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The presentation will introduce NASA Earth Science Applied Sciences Program, introduce a few use-cases from Application Community, and also provide users perspective.
Scientific data services are a critical aspect of the NASA Center for Climate Simulations mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyber infrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model inter-comparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.
A virtual observatory with an Application Programming Interface (API) can become a powerful tool in analysis and modeling. In particular, an API that integrates time selection on such criteria as "most recent" and closest to a given absolute time simplifies the user-end programming considerably. We examine three types of use cases (nowcasting, data assimilation input, and user-defined sampling rates) for such functionality in the Virtual Solar Observatory (VSO).
NASA AIST Program is now designing and developing Digital Twins of the Earth and/or Earth systems. Organized around interconnected, multi-domain, high-scale modeling capabilities, the three major components of an Earth System Digital Twin are a continuously updated Digital Replica of the Earth System of interest, dynamic Forecasting models, and Impact Assessment capabilities. In order to define ESDT's benefits to Earth Science, as well as the AIST capabilities required to develop such systems, the AIST Program has been developed 6 science use cases corresponding to 6 of the main Earth Science domains.
NASA AIST Program is now designing and developing Digital Twins of the Earth and/or Earth systems. Organized around interconnected, multi-domain, high-scale modeling capabilities, the three major components of an Earth System Digital Twin are a continuously updated Digital Replica of the Earth System of interest, dynamic Forecasting models, and Impact Assessment capabilities. In order to define ESDT's benefits to Earth Science, as well as the AIST capabilities required to develop such systems, the AIST Program has been developed 6 science use cases corresponding to 6 of the main Earth Science domains.
In this work, we focus on the development of a simulation tool to assist in analysis of current and future (proposed) network architectures for NASA. Specifically, the Space Communications and Navigation (SCaN) Network is being architected as an integrated set of new assets and a federation of upgraded legacy systems. The SCaN architecture for the initial missions for returning humans to the moon and beyond will include the Space Network (SN) and the Near-Earth Network (NEN). In addition to SCaN, the initial mission scenario involves a Crew Exploration Vehicle (CEV), the International Space Station (ISS) and NASA Integrated Services Network (NISN). We call the tool being developed the SCaN Network Integration and Engineering (SCaN NI&E) Simulator. The intended uses of such a simulator are: (1) to characterize performance of particular protocols and configurations in mission planning phases; (2) to optimize system configurations by testing a larger parameter space than may be feasible in either production networks or an emulated environment; (3) to test solutions in order to find issues/risks before committing more significant resources needed to produce real hardware or flight software systems. We describe two use cases of the tool: (1) standalone simulation of CEV to ISS baseline scenario to determine network performance, (2) participation in Distributed Simulation Integration Laboratory (DSIL) tests to perform function testing and verify interface and interoperability of geographically dispersed simulations/emulations.
NASA's Earth Observing System Data and Information System (EOSDIS) is tasked with archiving and distributing Earth Observation data across a range of disciplines, including atmospheric science, oceanography, land processes, natural hazards, solar radiance and even socioeconomic aspects relating to the environment. Driven by rapidly rising data volumes, EOSDIS is migrating to a cloud computing based archive over the next few years. Although this simplifies data management somewhat, the main aim is to provide the data in an environment where end users can bring their analysis to the data rather than attempting to download and manage ever-increasing volumes. To that end, a cloud-based analysis platform is being constructed to enable data transformations, analyses and visualization without egressing the data from the cloud. In this endeavor, we expect a wide variety of users, algorithms and use cases. Consequently, the architecture of this cloud analytics platform is expressly designed to be based on open services, thus fostering an ecosystem that enables the efficient combination of common components with data-specific or analysis-specific components. Reviewed and approved by Andrew Mitchell, ESDIS project manager.
"Extreme weather events, such as hurricanes, tornadoes, floods, droughts, heatwaves, and blizzards, can cause widespread damage, disrupting ecosystems, agricultural production, and economies. The frequency and intensity of these events have been increasing, likely due to climate change, raising concerns and the need for more accurate analysis and predictions. NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) has migrated its long-term historical datasets, including precipitation data from MERRA-2 reanalysis, GLDAS land data assimilation, and IMERG satellite observations, to the cloud. This cloud-stored data enables scientists and researchers to utilize cloud computing for advanced modeling and forecasting of extreme weather events, eliminating the need to download large datasets. In this presentation, we will provide an overview of the cloud-based data and services managed by GES DISC; demonstrate methods for accessing and analyzing time series data stored in the cloud; and compare results across various datasets to address critical questions related to extreme precipitation. We will present use cases including: 1. Determining the average total precipitation in California during January and February from 2000 to 2024, and identifying anomalous precipitation in 2021. 2. Calculating the 10, 20, 50, and 100-year return periods for maximum daily rainfall based on 25 years of historical precipitation data (2000-2024) for Maryland.
NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, review of potential use case(s), and outline plans for further development and content generation. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.
NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at https://s3vi.ndc.nasa.gov/ssri-kb/. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months, significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content, an Application Programming Interface (API), improved user interfaces, new interfaces for crowdsourcing of content and user ratings, and custom website analytics to inform future development. This presentation and paper will discuss the motivation for the SSRI Knowledge Base, review educational use case(s), and outline plans for further development. The 2022 session topic that best fits the abstract (select only one): Coordinating Successful Educational Programs
The use of the case method in teaching various technical communication skills is described. Features of the method considered include: solving communication problems, identifying an audience, planning written communications, presenting written communications, and using visual aids.
The research direction was articulated in a statement of work created in collaboration between two program colleagues, an outside researcher and an internal user. The researcher was to deliver an implemented CASE tool (CasewiseTM) that was to be used to serve non-traditional (i.e., not software development related) organizational purposes. The explicitly stated functions of the tool were the support of 1) ISO-9000 compliance in the documentation of processes and 2) the management of process improvement. The collaborative team consisted of the researcher (GT), a full-time accompanying student (CRO), and the user (JD). The team originally focused on populating the CASE repository for the purpose of solving the two primary objectives. Consistent with the action research approach, several additional user requirements emerged as the project evolved, needs became apparent in discussions about how the tool would be used to solve organizational problems. These deliverables were contained within the CASE repository: 1) the creation of a paradigm diagram 2) the creation of a context diagram 3) the creation of child diagrams 4) the generation of 73 issues relating to organizational change 5) a compendium of stakeholder interview transcripts All record keeping was done manually and then keyed into the CASE interface. An issue is the difference between an organization s current situation (action) and its collective ideals.
A documentation of eight National Severe Storm Laboratory severe storm cases, which serve as a basis for a simulated VISSR Atmospheric Sounder retrieval study, is presented in this paper. Six of the selected cases provide a control data set to complete the statistical information needed for retrieval techniques based upon the use of regression matrices. The other two cases are to be used in the actual retrieval experiments. The selection was based upon the presence of moisture gradients in the analysis region, the availability of satellite images at the selected time periods, and the extent of cloud cover within the observing network.
No abstract available
The Regional Atmospheric Modeling System (RAMS), developed at Colorado State University, was used during the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) 2 (13 Nov. through 6 Dec. 1991) to provide real time forecasts of cirrus clouds. Forecasts were run once a day, initializing with the 0000 UTC dataset provided by NOAA (Forecast Systems Laboratory (FSL) Mesoscale Analysis and Prediction System (MAPS)). In order to obtain better agreement with observations, a second set of simulations were done for the FIRE 2 cases that occurred on 25-26 Nov. and 5-6 Dec. In this set of simulations, a more complex radiation scheme was used, the Chen/Cotton radiation scheme, along with the nucleation of ice occurring at ice supersaturations as opposed to nucleation occurring at water supersaturations that was done in the actual forecast version. The runs using these more complex schemes took longer wall clock time (7-9 hours for the actual forecasts as compared to 12-14 hrs for the runs using the more complex schemes) however, the final results of the simulations were definitely improved upon. Comparisons between these two sets of simulations are given. Now underway are simulations of these cases using a closed analytical solution for the auto-conversion of ice from a pristine ice class (sizes less than about 50 microns in effective diameter) to a snow class (effective diameters on the order of several hundred microns). This solution is employed along with a new scheme for the nucleation of ice crystals due to Meyers et al and Demott et al. The scheme is derived assuming complete gamma distributions for both the pristine and snow classes. The time rate of change of the number concentration and mass mixing-ratio of each distribution is found by calculating either the flux of crystals that grow beyond a certain critical diameter by vapor deposition in an ice supersaturated regime or by calculating the flux of crystals that evaporate to sizes below that same critical effective diameter.
Spaceflight computing is a key resource in NASA space missions and a core determining factor of spacecraft capability, with ripple effects throughout the spacecraft, end-to-end system, and mission. Onboard computing can be aptly viewed as a "technology multiplier" in that advances provide direct dramatic improvements in flight functions and capabilities across the NASA mission classes, and enable new flight capabilities and mission scenarios, increasing science and exploration return. Space-qualified computing technology, however, has not advanced significantly in well over ten years and the current state of the practice fails to meet the near- to mid-term needs of NASA missions. Recognizing this gap, the NASA Game Changing Development Program (GCDP), under the auspices of the NASA Space Technology Mission Directorate, commissioned a study on space-based computing needs, looking out 15-20 years. The study resulted in a recommendation to pursue high-performance spaceflight computing (HPSC) for next-generation missions, and a decision to partner with the Air Force Research Lab (AFRL) in this development.
Modern Application of Medical-Based Drone Delivery Drones have been used advantageously by militaries for nearly a century, but their uses in civilian life are still mostly cutting-edge, if not theoretical. After a decade of bold proclamations, Amazon’s “PrimeAir” drone delivery system is still in the stage of “preparing” for deliveries, while the public awaits for start ups like SkyDrop (formerly Flirtey) to follow through on impressive promises. Despite the well-publicized disappointment so far in commercial drone delivery, medical drone delivery has already proven itself practical and cheap in several countries, and it promises to expand in the coming years. Drones are uniquely suited to make valuable and urgent deliveries to remote areas, quickly transporting medical supplies where road transportation is prohibitively slow or not available at all. Drones have been used notably to deliver AEDs for out-of-hospital cardiac arrest, frequently beating first-responders to the scene; to deliver blood when there is none on hand at hospitals; to deliver vaccines to an island nation with little transportation infrastructure; and to respond flexibly to medical emergencies in a war zone. Economics make the delivery of food and other cheap goods by drone unattractive in the near-term, but the value and time-sensitivity of medical deliveries mean that drones are already saving lives in healthcare. “We believe the value of new technology is most valuable where it is clearly needed...that’s why we wanted to focus on drones delivering medicine and not delivering pizzas, ”said one executive of a drone system manufacturer. The immediate prospects for the expansion of medical drone use are many; however, they do not exist without their own drawbacks and challenges. Most obvious is the limited range of current commercially-available drones, most of which are isolated to a perimeter of roughly 18 miles. Technological know-how presents another barrier to integration of medical drones on a larger scale. Reports from the United Nations frequently cite a“skill deficit”—a prohibitively low number of qualified drone operators in low-and moderate-income countries (LMICs). Another perhaps more discreet speed bump in global drone development and usage are the various regulations on drone usage. Drone technology has developed so quickly that many states, out of an excess of caution, have nearly snuffed out the fledgling industry with regulation. There also exist significant concerns over the security of private citizens, the efficacy of medical deliveries, and the costs of drone operation. It is these last three barriers which this study will seek to overcome. Put simply, the prospect for human development in LMICs from drone-based medical delivery is far too great to disregard. As of 2020, 3.4 billion people live in rural communities, containing fewer than 5,000 people/km^2. Often lacking infrastructure, these communities are largely isolated from their more populated, urban counterparts. In drones lies the potential to reshape the geographic and developmental distinctions that divide the global population. This development must, therefore, begin first and foremost with advancement in regional well-being and life expectancy. Life expectancy makes up a key facet of human development. The United Nations relies on it as a key indicator of a state’s health. Lars Kunze of the Dortmund University Department of Economic sex plains this as a matter of physical capital accumulation. The longer people live, the more they save as opposed to spend. The more they save, the more which eventually gets invested in themselves and the community as a whole. In providing medical products via drone, it is the intention of this study to enable communities with the means and incentives for long-run savings and investment for future economic development. Through a close analysis of Vanuatu, Rwanda, Tanzania, and Ukraine—four states where drones are currently used to deliver medical supplies—this study develops a framework that LMICs in general and Mexico and particular can adopt and to use medical drones in difficult-to-reach communities for the sake of long-run human developmental initiatives.
This paper presents solution assessments and grid convergence studies for the test cases outlined in the Fifth High-Lift Prediction Workshop (HLPW-5), focusing on the high-lift Common Research Models (CRM-HL). The study utilizes a wall-modeled large-eddy simulation (WMLES) methodology developed in the unstructured-grid, node-centered flow solver FUN3D. The second-order accurate simulations conducted in this study utilize a finite-volume spatial discretization and an implicit temporal scheme. Large-scale turbulent features are resolved away from the wall, with small-scale effects captured by the Vreman subgrid-scale model. An equilibrium wall function uses the first grid point off the wall serving as the critical interface between the wall model and the large-eddy simulation region, thus requiring careful placement in grid design. WMLES solutions are assessed for HLPW-5 cases, including a clean wing-body configuration and geometry-buildup configurations corresponding to the 5.1\% ONERA CRM-HL model. Grid-convergence studies are systematically conducted using uniformly refined grids. Moreover, simulation results and grid sensitivity are presented for the NASA 5.2\% CRM-HL configuration at both moderate and flight-scale Reynolds numbers. Overall, WMLES results are satisfactory and agree well with available experimental data, especially on sufficiently fine grids.