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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
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NASA NTRS1977

Summary report of the General Aviation Committee

Fatal weather involved general aviation accidents and the criteria for weather observations at general aviation airports were discussed. It was generally agreed that: (1) meteorologists do not seem to have sufficient understanding of general aviation requirements, (2) pilots are not aware of the meteorological services and publications which are available to them; (3) Uniform capability is not being utilized to the degree possible; and (4) there is a wealth of weather data available within the Department of Defense which is not available in the system for civil use. The committee recommends that student pilot training programs include actual inflight weather experience accomplished through instructor training, and efforts be made to make real time weather data available to the pilot from all sources to include military installations, Unicom operators, tower and approach controllers, and air traffic controllers.

Goodrich, W. C.↗
NASA NTRS2010

Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation

Convective weather is currently the largest contributor to air traffic delays in the United States. In order to make effective traffic flow management decisions to mitigate these delays, weather forecasts must be made as early and as accurately as possible. A forecast product that could be used to mitigate convective weather impacts is the Consolidated Storm Prediction for Aviation. This product provides forecasts of cloud water content and convective top heights at 0- to 8-hour look-ahead times. The objective of this study was to examine a method of predicting the impact of convective weather on air traffic sector capacities using these forecasts. Polygons representing forecast convective weather were overlaid at multiple flight levels on a sector map to calculate the fraction of each sector covered by weather. The fractional volume coverage was used as the primary metric to determine convection s impact on sectors. Results reveal that the forecasts can be used to predict the probability and magnitude of weather impacts on sector capacity up to eight hours in advance.

Russell, Carl↗
NASA NTRS2023

Improved Space Weather Observations and Modeling for Aviation Radiation

In recent years there has been a growing interest from the aviation community for space weather radiation forecasts tailored to the needs of the aviation industry. In 2019 several space weather centers began issuing advisories for the International Civil Aviation Organization alerting users to enhancements in the radiation environment at aviation flight levels. Due to a lack of routine observations, radiation modeling is required to specify the dose rates experienced by flight crew and passengers. While mature models exist, support for key observational inputs and further modeling advancements are needed. Observational inputs required from the ground-based neutron monitor network must be financially supported for research studies and operations to ensure real-time data is available for forecast operations and actionable end user decision making. An improved understanding of the geomagnetic field is required to reduce dose rate uncertainties in regions close to the open/closed geomagnetic field boundary, important for flights such as those between the continental US and Europe which operate in this region. Airborne radiation measurements, which are crucial for model validation and improvement, are lacking, particularly during solar energetic particle events. New measurement campaigns must be carried out to ensure progress and in situ atmospheric radiation measurements made available for real-time situational awareness. Furthermore, solar energetic particle forecasting must be improved to move aviation radiation nowcasts to forecasts in order to meet customer requirements for longer lead times for planning and mitigation.

space weather↗
NASA NTRS2004

Towards a Three-Dimensional Near-Real Time Cloud Product for Aviation Safety and Weather Diagnoses

Satellite data have long been used for determining the extent of cloud cover and for estimating the properties at the cloud tops. The derived properties can also be used to estimate aircraft icing potential to improve the safety of air traffic in the region. Currently, cloud properties and icing potential are derived in near-real time over the United States of America (USA) from the Geostationary Operational Environmental Satellite GOES) imagers at 75 W and 135 W. Traditionally, the results have been given in two dimensions because of the lack of knowledge about the vertical extent of clouds and the occurrence of overlapping clouds. Aircraft fly in a three-dimensional space and require vertical as well as horizontal information about clouds, their intensity, and their potential for icing. To improve the vertical component of the derived cloud and icing parameters, this paper explores various methods and datasets for filling in the three-dimensional space over the USA with cloud water.

Minnis, Patrick↗
NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗
NASA NTRS2002

Tactical Versus Strategic Behavior: General Aviation Piloting in Convective Weather Scenarios

We commonly describe environments and behavioral responses to environmental conditions as 'tactical' and 'strategic.' However theoretical research defining relevant environmental characteristics is rare, as are empirical investigations that would inform such theory. This paper discusses General Aviation (GA) pilots' descriptions of tactical/strategic conditions with respect to weather flying, and evaluates their ratings along a tactical/strategic scale in response to real convective weather scenarios experienced during a flight experiment with different weather information cues. Perceived risk was significantly associated with ratings for all experimental conditions. In addition, environmental characteristics were found to be predictive of ratings for Traditional IMC (instrument meteorological conditions), i.e., aural weather information only, and Traditional VMC (visual meteorological conditions), i.e., aural information and an external view. The paper also presents subjects' comments regarding use of Graphical Weather Information Systems (GWISs) to support tactical and strategic weather flying decisions and concludes with implications for the design and use of GWISs.

Latorella, Kara A.↗
NASA NTRS1988

The Federal Aviation Administration/Massachusetts Institute of Technology (FAA/MIT) Lincoln Laboratory Doppler weather radar program

The program focuses on providing real-time information on hazardous aviation weather to end users such as air traffic control and pilots. Existing systems will soon be replaced by a Next Generation Weather Radar (NEXRAD), which will be concerned with detecting such hazards as heavy rain and hail, turbulence, low-altitude wind shear, and mesocyclones and tornadoes. Other systems in process are the Central Weather Processor (CWP), and the terminal Doppler weather radar (TDWR). Weather measurements near Memphis are central to ongoing work, especially in the area of microbursts and wind shear.

Evans, James E.↗
NASA NTRS2002

Collegiate Aviation Research and Education Solutions to Critical Safety Issues

This Conference Proceedings is a collection of 6 abstracts and 3 papers presented April 19-20, 2001 in Denver, CO. The conference focus was "Best Practices and Benchmarking in Collegiate and Industry Programs". Topics covered include: satellite-based aviation navigation; weather safety training; human-behavior and aircraft maintenance issues; disaster preparedness; the collegiate aviation emergency response checklist; aviation safety research; and regulatory status of maintenance resource management.

Bowen, Brent↗
NASA NTRS2001

Cognitive Task Analysis of Business Jet Pilots' Weather Flying Behaviors: Preliminary Results

This report presents preliminary findings from a cognitive task analysis (CTA) of business aviation piloting. Results describe challenging weather-related aviation decisions and the information and cues used to support these decisions. Further, these results demonstrate the role of expertise in business aviation decision-making in weather flying, and how weather information is acquired and assessed for reliability. The challenging weather scenarios and novice errors identified in the results provide the basis for experimental scenarios and dependent measures to be used in future flight simulation evaluations of candidate aviation weather information systems. Finally, we analyzed these preliminary results to recommend design and training interventions to improve business aviation decision-making with weather information. The primary objective of this report is to present these preliminary findings and to document the extended CTA methodology used to elicit and represent expert business aviator decision-making with weather information. These preliminary findings will be augmented with results from additional subjects using this methodology. A summary of the complete results, absent the detailed treatment of methodology provided in this report, will be documented in a separate publication.

Latorella, Kara↗
NASA NTRS1998

The Aviation System Analysis Capability Airport Capacity and Delay Models

The ASAC Airport Capacity Model and the ASAC Airport Delay Model support analyses of technologies addressing airport capacity. NASA's Aviation System Analysis Capability (ASAC) Airport Capacity Model estimates the capacity of an airport as a function of weather, Federal Aviation Administration (FAA) procedures, traffic characteristics, and the level of technology available. Airport capacity is presented as a Pareto frontier of arrivals per hour versus departures per hour. The ASAC Airport Delay Model allows the user to estimate the minutes of arrival delay for an airport, given its (weather dependent) capacity. Historical weather observations and demand patterns are provided by ASAC as inputs to the delay model. The ASAC economic models can translate a reduction in delay minutes into benefit dollars.

Lee, David A.↗
NASA NTRS2008

Mitigating Aviation Communication and Satellite Orbit Operations Surprises from Adverse Space Weather

Adverse space weather affects operational activities in aviation and satellite systems. For example, large solar flares create highly variable enhanced neutral atmosphere and ionosphere electron density regions. These regions impact aviation communication frequencies as well as precision orbit determination. The natural space environment, with its dynamic space weather variability, is additionally changed by human activity. The increase in orbital debris in low Earth orbit (LEO), combined with lower atmosphere CO2 that rises into the lower thermosphere and causes increased cooling that results in increased debris lifetime, adds to the environmental hazards of navigating in near-Earth space. This is at a time when commercial space endeavors are posed to begin more missions to LEO during the rise of the solar activity cycle toward the next maximum (2012). For satellite and aviation operators, adverse space weather results in greater expenses for orbit management, more communication outages or aviation and ground-based high frequency radio used, and an inability to effectively plan missions or service customers with space-based communication, imagery, and data transferal during time-critical activities. Examples of some revenue-impacting conditions and solutions for mitigating adverse space weather are offered.

Tobiska, W. Kent↗
NASA NTRS1984

1983 lightning, turbulence, wind shear, and Doppler radar studies at the National Severe Storms Laboratory

As part of continuing research on aviation related weather hazards, numerous experiments were incorporated into the 1983 Spring Observation Program. This year's program was an abbreviated one because of commitments made to the development of the Next Generation Radar (NEXRAD) project. The National Oceanic and Atmospheric Administration's (NOAA) P-3 Orion and the National Aeronautics and Space Administration's (NASA) RB-57B and U-2 were the main aircraft involved in the studies of lightning, wind shear, turbulence, and storm structure. A total of 14 flights were made by these aircraft during the period of May 16 through June 5, 1983. Aircraft instrumentation experiments are described, and resultant data sets available for research are detailed. Aircraft instrumentation and Doppler radar characteristics are detailed.

Lee, J. T.↗
NASA NTRS1984

Rotorcraft flight-propulsion control integration

The parallel development of digital engine and flight controls for U.S. Army helicopters has made possible the future derivation of a fully integrated digital flight/propulsion control system. A NASA/Army research program has been undertaken to exploit these possibilities, ultimately yielding a generation of helicopters with exceptional agility and maneuverability in military roles and low pilot workloads in all-weather civil aviation missions. The program's three phases respectively address system modeling and analysis, flight hardware and software development, and flight evaluations aboard a research vehicle.

Mihaloew, J. R.↗
NASA NTRS2021

Weather Intelligent Navigation Data and Models for Aviation Planning (WINDMAP)

WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere;(3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5)integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models

Koushik Datta↗
NASA NTRS2023

Toward Physics-informed Neural Networks for 3D Multi-layer Cloud Mask Reconstruction

Three-dimensional (3D) cloud retrievals are critical for understanding their impact on climate and other applications such as aviation safety, weather prediction, and remote sensing. However, obtaining high-resolution and accurate vertical representation of clouds remains unsolved due to the limitations imposed by satellite instrumentation, viewing conditions, and the complexity of cloud dynamics. Cloud masks are essential for comprehending various cloud vertical properties, but deriving accurate 3D cloud masks from 2D satellite imagery data is a challenging task. To tackle these challenges, we introduce a physics-informed loss function for training deep learning models that can extend 2D cloud images into 3D cloud masks. The proposed loss, called CloudMask Loss, is composed of two domain knowledge-informed loss terms: one for evaluating cloud position and thickness, and the other for measuring the number of layers. By combining these loss terms, we improve the trainability of the deep learning models for more accurate and meaningful results. We apply the proposed loss function to different neural networks and demonstrate significant improvements in multi-layer cloud mask reconstruction. Utilizing the same neural network architecture, our proposed loss outperforms standard binary crossentropy loss in terms of multi-layer cloud classification accuracy, number of layers accuracy, and thickness mean absolute error (MAE). The proposed loss function can be readily integrated into various neural network architectures, resulting in substantial performance gains in 3D cloud mask generation.

multi-layer clouds↗