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NASA NTRS2000

Communications Related to Weather Information Handling and Dissemination

This report summarizes the tasking contained in the Statement of Work and describes the results of the project. In addition, it addresses the principles, procedures, and methods of application that would be generally applicable to using the results of the project. NASA Glenn Research Center (GRC) is involved in the Aviation Weather Information (AWIN) Program, which has a goal of reducing the aircraft accident rate, by a factor of five within 10 years and by a factor of 10 within 20 years. GRC's effort concentrates on the communications means needed to disseminate effective weather data. GRC's focus in on developing new technologies and techniques to support the digital communication of weather information between airborne and ground-based users.

Dhas, Chris↗
NASA NTRS2014

Assessment of the Pseudo Geostationary Lightning Mapper Products at the Spring Program and Summer Experiment

Since 2010, the de facto Geostationary Lightning Mapper (GLM) demonstration product has been the Pseudo‐Geostationary Lightning Mapper (PGLM) product suite. Originally prepared for the Hazardous Weather Testbed's Spring Program (specifically the Experimental Warning Program) when only four ground‐based lightning mapping arrays were available, the effort now spans collaborations with several institutions and eight collaborative networks. For 2013, NASA's Short‐term Prediction Research and Transition (SPoRT) Center and NOAA's National Severe Storms Laboratory have worked to collaborate with each network to obtain data in real‐time. This has gone into producing the SPoRT variant of the PGLM that was demonstrated in AWIPS II for the 2013 Spring Program. Alongside the PGLM products, the SPoRT / Meteorological Development Laboratory's total lightning tracking tool also was evaluated to assess not just another visualization of future GLM data but how to best extract more information while in the operational environment. Specifically, this tool addressed the leading request by forecasters during evaluations; provide a time series trend of total lightning in real‐time. In addition to the Spring Program, SPoRT is providing the PGLM "mosaic" to the Aviation Weather Center (AWC) and Storm Prediction Center. This is the same as what is used at the Hazardous Weather Testbed, but combines all available networks into one display for use at the national centers. This year, the mosaic was evaluated during the AWC's Summer Experiment. An important distinction between this and the Spring Program is that the Summer Experiment focuses on the national center perspective and not at the local forecast office level. Specifically, the Summer Experiment focuses on aviation needs and concerns and brings together operational forecaster, developers, and FAA representatives. This presentation will focus on the evaluation of SPoRT's pseudo‐GLM products in these separate test beds. The emphasis will be on how future GLM observations can support operations at both the local and national scale and how the PGLM was used in combination with other lightning data sets. Evaluations for the PGLM were quite favorable with forecasters appreciating the high temporal resolution, the ability to look for rapid increases in lightning activity ahead of severe weather, as well as situational awareness for where convection is firing and for flight routing.

Stano, Geoffrey T.↗
NASA NTRSDate not supplied

Novel Hourly-Resolved Global Cloud Property Composite from Operational Satellites Imagers

Numerous applications in satellite remote sensing of surface properties, atmospheric state, composition, and radiation require accurate knowledge of the location and characteristics of clouds. Operational satellite imager radiances are valuable for cloud detection and for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. This paper describes a new global high-resolution dataset of cloud properties made available for community use that is constructed from analyses of a constellation of meteorological satellite imagers. Data taken from Meteosat-8/9 and -11, Himawari-8/9, GOES-16, and -17/-18, Aqua, Terra, Suomi-NPP and NOAA-20 are processed and composited on a 3-km grid to provide hourly global coverage. A historical multi-year dataset is available to serve various needs including modeling challenges related to cloud evaluations and parameterizations. Near real-time data products are also currently available between 60N and 60S. Efforts are underway to operationalize polar orbiting satellite cloud detection methods for low-latency applications over polar regions. The cloud detection and retrieval algorithms have been developed over many years to support NASA weather and climate programs such as the Clouds and Earth’s Radiant Energy System (CERES). To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. A brief description of the methods highlighting the unique aspects of the SatCORPS data products will be presented along with information on their status and availability.

SatCORPS↗
NASA NTRS2001

Ground-Based Icing Condition Remote Sensing System Definition

This report documents the NASA Glenn Research Center activities to assess and down select remote sensing technologies for the purpose of developing a system capable of measuring icing condition hazards aloft. The information generated by such a remote sensing system is intended for use by the entire aviation community, including flight crews. air traffic controllers. airline dispatchers, and aviation weather forecasters. The remote sensing system must be capable of remotely measuring temperature and liquid water content (LWC), and indicating the presence of super-cooled large droplets (SLD). Technologies examined include Profiling Microwave Radiometer, Dual-Band Radar, Multi-Band Radar, Ka-Band Radar. Polarized Ka-Band Radar, and Multiple Field of View (MFOV) Lidar. The assessment of these systems took place primarily during the Mt. Washington Icing Sensors Project (MWISP) in April 1999 and the Alliance Icing Research Study (AIRS) from November 1999 to February 2000. A discussion of the various sensing technologies is included. The result of the assessment is that no one sensing technology can satisfy all of the stated project goals. Therefore a proposed system includes radiometry and Ka-band radar. A multilevel approach is proposed to allow the future selection of the fielded system based upon required capability and available funding. The most basic level system would be the least capable and least expensive. The next level would increase capability and cost, and the highest level would be the most capable and most expensive to field. The Level 1 system would consist of a Profiling Microwave Radiometer. The Level 2 system would add a Ka-Band Radar. The Level 3 system would add polarization to the Ka-Band Radar. All levels of the system would utilize hardware that is already under development by the U.S. Government. However, to meet the needs of the aviation community, all levels of the system will require further development. In addition to the proposed system, it is also recommended that NASA continue to foster the development of Multi-Band Radar and airborne microwave radiometer technologies.

Reehorst, Andrew L.↗
NASA NTRS1977

Summary report of Committee A

Sessions were held with the standing committees on aircraft design, simulation, general services, and general aviation. It was stated that current procedures for designing structural components with respect to turbulence forcing functions were adequate. It was agreed that the ultimate goal of aviation weather services was the delivery of accurate and timely information to the cockpit flight crew.

Sprinkle, C. H.↗
NASA NTRS1981

Automated Pilot Advisory System

An Automated Pilot Advisory System (APAS) was developed and operationally tested to demonstrate the concept that low cost automated systems can provide air traffic and aviation weather advisory information at high density uncontrolled airports. The system was designed to enhance the see and be seen rule of flight, and pilots who used the system preferred it over the self announcement system presently used at uncontrolled airports.

Parks, J. L., Jr.↗
NASA NTRS2002

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Sensor Development

In response to recommendations from the National Aviation Weather Program Council, the National Aeronautics and Space Administration (NASA) is working with industry to develop an electronic pilot reporting capability for small aircraft. This paper describes the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor development effort. NASA is working with industry to develop a sensor capable of measuring temperature, relative humidity, magnetic heading, pressure, icing, and average turbulence energy dissipation. Users of the data include National Centers for Environmental Prediction (NCEP) forecast modelers, air traffic controllers, flight service stations, airline operation centers, and pilots. Preliminary results from flight tests are presented.

Daniels, Taumi S.↗
NASA NTRS2003

NASA-FAA-NOAA Partnering Strategy

This viewgraph presentation provides an overview of NASA-FAA (Federal Aviation Administration) and NOAA (National Oceanic and Atmospheric Administration) collaboration efforts particularly in the area of aviation and aircraft safety. Five technology areas are being jointly by these agencies: (1) aviation weather information; (2) weather products; (3) automet technologies; (4) forward looking weather sensors and (5) turbulence controls and mitigation systems. Memorandum of Agreements (MOU) between these agencies are reviewed. A general review of the pros and pitfalls of inter-agency collaborations is also presented.

Colantonio, Ron↗
NASA NTRS2004

Cockpit System Situational Awareness Modeling Tool

This project explored the possibility of predicting pilot situational awareness (SA) using human performance modeling techniques for the purpose of evaluating developing cockpit systems. The Improved Performance Research Integration Tool (IMPRINT) was combined with the Adaptive Control of Thought-Rational (ACT-R) cognitive modeling architecture to produce a tool that can model both the discrete tasks of pilots and the cognitive processes associated with SA. The techniques for using this tool to predict SA were demonstrated using the newly developed Aviation Weather Information (AWIN) system. By providing an SA prediction tool to cockpit system designers, cockpit concepts can be assessed early in the design process while providing a cost-effective complement to the traditional pilot-in-the-loop experiments and data collection techniques.

Keller, John↗
NASA NTRS2005

Tropospheric Airborne Meteorological Data and Reporting (TAMDAR) Icing Sensor Performance during the 2003/2004 Alliance Icing Research Study (AIRS II)

NASA Langley Research Center and its research partners from the University of North Dakota (UND) and the National Center for Atmospheric Research (NCAR) participated in the AIRS II campaign from November 17 to December 17, 2003. AIRS II provided the opportunity to compare TAMDAR in situ in-flight icing condition assessments with in situ data from the UND Citation II aircraft's Rosemont system. TAMDAR is designed to provide a general warning of ice accretion and to report it directly into the Meteorological Data Communications and Reporting System (MDCRS). In addition to evaluating TAMDAR with microphysical data obtained by the Citation II, this study also compares these data to the NWS operational in-flight icing Current Icing Potential (CIP) graphic product and with the NASA Advanced Satellite Aviation-weather Products (ASAP) Icing Severity product. The CIP and ASAP graphics are also examined in this study to provide a context for the Citation II's sorties in AIRS II.

Murray, John J.↗
NASA NTRS2011

Evaluation of Long-Range Lightning Detection Networks Using TRMM/LIS Observations

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. Toward this end, the present study evaluates data from the World Wide Lightning Location Network (WWLLN) using observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study documents the WWLLN detection efficiency and location accuracy relative to LIS observations, describes the spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by WWLLN. Improved knowledge of the WWLLN detection capabilities will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Rudlosky, Scott D.↗
NASA NTRS2012

Relationships Between Long-Range Lightning Networks and TRMM/LIS Observations

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. The present study intercompares long-range lightning data with observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study examines network detection efficiency and location accuracy relative to LIS observations, describes spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by the long-range networks. Improved knowledge of relationships between these datasets will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Rudlosky, Scott D.↗
NASA NTRSDate not supplied

The International Space Station Lightning Imaging Sensor: Performance, Applications, and Cross-Platform Science

The Lightning Imaging Sensor (LIS), an optically based lightning detector developed at NASA Marshall Space Flight Center, was launched to the International Space Station (ISS) in February 2017, detecting optical signatures of lightning with storm-scale horizontal resolution (4-km) during both day and night. ISS LIS data are available 1 March 2017 to present. Millisecond timing allows detailed intercalibration and validation with other spaceborne and ground-based lightning sensors. Initial comparisons with those other sensors suggest flash detection efficiency around 60% (diurnal variability of 51-75%), false alarm rate under 5%, timing accuracy better than 2 ms, and horizontal location accuracy around 3 km. The spatially uniform flash detection capability of ISS LIS from low-Earth orbit allows assessment of spatially varying flash detection efficiency for other sensors and networks, particularly the Geostationary Lightning Mappers (GLMs). ISS LIS provides research data suitable for investigations of lightning physics, climatology, thunderstorm processes, and atmospheric composition, as well as realtime lightning data for operational forecasting and aviation weather interests. ISS LIS enables enrichment and extension of the long-term global climatology of lightning from space, and is the only recent platform that extends the global record to higher latitudes (± 55°). The global spatial distribution of lightning from ISS LIS is broadly similar to previous datasets, with globally averaged seasonal/annual flash rates (3-year average: ~44 s) about 5-10% lower. This difference is likely due to reduced flash detection efficiency that will be mitigated in future ISS LIS data processing, as well as the shorter ISS LIS period of record. The expected land/ocean contrast in the diurnal variability of global lightning is also observed. The near-realtime ISS LIS data make the instrument important to operational weather forecasting and related applications, including public safety. Finally, ISS LIS is demonstrating utility as part (or potential part) of cross-platform studies examining a diverse array of topics, including lightning physics, thunderstorm processes, convective precipitation, and atmospheric composition.

Timothy Lang↗
NASA NTRSDate not supplied

Advancing NASA SatCORPS Global Data Products with Cloud Computing and Machine Learning

Operational satellite imager radiances are valuable for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. The use of cloud computing has enabled recent enhancements to process a constellation of geostationary satellites at higher spatiotemporal resolutions than previously possible that meet low latency and near real-time needs. Data taken from Meteosat-8 and -11, Himawari, GOES-16, and -17 are processed and combined with operational polar orbiting satellite data and composited on a 3-km grid to provide global coverage. To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. An update on recent SatCORPS enhancements is presented, highlighting the community benefits achieved with the use of cloud computing and machine learning.

William L Smith↗
NASA NTRS2022

NASA’s Global Precipitation Measurement Mission: Leveraging Stakeholder Engagement & Applications Activities to Inform Decision-making

The application of satellite precipitation estimates from NASA’s Global Precipitation Measurement (GPM) Mission for decision-making has been a focus for the mission since launch. As a result, GPM data have enabled a range of applications that address societal needs, including water resource management, crop forecasting, ecological monitoring, disaster response, public health, aviation, weather forecasting, and climate modeling, among others. GPM applications activities have continued to focus on user engagement through in person trainings and interviews, workshops, webinars, and educational outreach activities. The goals of these efforts are to synthesize community data needs in order to effectively support and enable decision-making across agencies, academia and the global community. While these efforts have helped the GPM mission establish a large stakeholder community that encompasses federal and state partners, academic institutions, nd private and nonprofit companies, there remains difficulties associated with accessing, processing, and applying the data to support or enable applications. In this article, we present GPM applications strategies and approaches used to enhance the applications value of GPM data, and most importantly, demonstrate how these efforts have and can inform different decision-making contexts. This work also provides a discussion on key lessons learned from the user community and how this information can be utilized to help better support and shape applications approaches for future NASA Earth Science missions.

Satellite precipitation↗
NASA NTRS2001

Convection Weather Detection by General Aviation Pilots with Convectional and Data-Linked Graphical Weather Information Sources

This study compares how well general aviation (GA) pilots detect convective weather in flight with different weather information sources. A flight test was conducted in which GA pilot test subjects were given different in-flight weather information cues and flown toward convective weather of moderate or greater intensity. The test subjects were not actually flying the aircraft, but were given pilot tasks representative of the workload and position awareness requirements of the en route portion of a cross country GA flight. On each flight, one test subject received weather cues typical of a flight in visual meteorological conditions (VMC), another received cues typical of flight in instrument meteorological conditions (IMC), and a third received cues typical of flight in IMC but augmented with a graphical weather information system (GWIS). The GWIS provided the subject with near real time data-linked weather products, including a weather radar mosaic superimposed on a moving map with a symbol depicting the aircraft's present position and direction of track. At several points during each flight, the test subjects completed short questionnaires which included items addressing their weather situation awareness and flight decisions. In particular, test subjects were asked to identify the location of the nearest convective cells. After the point of nearest approach to convective weather, the test subjects were asked to draw the location of convective weather on an aeronautical chart, along with the aircraft's present position. This paper reports preliminary results on how accurately test subjects provided with these different weather sources could identify the nearest cell of moderate or greater intensity along their route of flight. Additional flight tests are currently being conducted to complete the data set.

Chamberlain, James P.↗
NASA NTRS2001

Audio-Visual Situational Awareness for General Aviation Pilots

Weather is one of the major causes of general aviation accidents. Researchers are addressing this problem from various perspectives including improving meteorological forecasting techniques, collecting additional weather data automatically via on-board sensors and "flight" modems, and improving weather data dissemination and presentation. We approach the problem from the improved presentation perspective and propose weather visualization and interaction methods tailored for general aviation pilots. Our system, Aviation Weather Data Visualization Environment (AWE), utilizes information visualization techniques, a direct manipulation graphical interface, and a speech-based interface to improve a pilot's situational awareness of relevant weather data. The system design is based on a user study and feedback from pilots.

Spirkovska, Lilly↗