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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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NASA NTRS2018

NASA Earth eXchange (NEX) App Store

NASA Earth Exchange (NEX), and her public cloud version OpenNEX, have become platforms supporting scientific collaboration, knowledge sharing and research for the entire Earth science community. To date, a number of custom tools and capabilities have been integrated into the platforms. However, such integration has to undergo a case-by-case manual process thus lacks scalability. This timely project builds an App Store onto OpenNEX as a building block. Climate data analytics tools/programs can be easily uploaded, shared, organized, searched, and recommended like photos and videos on the YouTube. The foundation of our App Store is a provenance server, which not only records metadata but also execution history of climate data analytics apps including the input data and parameters, output data and products, who runs the app for which purpose, and how apps may be chained into workflows. Researchers can thus understand, reproduce, and repurpose existing apps and workflows. Machine learning approaches are applied to mine provenance to provide recommend-as-you-go services for Earth scientists, such as to recommend suitable apps and workflow snippets. A browser-based workflow tool is also provided for researchers to explore the provenance server and design value-added workflows. Scalability, sustainability, extensibility, usability, adaptability, security and privacy are considered in the App Store.

eXchange↗
DOE OSTI2023

Software Bill of Materials (SBOM) Sharing Lifecycle Report

As Software Bill of Materials (SBOM) adoption efforts mature, SBOM sharing continues to occur, but no single solution or set of solutions have become ubiquitous. The purpose of this report is to enumerate and describe the different parties and phases of the SBOM sharing lifecycle and assist readers in choosing suitable SBOM sharing solutions based on the amount of time, resources, subject-matter expertise, effort, and access to tooling that is available to the reader to implement a phase of the SBOM sharing lifecycle. The SBOM sharing lifecycle consists of the Discovery, Access, and Transport of an SBOM and this report details these individual phases and how an SBOM goes from author to the consumer. This report also details how potential enrichment activities may be performed on an SBOM to create a new product before or after it has been shared. The concept of a sophistication classification for SBOM sharing solutions is concurrently introduced with a focus on the inclusion or lack of certain features and effort associated with their implementation. Examples of low, medium, and high-sophistication solutions are provided; however, these examples and associated categorizations should not be seen as a qualitative judgment meant to push the reader towards a particular adoption strategy since sharing solutions are chosen based on the unique needs of the user. This report does recommend the SBOM community consider how to make current and future sharing solutions interoperable with each other as well as more automated methods to facilitate sharing and broader SBOM adoption. This report also highlights an SBOM sharing survey results obtained from interviews with stakeholders to understand the current SBOM sharing landscape. The categorized results of the survey suggest that SBOMs are currently transported directly to the receiver through email or similar informal communication mechanisms or alternatively the SBOM resides on a repository available to consumers. In addition to these transport methods, this report captures industry efforts to create private sharing solutions and services that can store and transport enrichment data and may use higher sophistication features that are cloud-based or using distributed ledger technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

2022 Power Global Community (Gloco) Summit Summary Report

The Prediction Of Worldwide Energy Resources (POWER) Project 25th year of providing global solar and meteorology data products to the world. The goals of this event were to inform users of new, enhanced features, gather feedback on services, identify new requests and requirements, and capture more thorough user stories while identifying new user communities and partnership opportunities. The event was “virtual” and had over 580 registrants. Over the two-day event, the Summit was attended by 162 unique attendees representing 24 countries.

NASA↗
NASA NTRS2010

The Geostationary Lightning Mapper (GLM) for the GOES-R Series Next Generation Operational Environmental Satellite Constellation

The next generation Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2015 is a follow on to the existing GOES system currently operating over the Western Hemisphere. The system will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. The system provides products including lightning, cloud properties, rainfall rate, volcanic ash, air quality, hurricane intensity, and fire/hot spot characterization. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved spectral, spatial, and temporal resolution for the 16-channel Advanced Baseline Imager (ABI). The Geostationary Lightning Mapper (GLM), an optical transient detector will map total (in-cloud and cloud-to-ground) lightning flashes continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions, from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the higher level algorithms and applications using the GLM alone and decision aids incorporating information from the ABI, ground-based weather radar, and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional lightning networks are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time total lightning mapping data are also being provided in an experimental mode to selected National Weather Service (NWS) national centers and forecast offices via the GOES-R Proving Ground to help improve our understanding of the application of these data in operational settings and facilitate early on-orbit user readiness for this new capability.

Goodman, Steven J.↗
NASA NTRSDate not supplied

Hyperspectral Sounder Products: SiFSAP and ClimFiSP

Infrared hyperspectral sounders have demonstrated to provide very high value information for a range of weather and climate applications. Traditional retrieval algorithms of hyperspectral sounders use the ‘cloud clearing’ methodology in the retrieval process due to the lack of means to account for cloud scattering effect. The corresponding Level-2 data products are therefore limited by two major factors: the degradation of spatial resolution as compared with the native resolution of the instruments and the lack of ‘radiance closure’ that is critically needed for climate trend studies. The single field-of-view (FOV) sounder atmospheric products (SiFSAP) algorithm has been developed to supplement existing operational products. The SiFSAP algorithm uses the principal component based radiative transfer model (PCRTM) that is ultra-fast and has been extensively validated for the cloud scattering simulation capability. The cloud properties are physically retrieved along with profiles of temperature, moisture, and trace gases of interest, surface properties simultaneously by fitting single FOV spectral measurements. Therefore, SiFSAP ensure the radiometric consistency between the retrieved geophysical properties and the top-of-atmosphere (TOA) spectral measurements. This presentation provides a general introduction of SiFSAP and details about the physical retrieval algorithm. Also introduced here is a derivative data product of SiFSAP, the climate fingerprinting Sounder Products (ClimFiSP). ClimFiSP include the space-time averaged properties of key climate variables that are derived from the space-time averaged radiances via the spectral fingerprinting scheme. The spectral fingerprinting method allows a low latency data update on ClimFiSP following any potential calibration update on satellite measurements. ClimFiSP also include the rigorously defined uncertainty characterization for trends or anomalies of climate variables, being critical for the construction of long-term climate data record. Both SiFSAP and ClimFiSP will be available to users through NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC).

Wan Wu↗
NASA NTRSDate not supplied

Study the Vertical Structure and Transportation of the Extreme African Dust Storms using MERRA-2 Data

The Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2) provides the first long-term global reanalysis to assimilate space-based observations of aerosols and represents their interactions with other physical processes in the climate system. In this study, we have examined the variations of atmospheric aerosols for the last 20 years since 2002 using the sub-daily MERRA-2 data and found seven extreme African dust storms that were transported westward across the Atlantic Ocean from the Sahara, crossing 70o-80oW. In particular, the well-known ‘Godzilla’ dust storm occurred in June 2020, and its dust cloud, with the highest-on-record aerosol optical depths, was transported toward the Americas. This storm greatly degraded air quality over large areas of the Caribbean Basin and the United States. The air quality index reached unhealthy levels for sensitive groups in more than ten U.S. states. In our study, the vertical structure and transport characteristics of the dust layers during this extreme dust event are investigated. The geopotential height and temperature were found anomalously low (around 600 hPa) over the Atlantic Ocean off northwest Africa before the June 2020 dust storm. This anomalous circulation pattern was persistent for more than 12 days starting from around May 30, breaking the regular easterly waves that transport dust from the Sahara Desert to the west. To verify the data quality, daily MERRA-2 PM2.5 data were calculated and compared with PM2.5 observations from the U.S. Environmental Protection Agency (EPA) at several selected ground stations in Florida. We provide this case study to illustrate how to effectively use various MERRA-2 data services at Goddard Earth Sciences Data and Information Services Center (GES DISC) where MERRA-2 data are archived, hoping to help data users in exploring their own topics of interest using data services at GES DISC.

data management, reanalysis↗
NASA NTRS2010

The Geostationary Lighting Mapper (GLM) for GOES-R: A New Operational Capability to Improve Storm Forecasts and Warnings

The next generation Geostationary Operational Environmental Satellite (GOES-R) series is a follow on to the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved spectral (3x), spatial (4x), and temporal (5x) resolution for the Advanced Baseline Imager (ABI). The GLM, an optical transient detector and imager operating in the near-IR at 777.4 nm will map all (in-cloud and cloud-to-ground) lighting flashes continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions, from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (a prototype and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) national centers and forecast offices via the GOES-R Proving Ground to help improve our understanding of the application of these data in operational settings and facilitate Day-1 user readiness for this new capability.

Goodman, Steven J.↗
NASA NTRS2011

Developing and Evaluating RGB Composite MODIS Imagery for Applications in National Weather Service Forecast Offices

Satellite remote sensing has gained widespread use in the field of operational meteorology. Although raw satellite imagery is useful, several techniques exist which can convey multiple types of data in a more efficient way. One of these techniques is multispectral compositing. The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed two multispectral satellite imagery products which utilize data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites, based upon products currently generated and used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The nighttime microphysics product allows users to identify clouds occurring at different altitudes, but emphasizes fog and low cloud detection. This product improves upon current spectral difference and single channel infrared techniques. Each of the current products has its own set of advantages for nocturnal fog detection, but each also has limiting drawbacks which can hamper the analysis process. The multispectral product combines each current product with a third channel difference. Since the final image is enhanced with color, it simplifies the fog identification process. Analysis has shown that the nighttime microphysics imagery product represents a substantial improvement to conventional fog detection techniques, as well as provides a preview of future satellite capabilities to forecasters.

Oswald, Hayden↗
NASA NTRS2009

Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

Dreher, Joseph↗
DOE OSTI2026

Demonstrating the data center as a flexible grid asset using a C-HIL setup

Increasing data center demand is outpacing grid infrastructure development. Artificial intelligence workloads and hyperscale cloud growth are creating unprecedented demand for power, while traditional grid expansion faces multiyear development timelines. Verrus is developing an innovative datacenter solution for this challenge, data centers that act as active grid-supportive assets rather than passive loads. Our approach integrates a novel grid-aware power flow management system with battery energy storage systems(BESS) into a microgrid-controlled, medium-voltage power distribution architecture that delivers critical capabilities, such as: * Fast response to grid disturbances such over/ under voltage or over/ under frequency * Demand flexibility that can service requests from the utility within 10 s * Uninterrupted transition to islanded operation during grid outages * Continuous uptime assurance for compute loads while maintaining all customer service level agreements. Through Verrus' strategic partnership with the National Renewable Energy Laboratory (NREL), these capabilities were validated using NREL's Advanced Research on Integrated Energy Systems (ARIES) virtual emulation environment to model a 70-MW grid-interactive data center. This paper outlines the design, methodology, and results of this emulated deployment, demonstrating that data centers can provide both critical load resilience and ancillary grid support without compromising uptime requirements. Specifically, we present a digital real time simulation of a 70 MW data center integrated with a physical microgrid controller, and demonstrate the data center response in the event of a grid voltage and frequency event, utility demand response request and utility outage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Climate data system supports FIRE

The NASA Climate Data System (NCDS) at Goddard Space Flight Center is serving as the FIRE Central Archive, providing a centralized data holding and data cataloging service for the FIRE project. NCDS members are carrying out their responsibilities by holding all reduced observations and data analysis products submitted by individual principal investigators in the agreed upon format, by holding all satellite data sets required for FIRE, by providing copies of any of these data sets to FIRE investigators, and by producing and updating a catalog with information about the FIRE holdings. FIRE researchers were requested to provide their reduced data sets in the Standard Data Format (SDF) to the FIRE Central Archive. This standard format is proving to be of value. An improved SDF document is now available. The document provides an example from an actual FIRE SDF data set and clearly states the guidelines for formatting data in SDF. NCDS has received SDF tapes from a number of investigators. These tapes were analyzed and comments provided to the producers. One product which is now available is William J. Syrett's sodar data product from the Stratocumulus Intensive Field Observation. Sample plots from all SDF tapes submitted to the archive will be available to FSET members. Related cloud products are also available through NCDS. Entries describing the FIRE data sets are being provided for the NCDS on-line catalog. Detailed information for the Extended Time Observations is available in the general FIRE catalog entry. Separate catalog entries are being written for the Cirrus Intensive Field Observation (IFO) and for the Marine Stratocumulus IFO. Short descriptions of each FIRE data set will be installed into the NCDS Summary Catalog.

Olsen, Lola M.↗
DOE OSTI2022

Cloud-Control of Legacy Building Automation System: A case study

As Internet of Things devices and cloud-based platforms become more mature, Energy Management and Information Systems (EMIS) are increasingly gaining momentum in the building industry. In large commercial buildings, Fault-Detection and Diagnostic (FDD) and energy information systems (EIS) are now established technologies with tens of providers and thousands of deployment sites across North America. The new frontier for the EMIS technology is now represented by control systems that use advanced system optimization (ASO) methods to improve the operations of the HVAC system. Given the complexity of the integration of such systems with the existing building automation systems (BAS) and the higher risk involved with direct control of the HVAC, these systems are still emerging in the market. This paper presents the results of a project in which a start-up company partnered with a research institution to develop a cloud-based software EMIS solution and deployed it in a university campus in California. The software system included advanced sensing, data acquisition, storage and advanced control and analytics applications developed on top of the native BAS. The new platform controls ten buildings on the campus and the FDD and the ASO applications deployed on this platform were able to generate energy savings of up to 35% and 25% in certain buildings for each functionality respectively. Where the platform did not save energy, it improved building service (air quality). Lessons learned include the importance of collaborating with and training the building operators and evaluating whether the legacy system can work reliably with the new technology.

Prakash, Anand Krishnan↗
NASA NTRS2015

An Integrated 0-1 Hour First-Flash Lightning Nowcasting, Lightning Amount and Lightning Jump Warning Capability

Using satellite-based methods that provide accurate 0-1 hour convective initiation (CI) nowcasts, and rely on proven success coupling satellite and radar fields in the Corridor Integrated Weather System (CIWS; operated and developed at MIT-Lincoln Laboratory), to subsequently monitor for first-flash lightning initiation (LI) and later period lightning trends as storms evolve. Enhance IR-based methods within the GOES-R CI Algorithm (that must meet specific thresholds for a given cumulus cloud before the cloud is considered to have an increased likelihood of producing lightning next 90 min) that forecast LI. Integrate GOES-R CI and LI fields with radar thresholds (e.g., first greater than or equal to 40 dBZ echo at the -10 C altitude) and NWP model data within the WDSS-II system for LI-events from new convective storms. Track ongoing lightning using Lightning Mapping Array (LMA) and pseudo-Geostationary Lightning Mapper (GLM) data to assess per-storm lightning trends (e.g., as tied to lightning jumps) and outline threat regions. Evaluate the ability to produce LI nowcasts through a "lightning threat" product, and obtain feedback from National Weather Service forecasters on its value as a decision support tool.

remote sensing↗
NASA NTRS2021

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

coastal environment↗
NASA NTRS1996

Workstation-Based Real-Time Mesoscale Modeling Designed for Weather Support to Operations at the Kennedy Space Center and Cape Canaveral Air Station

This paper describes the capabilities and operational utility of a version of the Mesoscale Atmospheric Simulation System (MASS) that has been developed to support operational weather forecasting at the Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS). The implementation of local, mesoscale modeling systems at KSC/CCAS is designed to provide detailed short-range (less than 24 h) forecasts of winds, clouds, and hazardous weather such as thunderstorms. Short-range forecasting is a challenge for daily operations, and manned and unmanned launches since KSC/CCAS is located in central Florida where the weather during the warm season is dominated by mesoscale circulations like the sea breeze. For this application, MASS has been modified to run on a Stardent 3000 workstation. Workstation-based, real-time numerical modeling requires a compromise between the requirement to run the system fast enough so that the output can be used before expiration balanced against the desire to improve the simulations by increasing resolution and using more detailed physical parameterizations. It is now feasible to run high-resolution mesoscale models such as MASS on local workstations to provide timely forecasts at a fraction of the cost required to run these models on mainframe supercomputers. MASS has been running in the Applied Meteorology Unit (AMU) at KSC/CCAS since January 1994 for the purpose of system evaluation. In March 1995, the AMU began sending real-time MASS output to the forecasters and meteorologists at CCAS, Spaceflight Meteorology Group (Johnson Space Center, Houston, Texas), and the National Weather Service (Melbourne, Florida). However, MASS is not yet an operational system. The final decision whether to transition MASS for operational use will depend on a combination of forecaster feedback, the AMU's final evaluation results, and the life-cycle costs of the operational system.

Manobianco, John↗
NASA NTRS1995

STS-70 Mission Highlights

The highlights of the STS-70 mission are presented in this video. The flight crew consisted of Cmdr. Tom Henricks, Pilot Kevin Kregel, Flight Engineer Nancy Currie, and Mission Specialists Dr. Don Thomas and Dr. Mary Ellen Weber. The mission's primary objective was the deployment of the 7th Tracking Data and Relay Satellite (TDRS), which will provide a communication, tracking, telemetry, data acquisition, and command services space-based network system essential to low Earth orbital spacecraft. Secondary mission objectives included activating and studying the Physiological and Anatomical Rodent Experiment/National Institutes of Health-Rodents (PARE/NIH-R), The Bioreactor Demonstration System (BDS), the Commercial Protein Crystal Growth (CPCG) studies, the Space Tissue Loss/National Institutes of Health-Cells (STL/NIH-C) experiment, the Biological Research in Canisters (BRIC) experiment, Shuttle Amateur Radio Experiment-2 (SAREX-2), the Visual Function Tester-4 (VFT-4), the Hand-Held, Earth Oriented, Real-Time, Cooperative, User-Friendly, Location-Targeting and Environmental System (HERCULES), the Microcapsules in Space-B (MIS-B) experiment, the Windows Experiment (WINDEX), the Radiation Monitoring Equipment-3 (RME-3), and the Military Applications of Ship Tracks (MAST) experiment. There was an in-orbit dedication ceremony by the spacecrew and the newly Integrated Mission Control Center to commemorate the Center's integration. The STS-70 mission was the first mission monitored by this new control center. Earth views included the Earth's atmosphere, a sunrise over the Earth's horizon, several views of various land masses, some B/W lightning shots, some cloud cover, and a tropical storm.

Source record↗
NASA NTRS2009

Anvil Forecast Tool in the Advanced Weather Interactive Processing System

Meteorologists from the 45th Weather Squadron (45 WS) and National Weather Service Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Shuttle Flight Rules. As a result, the Applied Meteorology Unit (AMU) was tasked to create a graphical overlay tool for the Meteorological Interactive Data Display System (MIDDS) that indicates the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input. The tool creates a graphic depicting the potential location of thunderstorm anvils one, two, and three hours into the future. The locations are based on the average of the upper level observed or forecasted winds. The graphic includes 10 and 20 n mi standoff circles centered at the location of interest, as well as one-, two-, and three-hour arcs in the upwind direction. The arcs extend outward across a 30 sector width based on a previous AMU study that determined thunderstorm anvils move in a direction plus or minus 15 of the upper-level wind direction. The AMU was then tasked to transition the tool to the Advanced Weather Interactive Processing System (AWIPS). SMG later requested the tool be updated to provide more flexibility and quicker access to model data. This presentation describes the work performed by the AMU to transition the tool into AWIPS, as well as the subsequent improvements made to the tool.

Barrett, Joe H., III↗