Publications

As new observation systems are developed and deployed, new and presumably more precise information is becoming available for weather forecasting and climate monitoring. To take advantage of these new observations, it is desirable to have schemes to accurately retrieve the information before statistical analyses are performed so that statistical computation can be more effectively used where it is needed most. The authors propose a sequential variational approach that possesses advantages of both a standard statistical analysis [such as with a three-dimensional variational data assimilation (3DVAR) or Kalman filter] and a traditional objective analysis (such as the Barnes analysis). The sequential variational analysis is multiscale, inhomogeneous, anisotropic, and temporally consistent, as shown by an idealized test case and observational datasets in this study. The real data cases include applications in two-dimensional and three-dimensional space and time for storm outflow boundary detection (surface application) and hurricane data assimilation (three-dimensional space application). Implemented using a multigrid technique, this sequential variational approach is a very efficient data assimilation method.

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.

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.

Turbulence encounters continue to be one of the largest sources of personal injury in both commercial and general aviation. A significant percentage of these encounters occur without warning, at low altitudes, and have been observed to occur outside of the strong reflectivity storm cores where pilots typically anticipate severe wind shear and/or turbulence. In this paper, statistics illustrating the altitude distributions of specific turbulence encounters are presented. These results suggest that a significant percentage of the moderate and greater turbulence encounters occur at low altitudes. One particularly dangerous form of low altitude turbulence, often associated with convective storms, is the buoyancy wave (BW). Observational evidence of commercial airline encounters with these phenomena indicates that they can cause an impairment of aircraft control that results in significant attitude and altitude fluctuations. Over the past two years several serious aircraft incidents involving low altitude turbulence have been reported. In our investigation of the meteorological conditions surrounding these incidents, there are strong indications that buoyancy waves played a major role in initiating the turbulence. While encounters with this type of buoyancy wave-induced turbulence can be as severe as microburst wind shear encounters, they are typically not detected by current wind shear detection systems. However, these phenomena do have detectable signatures. We suggest two modifications to existing wind shear detection systems that would make it possible to detect these potentially dangerous phenomena.

Major airlines and FAA Traffic Flow Managers alike would prefer to plan their flight routes around convective weather and thereby avoid the tactical maneuvering that results when unforecasted thunderstorms occur. Strategic planning takes place daily and 2-6 hr forecasts are utilized, but these early plans remain unaltered in only the most predictable of convective weather scenarios. More typically, the ATC System Command Center and the Air Route Traffic Control Centers together with airline dispatchers will help flights to utilize jet routes that remain available within regions of convection, or facilitate major reroutes around convection, according to the available "playbook" routes. For this tactical routing in the presence of convective weather to work, both a precise and timely shared picture of current weather is required as well as an accurate, reliable short term (0-2 hr) forecast. This is crucial to containing the system-wide and airport-specific delays that are so prevalent in the summer months (Figure 1), especially as traffic demands approach full capacity at the pacing airports. This paper describes the Tactical 0-2 hr Convective Weather Forecast (CWF) algorithm developed by the MIT Lincoln Laboratory for the FAA, principally sponsored by the Aviation Weather Research Program (AWRP). This CWF technology is currently being utilized in both the Integrated Terminal Weather System (ITWS; Wolfson et al., 2004) and the Corridor Integrated Weather System (CIWS; Evans et al., 2004) proof-of-concept demonstrations. Some of this technology is also being utilized in the National Convective Weather Forecast from the Aviation Weather Center (Megenhardt, 2004), the NCAR Autonowcaster (Saxen et al., 2004), and in various private-vendor forecast systems.

Many of the recent conflicts where the United States (US) military forces have been deployed are regions that contain complex terrain (i.e. Korea, Kosovo, Afghanistan, and northern Iraq). Accurate weather forecasts are critical to the success of operations in these regions and are typically supplied by numerical weather prediction (NWP) models like the US Navy NOGAPS, CAOMPS, and US Airforce MM5. Unfortunately the weather observations required to generate accurate initial conditions needed by these models are often not available. In these cases it is desirable to deploy additional weather sensors. The question then becomes: Where should the military planners deploy their sensor resources? This study demonstrates that knowledge of just the terrain within the model domain may be a useful factor for military planners to consider. For NWP, model forecast errors in mountainous areas are typically thought to be due to poorly resolved terrain, or model physics not suited for use in a complex terrain environment. Recent advances in computational technology are making it possible to run these models at resolutions where many of the significant terrain features are now being well resolved. While terrain can be accurately specified, often the gradients in wind, temperature, and moisture fields associated with the higher resolution terrain are not. As a result, initial conditions in complex terrain environments are not be adequately specified. Since not all initial condition errors contribute significantly to model forecast error, knowledge of terrain induced NWP model forecast sensitivity may be important when developing and deploying a weather sensor network to support a regional scale NWP model. The terrain induced model sensitivity can provide an indication of which variables in the initial conditions have a significant influence on the forecast and where initial conditions need to be most accurate to minimize model forecast error. A sensor network can then be designed to minimize these errors by deploying critical sensors in sensitive locations, thereby reducing relevant initial condition error without the costly deployment of a high-density sensor network. This is similar to the targeted observation technique first suggested by Emanuel et al. (1995), except that in this example the targeted observations would be designed to reduce initial condition error associated with poorly resolved atmospheric features created by the terrain. This paper is organized as follows. Section 2 contains a brief description of the data collection effort designed to support this study. The experimental design and the specifics of the case used in this study are described in section 3. The analysis and results from both the forward and adjoint simulations are presented in section 4. Section 5 contains a summary of the results, and a brief discussion of their implications.

Errors in NWP model forecasts are typically due to deficiencies in the model formulation, inaccuracies associated with the numerical integration techniques, and errors in the specification of initial conditions. This study investigates the latter of these three issues and, in particular, elucidates the errors in the initial conditions due to inadequate data resolution. In a basic sense, for the atmosphere to be adequately sampled at a given length scale, it is not always necessary to increase the number of samples throughout the entire domain. Increased sampling resolution has the greatest benefit in the regions where gradients in the atmospheric conditions exist. Targeted observation techniques attempt to take advantage of this fact by using additional observations to improve the initial analysis in the regions that will have the most impact on forecast accuracy (Emanuel et al. 1995). The result is an economical means to reduce initial condition error and improve forecast accuracy. It is well known that terrain can serve as a localized forcing mechanism in high-resolution models. In addition to acting as a forcing mechanism, variations in terrain can also create strong gradients in the atmospheric fields of models using terrain following vertical coordinates. It is reasonable to assume that if these gradients were better represented in the initial conditions, forecasts accuracies could improve. The present study examines the relationship between terrain variability and the sensitivity of a high-resolution wind forecast to errors in the initial conditions in these areas. The background behind this study and a brief description of the terrain and atmospheric characteristics of the cases used in the experiments are presented in section 2. Initial condition sensitivity analysis results from the fifth generation Pennsylvania State University (PSU), National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) adjoint and forward models are contained in sections 3 and 4. A summary of the results and conclusions are found in section 5.

Weather comprises one of the most significant safety hazards facing civilian aviation today. This hazard has been significantly reduced by the development and use of microburst wind shear detection technologies such as the Low Level Wind Shear Alert System (LLWAS), the Terminal Doppler Weather Radar (TDWR), the ASR-9 Weather Systems Processor (WSP) and the Integrated Terminal Weather System (ITWS). Each was designed to detect and warn for the presence of low altitude wind shear resulting from microburst and gust fronts. These systems have made an unquestionable improvement in aviation safety; however, there are other forms of low altitude wind shear hazardous to aviation. This paper provides a description of a low altitude buoyancy wave (BW) induced turbulence phenomena that appears to also be a significant hazard to aviation. Buoyancy wave turbulence can be particularly dangerous since it often occurs outside regions containing intense precipitation where pilots typically expect to encounter thunderstorm induced wind shear conditions. Section 2 of this paper contains a general description of BW phenomena based on laboratory and observational studies. Section 3 will briefly summarize several incidents where commercial and civilian aircraft have encountered buoyancy waved induced turbulence. A summary and conclusions are made in section 4.