Data information system at the National Space Science Data Center
Integrated information system to support data handling activities of National Science Data Center
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Integrated information system to support data handling activities of National Science Data Center
he exponential growth of data centers—driven by artificial intelligence and cloud computing—is reshaping the U.S. energy landscape, presenting urgent challenges and transformative opportunities for data center developers, nuclear energy providers, and utility operators. As data centers are projected to consume up to 12% of U.S. electricity by 2028, stakeholders must address rapid deployment needs, grid congestion, and the demand for reliable, high-quality power. This presentation explores the multifaceted barriers to integrating data centers with nuclear and utility infrastructure, including land use constraints, public perception, regulatory complexity, and workforce alignment. It highlights the distinct priorities and operational cultures of each sector, and the friction that arises from misaligned planning horizons and risk tolerances. We examine collaborative strategies such as co-siting, hybrid power-purchase agreements, unified community engagement, and innovative financing models.
The amount of hydrological data available from NASA remote sensing and modeling systems is vast and ever-increasing;but, one challenge persists:increasing the usefulness of these data for, and thus their use by, end user communities. The Hydrology Data and Information Services Center (HDISC), part of the Goddard Earth Sciences DISC, has continually worked to better understand the hydrological data needs of different end users, to thus better able to bridge the gap between NASA data and end user communities. One effective strategy is integrating the data in to end user community tools and environments. There is an ongoing collaborative effort between NASA HDISC, NASA Hydrological Sciences Branch, and CUAHSI to integrate NASA gridded hydrology data in to the CUAHSI Hydrologic Information System (HIS).
The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP.
Gamma-ray lines are produced in nature by a variety of different physical processes. They can be valuable astrophysical diagnostics providing information the may be unobtainable by other means. We have carried out an extensive search for gamma-ray lines in the first year of public data from the Spectrometer (SPI) on the INTEGRAL mission. INTEGRAL has spent a large fraction of its observing time in the Galactic Plane with particular concentration in the Galactic Center (GC) region (approximately 3 Msec in the first year). Hence the most sensitive search regions are in the Galactic Plane and Center. The phase space of the search spans the energy range 20-8000 keV, and line widths from 0-1000 keV (FWHM) and includes both diffuse and point-like emission. We have searched for variable emission on time scales down to approximately 1000 sec. Diffuse emission has been searched for on a range of different spatial scales from approximately 20 degrees (the approximate field-of-view of the spectrometer) up to the entire Galactic Plane. Our search procedures were verified by the recovery of the known gamma-ray lines at 511 keV and 1809 keV at the appropriate intensities and significances. We find no evidence for any previously unknown gamma-ray lines. The upper limits range from a few x10(exp -5) per square centimeter per second to a few x10(exp -3) per square centimeter per second depending on line width, energy and exposure. Comparison is made between our results and various prior predictions of astrophysical lines
The Space Station Data Management System (DMS) comprises the networked computers, mass storage, workstations, and instrumentation interfaces required to support onboard systems and payload operations. This paper gives an overview of the current DMS architecture and discusses its role as onboard integrator in four of its major functional areas: (1) data communication; (2) data processing; (3) data administration, storage and retrieval; and (4) data presentation at the human-computer interface.
his research investigates the combination of task and data parallel language constructs within a single programming language. There are an number of applications that exhibit properties which would be well served by such an integrated language. Examples include global climate models, aircraft design problems, and multidisciplinary design optimization problems. Our approach incorporates data parallel language constructs into an existing, object oriented, task parallel language. The language will support creation and manipulation of parallel classes and objects of both types (task parallel and data parallel). Ultimately, the language will allow data parallel and task parallel classes to be used either as building blocks or managers of parallel objects of either type, thus allowing the development of single and multi-paradigm parallel applications. 1995 Research Accomplishments In February I presented a paper at Frontiers '95 describing the design of the data parallel language subset. During the spring I wrote and defended my dissertation proposal. Since that time I have developed a runtime model for the language subset. I have begun implementing the model and hand-coding simple examples which demonstrate the language subset. I have identified an astrophysical fluid flow application which will validate the data parallel language subset. 1996 Research Agenda Milestones for the coming year include implementing a significant portion of the data parallel language subset over the Legion system. Using simple hand-coded methods, I plan to demonstrate (1) concurrent task and data parallel objects and (2) task parallel objects managing both task and data parallel objects. My next steps will focus on constructing a compiler and implementing the fluid flow application with the language. Concurrently, I will conduct a search for a real-world application exhibiting both task and data parallelism within the same program m. Additional 1995 Activities During the fall I collaborated with Andrew Grimshaw and Adam Ferrari to write a book chapter which will be included in Parallel Processing in C++ edited by Gregory Wilson. I also finished two courses, Compilers and Advanced Compilers, in 1995. These courses complete my class requirements at the University of Virginia. I have only my dissertation research and defense to complete.
This research investigates the combination of task and data parallel language constructs within a single programming language. There are an number of applications that exhibit properties which would be well served by such an integrated language. Examples include global climate models, aircraft design problems, and multidisciplinary design optimization problems. Our approach incorporates data parallel language constructs into an existing, object oriented, task parallel language. The language will support creation and manipulation of parallel classes and objects of both types (task parallel and data parallel). Ultimately, the language will allow data parallel and task parallel classes to be used either as building blocks or managers of parallel objects of either type, thus allowing the development of single and multi-paradigm parallel applications. 1995 Research Accomplishments In February I presented a paper at Frontiers 1995 describing the design of the data parallel language subset. During the spring I wrote and defended my dissertation proposal. Since that time I have developed a runtime model for the language subset. I have begun implementing the model and hand-coding simple examples which demonstrate the language subset. I have identified an astrophysical fluid flow application which will validate the data parallel language subset. 1996 Research Agenda Milestones for the coming year include implementing a significant portion of the data parallel language subset over the Legion system. Using simple hand-coded methods, I plan to demonstrate (1) concurrent task and data parallel objects and (2) task parallel objects managing both task and data parallel objects. My next steps will focus on constructing a compiler and implementing the fluid flow application with the language. Concurrently, I will conduct a search for a real-world application exhibiting both task and data parallelism within the same program. Additional 1995 Activities During the fall I collaborated with Andrew Grimshaw and Adam Ferrari to write a book chapter which will be included in Parallel Processing in C++ edited by Gregory Wilson. I also finished two courses, Compilers and Advanced Compilers, in 1995. These courses complete my class requirements at the University of Virginia. I have only my dissertation research and defense to complete.
Since the NASA Global Energy Water Exchanges (GEWEX) Surface Radiation Budget (SRB) project released its 3rd version of products in 2010, the GEWEX Data Assessments Panel (GDAP) has been working on integrating various data products to address issues in the closing of the global energy and water cycles. The 4th version of the SRB products, Rel. 4.0-IP, has integrated data products from the cloud, aerosol, atmosphere, ocean surface, and land surface projects, coordinating within GDAP, to produce a long-term time series of TOA and surface radiative estimates. The Rel. 4-IP shortwave products span 34 years continuously from July 1983 to June 2017 on a quasi-equal-area 1degree longitude by 1degree latitude grid system. The longwave products are for land only from 1983-07 to 1987-12; for both land and ocean from 1988-01 to 2009-12; and ocean only from 2010-01 to 2017-06. The data are provided at 3 hourly, 3-hourly-monthly, daily and monthly means. The ISCCP HXS clouds and radiances are the key cloud input of the current GEWEX SRB algorithms. In addition, the longwave algorithm has also made changes in cloud microphysical property, surface skin temperature input, surface emissivity, atmospheric profile, adding longwave aerosol optical properties, revising cloud overlap procedure, and so on. Details of changes in both inputs and algorithms are documented in a NASA Algorithm Theoretical Basis Document (ATBD). We have validated the surface longwave downward fluxes against the surface-based Baseline Surface Radiation Network (BSRN) and the Pacific Marine Environmental Laboratory (PMEL) buoy data. As of 2020, the BSRN archive has 12,116 site-months of observed records from 73 stations on all seven continents, and as of 2017, PMEL archive has 4389 buoy months of observed records from 64 buoys deployed in the tropics of Pacific, Atlantic and Indian Oceans. This paper presents how the SRB Rel. 4.0-IP surface longwave downward fluxes compare with these surface-based measurements and how the comparison statistics differ from that of Rel. 3.0.
Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.
Bit error analysis for PCM data link using integrate and dump mode of data regeneration
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Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite-derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R 2 = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R 2 = 0.61, with typical point differences <0.10 m (∼7% of the 1.5 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely (R 2 = 0.86). A simple phase adjustment of ∼26,min between TPXO and SWOT lowers the RMSE from 0.18,m to 0.11,m, showing that phase offset accounts for some of the discrepancy, with additional errors likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.
One Modeling Framework for Integrated Tasks (OMFIT) is a widely used software tool in the magnetic fusion research community. OMFIT provides magnetic fusion energy researchers with a framework for the development of special-purpose physics modules. This paper describes an OMFIT physics module pertaining to the Doppler Backscattering (DBS) fusion plasma diagnostic. DBS measures density fluctuations and flow velocity through plasma scattering of electromagnetic waves. The OMFIT DBS module was developed to analyze experimental DBS data and facilitate modeling of DBS systems installed on multiple tokamak devices. The OMFIT DBS module is designed to support several analysis workflows: detailed analysis of experimental data, experimental planning, and theory-based synthetic diagnostic modeling. The DBS module uses integrated modeling by leveraging other OMFIT physics modules to perform tasks related to DBS, e.g. ray/beam–tracing simulations, edge-localized mode–synchronized data analysis, magnetic equilibrium reconstruction, and fitting kinetic profile data. Furthermore, this paper describes several supported workflows and serves a reference for the OMFIT DBS module.
Flush air data sensing (FADS) systems have been previously used at both Earth and Mars to provide onboard estimates of angle of attack, sideslip angle, and dynamic pressure. However, these FADS data were often not used in an in-the-loop sense to inform the onboard guidance and control systems. A method to integrate FADS-derived density and wind estimates with a numerical predictor-corrector guidance algorithm is presented. The method is demonstrated in a high-fidelity simulation of a human-scale Mars entry vehicle that utilizes a hypersonic inflatable aerodynamic decelerator (HIAD) with direct force control. Effects on guidance commands and state uncertainties both with and without FADS system modeling are presented and discussed.
Four-flux theory is a way to model scattering through multiple layers of a system based on diffuse and collimated properties. When compared with measurement results obtained using an integrating sphere or a goniophotometer, an approximation is often made as the physical instrument cannot separate the collimated component from the diffuse light scattered in the forward direction. This paper tries to clarify the meaning of the word diffuse for the different cases and outlines simple corrections to improve the accuracy when comparing four-flux models and measured data based on sample haze and the geometry of the integrating sphere.
Integral inversion of Mariner 4 occultation data for Mars atmosphere analysis
Severe storms research is characterized by temporal scales ranging from minutes (for thunderstorms and tornadoes) to hours (for hurricanes and extra-tropical cyclones). Spatial scales range from tens to hundreds of kilometers. Sources of observational data include a variety of ground based and satellite systems. Requirements for registration and intercomparison of data from these various sources are examined and the potential for operational forecasting application of techniques resulting from the research is discussed. The sensor characteristics and processing procedures relating to the overlay and integrated analysis of satellite and surface observations for severe storms research are reviewed.