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By NASA
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA’s ECOSTRESS instrument on June 19 recorded scorching roads and sidewalks across Phoenix where contact with skin could cause serious burns in minutes to seconds, as indicated in the legend above. NASA/JPL-Caltech Roads and sidewalks in some areas get so hot that skin contact could result in second-degree burns.
Researchers at NASA’s Jet Propulsion Laboratory in Southern California have mapped scorching pavement in Phoenix where contact with skin — from a fall, for example — can cause serious burns. The image shows land surface temperatures across a grid of roads and adjacent sidewalks, revealing how urban spaces can turn hazardous during hot weather.
Data for this visualization of the Phoenix area — the fifth most populous city in the United States — was collected at 1:02 p.m. local time on June 19, 2024, by a NASA instrument aboard the International Space Station. Called ECOSTRESS (short for the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station), the instrument measures thermal infrared emissions from Earth’s surface.
The Image shows how miles of asphalt and concrete surfaces (colored here in yellow, red, and purple, based on temperature) trap heat. The surfaces registered at least 120 degrees Fahrenheit (49 degrees Celsius) to the touch — hot enough to cause contact burns in minutes to seconds.
The image also shows cooling effects of green spaces in communities like Encanto and Camelback East, in contrast to the hotter surface temperatures seen in Maryvale and Central City, where there are fewer parks and trees.
“We create these maps to be intuitive to users and help make data more accessible to the public and citizens scientists,” said Glynn Hulley, a JPL climate researcher. “We see them as a vital tool for planning effective heat interventions, such as tree planting, that can cool down the hottest roads and sidewalks.”
Homing in on Heat
At the lower right of the image is Phoenix’s Sky Harbor International Airport, where ECOSTRESS recorded some of the hottest land surface temperatures within the city —around 140 F (60 C). The air temperature on June 19 at the airport reached 106 F (43 C).
Air temperature, which is measured out of direct sunlight, can differ significantly from the temperature at the land surface. Streets are often the hottest surfaces of the built environment due to dark asphalt paving that absorbs more sunlight than lighter-colored surfaces; asphalt absorbs up to 95% of solar radiation. These types of surfaces can easily be 40 to 60 degrees F (22 to 33 degrees C) hotter than the air temperature on a very hot day.
Launched to the International Space Station in 2018, ECOSTRESS has as its primary mission the identification of plants’ thresholds for water use and water stress, giving insight into their ability to adapt to a warming climate. But the instrument is also useful for documenting other heat-related phenomena, like patterns of heat absorption and retention.
To produce the image of Phoenix, scientists used a machine learning algorithm that incorporates data from additional satellites: NASA/USGS Landsat and Sentinel-2. The combined measurements were used to “sharpen” the surface temperatures to a resolution of 100 feet (30 meters) by 100 feet (30 meters).
More About the Mission
JPL built and manages the ECOSTRESS mission for the Earth Science Division in the Science Mission Directorate at NASA Headquarters in Washington. ECOSTRESS is an Earth Venture Instrument mission; the program is managed by NASA’s Earth System Science Pathfinder program at NASA’s Langley Research Center in Hampton, Virginia.
More information about ECOSTRESS is available here: https://ecostress.jpl.nasa.gov/.
News Media Contacts
Jane J. Lee / Andrew Wang
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-0307 / 626-379-6874
jane.j.lee@jpl.nasa.gov / andrew.wang@jpl.nasa.gov
Written by Sally Younger
2024-096
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Last Updated Jul 02, 2024 Related Terms
Ecostress (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) Earth Science Extreme Weather Events Jet Propulsion Laboratory Weather and Atmospheric Dynamics Explore More
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By NASA
“Don’t let the NASA emblem scare you away.
“I was very intimidated by it because it was a childhood dream [to make it to NASA]. I saw a picture of me at Kennedy Space [Center’s] visitor center the last time I went home. I must have been five years old. I always used to tell myself that I wasn’t smart enough. [I assumed you needed to be] a literal rocket scientist, and I have absolutely no STEM [science, technology, engineering, and math] degree whatsoever.
“So my advice is, as long as you’re true to who you are, you’re transparent, you’re yourself, and you put the work in, you will get what you want.
“And make them tell you no — that was one of the first things I learned. If you don’t ask and you don’t apply, you have your answer. So make them tell you no. If you don’t ask, you’ll never know.
“And don’t be intimidated or influenced by an emblem or your perception of what kind of people are behind that emblem. Because now I realize, once I’ve made it to NASA, that it’s nothing like I thought it was. In a lot of ways, it’s better, right? Because I get these opportunities to do things that are not in my primary role to serve others, and in that capacity, it’s serving me. That’s my advice.”
— Melissa Coleman, Transportation Officer, Logistics Branch, NASA’s Kennedy Space Center
Image Credit: NASA/Cory Huston
Interviewer: NASA/Thalia Patrinos
Check out some of our other Faces of NASA.
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By NASA
The software discipline has broad involvement across each of the NASA Mission Directorates. Some recent discipline focus and development areas are highlighted below, along with a look at the Software Technical Discipline Team’s (TDT) approach to evolving discipline best practices toward the future.
Understanding Automation Risk
Software creates automation. Reliance on that automation is increasing the amount of software in NASA programs. This year, the software team examined historical software incidents in aerospace to characterize how, why, and where software or automation is mostly likely to fail. The goal is to better engineer software to minimize the risk of errors, improve software processes, and better architect software for resilience to errors (or improve fault-tolerance should errors occur).
Some key findings shown in the above charts, indicate that software more often does the wrong thing rather than just crash. Rebooting was found to be ineffective when software behaves erroneously. Unexpected behavior was mostly attributed to the code or logic itself, and about half of those instances were the result of missing software—software not present due to unanticipated situations or missing requirements. This may indicate that even fully tested software is exposed to this significant class of error. Data misconfiguration was a sizeable factor that continues to grow with the advent of more modern data-driven systems. A final subjective category assessed was “unknown unknowns”—things that could not have been reasonably anticipated. These accounted for 19% of software incidents studied.
The software team is using and sharing these findings to improve best practices. More emphasis is being placed on the importance of complete requirements, off-nominal test campaigns, and “test as you fly” using real hardware in the loop. When designing systems for fault tolerance, more consideration should be given to detecting and correcting for erroneous behavior versus just checking for a crash. Less confidence should be placed on rebooting as an effective recovery strategy. Backup strategies for automations should be employed for critical applications—considering the historic prevalence of absent software and unknown unknowns. More information can be found in NASA/TP-20230012154, Software Error Incident Categorizations in Aerospace.
Employing AI and Machine Learning Techniques
The rise of artificial intelligence (AI) and machine learning (ML) techniques has allowed NASA to examine data in new ways that were not previously possible. While NASA has been employing autonomy since its inception, AI/ML techniques provide teams the ability to expand the use of autonomy outside of previous bounds. The Agency has been working on AI ethics frameworks and examining standards, procedures, and practices, taking security implications into account. While AI/ML generally uses nondeterministic statistical algorithms that currently limit its use in safety-critical flight applications, it is used by NASA in more than 400 AI/ML projects aiding research and science. The Agency also uses AI/ML Communities of Practice for sharing knowledge across the centers. The TDT surveyed AI/ML work across the Agency and summarized it for trends and lessons.
Common usages of AI/ML include image recognition and identification. NASA Earth science missions use AI/ML to identify marine debris, measure cloud thickness, and identify wildfire smoke (examples are shown in the satellite images below). This reduces the workload on personnel. There are many applications of AI/ML being used to predict atmospheric physics. One example is hurricane track and intensity prediction. Another example is predicting planetary boundary layer thickness and comparing it against measurements, and those predictions are being fused with live data to improve the performance over previous boundary layer models.
Examples of how NASA uses AI/ML. Satellite images of clouds with estimation of cloud thickness (left) and wildfire detection (right). NASA-HDBK-2203, NASA Software Engineering and Assurance Handbook (https://swehb.nasa.gov) The Code Analysis Pipeline: Static Analysis Tool for IV&V and Software Quality Improvement
The Code Analysis Pipeline (CAP) is an open-source tool architecture that supports software development and assurance activities, improving overall software quality. The Independent Verification and Validation (IV&V) Program is using CAP to support software assurance on the Human Landing System, Gateway, Exploration Ground Systems, Orion, and Roman. CAP supports the configuration and automated execution of multiple static code analysis tools to identify potential code defects, generate code metrics that indicate potential areas of quality concern (e.g., cyclomatic complexity), and execute any other tool that analyzes or processes source code. The TDT is focused on integrating Modified Condition/Decision Coverage analysis support for coverage testing. Results from tools are consolidated into a central database and presented in context through a user interface that supports review, query, reporting, and analysis of results as the code matures.
The tool architecture is based on an industry standard DevOps approach for continuous building of source code and running of tools. CAP integrates with GitHub for source code control, uses Jenkins to support automation of analysis builds, and leverages Docker to create standard and custom build environments that support unique mission needs and use cases.
Improving Software Process & Sharing Best Practices
The TDT has captured the best practice knowledge from across the centers in NPR 7150.2, NASA Software Engineering Requirements, and NASA-HDBK-2203, NASA Software Engineering and Assurance Handbook (https://swehb.nasa.gov.) Two APPEL training classes have been developed and shared with several organizations to give them the foundations in the NPR and software engineering management. The TDT established several subteams to help programs/projects as they tackle software architecture, project management, requirements, cybersecurity, testing and verification, and programmable logic controllers. Many of these teams have developed guidance and best practices, which are documented in NASA-HDBK-2203 and on the NASA Engineering Network.
NPR 7150.2 and the handbook outline best practices over the full lifecycle for all NASA software. This includes requirements development, architecture, design, implementation, and verification. Also covered, and equally important, are the supporting activities/functions that improve quality, including software assurance, safety configuration management, reuse, and software acquisition. Rationale and guidance for the requirements are addressed in the handbook that is internally and externally accessible and regularly updated as new information, tools, and techniques are found and used.
The Software TDT deputies train software engineers, systems engineers, chief engineers, and project managers on the NPR requirements and their role in ensuring these requirements are implemented across NASA centers. Additionally, the TDT deputies train software technical leads on many of the advanced management aspects of a software engineering effort, including planning, cost estimating, negotiating, and handling change management.
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By Space Force
The Defense Travel Management Office announced a new policy to cover pet travel expenses, including pet transportation and quarantine fees, in June 2023.
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By NASA
2 min read
NASA Concludes Significant Technical Challenge: In-Time Terminal Area Risk Management
NASA’s System-Wide Safety project is working towards achieving NASA’s vision for safe, efficient skies.Busakorn Pongparnit Operations within the National Airspace System continue to grow in scale and complexity. As a result, causal factors of risks and hazards are increasingly complex and drive the need to transform the way we conduct risk management and safety assurance.
NASA’s System-Wide Safety (SWS) project recently commemorated the completion of a major step towards that transformation with an engaging hybrid event reflecting on the completion of its Technical Challenge 1 (TC-1): In-Time Terminal Area Risk Management.
The event highlighted key takeaways, provided technology demonstrations, and engaged stakeholders and partners in conversations around the myriad of capabilities and opportunities made possible by the tools, techniques, and processes developed under the technical challenge.
Speakers from NASA, the Federal Aviation Administration (FAA), airlines, and the aviation industry at large discussed how to best leverage TC-1 capabilities as the safety foundation of this new era of commercial aviation.
New technologies developed in TC-1 identify emerging risks and monitor safety margins before an accident occurs – not after. Powered by prognostic and predictive risk assessment algorithms and human factors research, TC-1 work will both improve today’s safety management systems and help us shape future operational systems.
Nikunj Oza, subproject manager for TC-1, speaks at the closeout event.NASA Through TC-1, NASA and its partners have developed and demonstrated:
Methods to improve risk management and safety assurance processes by proactively identifying risks and causal factors before an accident/incident occurs. Integrated risk assessment capabilities to monitor and assess terminal area operations based on advanced data analytics methods and predictive model development. Machine Learning Analytics Tools, in collaboration with our partners, that identify and characterize operational risks, monitor, and integrate data, evaluate risk mitigation strategies, and determine causal and contributing factors. TC-1’s findings are the bedrock of the rest of the SWS technical challenges. They pave the way for a new technical challenge (TC-6) that seeks to expand on the work completed thus far and address the call to action set forth by the FAA to address safety challenges facing the transforming aviation industry.
SWS extends sincere appreciation to TC-1’s subproject managers, Nikunj Oza and Chad Stephens, and to Abigail Glenn-Chase for coordinating such an impactful event.
A recording of the event is available below.
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