffic control interaction. This blockage model is being used by the automated Route Availability Planning Tool (RAPT) to predict route blockage for NYC departures. RAPT integrates the Corridor Integrated Weather Systems (CIWS) deterministic 0-2 hour forecasts of precipitation and echo tops into route specific forecasts of impact on air traffic in the congested east coast corridor. Applying the blockage model to the entire CIWS weather domain as a metric for scoring the performance of the forecast algorithms is shown to be an excellent approach for measuring the adequacy of the forecast in predicting the impact of the convective weather on air traffic operations.

The primary fields that provide weather situational awareness in the Corridor Integrated Weather System (CIWS) are radar-derived vertically-integrated liquid (VIL) and echo top height (ET). In situations of reduced or non-existent radar coverage, such as over the oceans, in mountainous terrain or during periods of radar outages, the radar VIL and ET fields are severely compromised or even absent. In these situations, the lightning data are often unaffected and fully available to use as a proxy for the radar fields in convective weather nowcasts. The purpose of this study is to develop the capability to utilize cloud-to-ground lightning strike data as a proxy for radar VIL and echo tops (ET) in the CIWS. The datasets used in this study are the National Lightning Detection Network (NLDN) and the 1 km/5min radar VIL and ET mosaics produced at MIT LL. To capture the synoptic variability of the lightning-VIL and lightning-ET relationships over the CIWS domain, atmospheric variables from the NOAA Rapid Update Cycle (RUC) model and the Space-time Mesoscale Analysis System (STMAS) are utilized with the lightning data in a statistical regression framework. Once spatially and temporally coherent regions of VIL and ET derived from the lightning are produced, the potential exists for tracking these regions and providing accurate short-term forecasting of convective hazards.