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Background Evidence establishing effectiveness of influenza vaccination for prevention of severe illness is limited. The US Hospitalized Adult Influenza Vaccine Effectiveness Network (HAIVEN) is a multiyear test-negative case-control study initiated in 2015–2016 to estimate effectiveness of vaccine in preventing influenza hospitalization among adults. Methods Adults aged ≥18 years admitted to 8 US hospitals with acute respiratory illness and testing positive for influenza by polymerase chain reaction were cases; those testing negative were controls. Vaccine effectiveness was estimated with logistic regression adjusting for age, comorbidities, and other confounding factors and stratified by frailty, 2-year vaccination history, and clinical presentation. Results We analyzed data from 236 cases and 1231 controls; mean age was 58 years. More than 90% of patients had ≥1 comorbidity elevating risk of influenza complications. Fifty percent of cases and 70% of controls were vaccinated. Vaccination was 51% (95% confidence interval [CI], 29%–65%) and 53% (95% CI, 11%–76%) effective in preventing hospitalization due to influenza A(H1N1)pdm09 and influenza B virus infection, respectively. Vaccine was protective for all age groups. Conclusions During the 2015–2016 US influenza A(H1N1)pdm09–predominant season, we found that vaccination halved the risk of influenza-association hospitalization among adults, most of whom were at increased risk of serious influenza complications due to comorbidity or age.

Although the question of who pays for open data is important (“Who will pay for public access to research data?”, F. Berman and V. Cerf, Policy Forum, 9 August, p. [616][1]), a greater challenge lies in implementing the institutional and cultural changes required before data from government-sponsored research can be openly shared. The Office of Science and Technology Policy (OSTP) has ordered U.S. federal agencies to formulate plans to share federally funded science data ([ 1 ][2]). This reflects a fundamental shift in the social contract between scientists and society. While seeking to strengthen science, the order also seeks better use of data to promote economic innovation, improve cross-disciplinary efforts, and address “grand challenge” societal problems such as global climate change and urban violence. The OSTP memo correctly notes that public availability of atmospheric data enabled commercial weather services and severe weather prediction. Yet many data, tools, and models in the geosciences are held by a mix of individual investigators, national data centers, university-based initiatives, and commercial labs, embedded in institutional arrangements that actively reward holding onto data and maximizing individual outcomes in a competitive environment. NSF's EarthCube project, a long-term strategic initiative to build the cyber infrastructure for integrating data, tools, and models in the geosciences, illustrates the challenges and benefits of community engagement and institutional alignment ([ 2 ][3]). The push for open data goes beyond the question of who pays. It challenges science to create a more cooperative culture that aligns credit and rewards with sharing data, tools, and models. 1. [↵][4] OSTP, Expanding Public Access to the Results of Federally Funded Research ([www.whitehouse.gov/blog/2013/02/22/expanding-public-access-results-federally-funded-research][5]). 2. [↵][6] EarthCube ([www.earthcube.org][7]). [1]: /lookup/doi/10.1126/science.1241625 [2]: #ref-1 [3]: #ref-2 [4]: #xref-ref-1-1 "View reference 1 in text" [5]: http://www.whitehouse.gov/blog/2013/02/22/expanding-public-access-results-federally-funded-research [6]: #xref-ref-2-1 "View reference 2 in text" [7]: http://www.earthcube.org

Background Healthcare provider immunization education is vital to accurately address concerns regarding vaccines, but such training is not standardized across residency programs. To assess educational needs and develop a vaccine curriculum for pediatric (Peds) and family medicine (FM) residents, the Collaboration for Vaccination Education and Research (CoVER) was established. There is a need to identify knowledge, attitudes, and hesitancy regarding vaccines among residents. Methods In July 2017, an anonymous 30-item survey was sent to residents from 26 US institutions participating in CoVER. Items included (1) vaccine knowledge, (2) attitudes toward vaccines, (3) resident vaccine hesitancy, and (4) demographics. Differences in proportions were calculated using Fisher’s Exact test while the Kruskal–Wallis test was used to compare continuous outcomes. Results Of 1,447 residents invited, 746 completed the survey (52% response rate). Among participants, 12 were excluded due to inability to determine residency type and or year. The final cohort consisted of 734 residents (Table 1). Knowledge (Figure 1): Percent correct increased with residency year from PGY1 to PGY4 (49%, [95% CI 47–51]; 64% [95% CI 58–70]; test for trend P < 0.001). Compared with FM residents, Peds residents were more likely to answer knowledge questions correctly (56%; 49%; P < 0.001). Attitudes (Table 2): Confidence in communicating with parents increased with training (P < 0.001) but confidence in vaccination did not. Hesitancy (Figure 2): Three percent of residents (n = 21) self-reported as vaccine hesitant. They were more likely to be FM (75%, P < 0.001). Residents were more likely to delay a vaccine in someone without a medical contraindication with increased year of training (P < 0.001). Conclusion This initial assessment of the residents’ knowledge, attitudes and hesitancy shows that despite increasing knowledge with training, vaccine confidence was unaffected. It was surprising to find hesitancy among residents, highlighting the need for further vaccine education. Future steps include evaluation of a vaccine education curriculum in residency training to increase confidence in the benefits of vaccination. Disclosures B. Pahud, Pfizer Foundation: Grant Investigator, Research grant. GlaxoSmithKline: Investigator, Salary. Alios Biopharma/Janssen: Investigator, Salary. Pfizer: Consultant, Consulting fee and Speaker honorarium. Sequirus: Consultant, Consulting fee. Sanofi Pasteur: Consultant, Consulting fee. B. R. Lee, PCORI: Grant Investigator, Research grant. KCALSI: Grant Investigator, Research grant. Merck: Investigator, Salary. D. Middleton, Merck: Scientific Advisor, Consulting fee. Pfizer: Scientific Advisor, Consulting fee. GlaxoSmithKline: Scientific Advisor, Consulting fee. Sanofi Pasteur: Scientific Advisor, Consulting fee.

Background Seasonal influenza causes substantial morbidity and mortality, and older adults are disproportionately affected. Newer vaccines have been developed for use in people 65 years and older, including a trivalent inactivated vaccine with a 4-fold higher dose of antigen (IIV-HD). In recent years, the use of IIV-HD has increased sufficiently to evaluate its effectiveness compared with standard-dose inactivated influenza vaccines (IIV-SD). Methods Hospitalized patients with acute respiratory illness were enrolled in an observational vaccine effectiveness study at 8 hospitals in 4 states participating in the United States Hospitalized Adult Influenza Vaccine Effectiveness Network during the 2015–2016 and 2016–2017 influenza seasons. Predominant influenza A virus subtypes were H1N1 and H3N2, respectively, during these seasons. All enrolled patients were tested for influenza virus with polymerase chain reaction. Receipt and type of influenza vaccine was determined from electronic records and chart review. Odds of laboratory-confirmed influenza were compared among vaccinated and unvaccinated patients. Relative odds of laboratory-confirmed influenza were determined for patients who received IIV-HD or IIV-SD, and adjusted for potential confounding variables via logistic regression. Results Among 1,744 enrolled patients aged ≥ 65 years, 1,105 (63%) were vaccinated; among those vaccinated, 621 (56%) received IIV-HD and 484 (44%) received IIV-SD. Overall, 315 (18%) tested positive for influenza, including 97 (6%) who received IIV-HD, 86 (5%) who received IIV-SD, and 132 (8%) who were unvaccinated. Controlling for age, race, sex, enrollment site, date of illness, index of comorbidity, and influenza season, the adjusted odds of influenza among patients vaccinated with IIV-HD vs. IIV-SD were 0.72 (P = 0.06, 95% CI: 0.52 to 1.01). Conclusion Comparison of high-dose vs. standard-dose vaccine effectiveness during 2 recent influenza seasons (1 H1N1 and 1 H3N2-predominant) suggested relative benefit (nonsignificant) of high-dose influenza vaccine in protecting against influenza-associated hospitalization among persons aged 65 years and older; additional years of data are needed to confirm this finding. Disclosures H. K. Talbot, sanofi pasteur: Investigator, Research grant. Gilead: Investigator, Research grant. MedImmune: Investigator, Research grant. Vaxinnate: Safety Board, none. Seqirus: Safety Board, none.

Earth science high-performance applications often require extensive analysis of their output in order to complete the scien- tific goals or produce a visual image or animation. Often this analysis cannot be done in situ because it requires calculating time-series statistics from state sampled over the entire length of the run or analyzing the relationship between similar time series from previous simulations or observations. Many of the tools used for this postprocessing are not themselves high- performance applications, but the new Parallel Gridded Analysis Library (ParGAL) provides high-performance data-parallel versions of several common analysis algorithms for data from a structured or unstructured grid simulation. The library builds on several scalable systems, including the Mesh Oriented DataBase (MOAB), a library for representing mesh data that sup- ports structured, unstructured finite element, and polyhedral grids; the Parallel-NetCDF (PNetCDF) library; and Intrepid, an extensible library for computing operators (such as gradient, curl, and divergence) acting on discretized fields. We have used ParGAL to implement a parallel version of the NCAR Command Language (NCL) a scripting language widely used in the climate community for analysis and visualization. The data-parallel algorithms in ParGAL/ParNCL are both higher performing and more flexible than their serial counterparts.

We have developed a semantic data framework that supports interdisciplinary virtual observatory projects across the fields of solar physics, space physics and solar-terrestrial physics. This work required a formal, machine understandable representation for concepts, relations and attributes of physical quantities in the domains of interest as well as their underlying data representations. To fulfill this need, we developed a set of solar-terrestrial ontologies as formal encodings of the knowledge in the Ontology Web Language-Description Logic (OWL-DL) format. We present our knowledge representation and reasoning needs motivated by the context of Virtual Observatories, from fields spanning upper atmospheric terrestrial physics to solar physics, whose intent is to provide access to observational datasets. The resulting data framework is built upon semantic web methodologies and technologies and provides virtual access to distributed and heterogeneous sets of data as if all resources appear to be organized, stored and retrieved from a local environment. Our conclusion is that the combination of use case-driven, small and modular ontology development, coupled with free and open-source software tools and languages provides sufficient expressiveness and capabilities for an initial production implementation and sets the stage for a more complete semantic-enablement of future frameworks.

The Earth System Curator is a National Science Foundation sponsored project developing a metadata formalism for describing the digital resources used in climate simulations. The primary motivating observation of the project is that a simulation/model’s source code plus the configuration parameters required for a model run are a compact representation of the dataset generated when the model is executed. The end goal of the project is a convergence of models and data where both resources are accessed uniformly from a single registry. In this paper we review the current metadata landscape of the climate modeling community, present our work on developing a metadata formalism for describing climate models, and reflect on technical challenges we have faced that require new research in the area of Earth Science Informatics.

With support from the U.S. Department of Energy's Scientific Discover Through Advanced Computing (SciDAC) program, we have developed and deployed the Earth System Grid (ESG) to make climate simulation data easily accessible to the global climate modelling and analysis community. ESG currently has 2500 registered users and manages 160 TB of data in archives distributed around the nation. From this past year alone, more than 200 scientific journal articles have been published from analyses of data delivered by the ESG.

ParVis was a project funded under LAB 10-05: “Earth System Modeling: Advanced Scientific Visualization of Ultra-Large Climate Data Sets”. Argonne was the lead lab with partners at PNNL, SNL, NCAR and UC-Davis. This report covers progress from January 1st, 2013 through Dec 1st, 2014. Two previous reports covered the period from Summer, 2010, through September 2011 and October 2011 through December 2012, respectively. While the project was originally planned to end on April 30, 2013, personnel and priority changes allowed many of the institutions to continue work through FY14 using existing funds. A primary focus of ParVis was introducing parallelism to climate model analysis to greatly reduce the time-to-visualization for ultra-large climate data sets. Work in the first two years was conducted on two tracks with different time horizons: one track to provide immediate help to climate scientists already struggling to apply their analysis to existing large data sets and another focused on building a new data-parallel library and tool for climate analysis and visualization that will give the field a platform for performing analysis and visualization on ultra-large datasets for the foreseeable future. In the final 2 years of the project, we focused mostly on the new data-parallel librarymore » and associated tools for climate analysis and visualization.« less

Understanding the earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array of distributed supercomputers. The resulting data archive, spread over several sites, currently contains upwards of 100 TB of simulation data and is growing rapidly. Looking toward mid-decade and beyond, we must anticipate and prepare for distributed climate research data holdings of many petabytes. The Earth System Grid (ESG) is a collaborative interdisciplinary project aimed at addressing the challenge of enabling management, discovery, access, and analysis of these critically important datasets in a distributed and heterogeneous computational environment. The problem is fundamentally a Grid problem. Building upon the Globus toolkit and a variety of other technologies, ESG is developing an environment that addresses authentication, authorization for data access, large-scale data transport and management, services and abstractions for high-performance remote data access, mechanisms for scalable data replication, cataloging with rich semantic and syntactic information, data discovery, distributed monitoring, and Web-based portals for using the system.

In numerous scientific disciplines, terabyte and petabyte-scale data collections are emerging as critical community resources. A new class of "data grid" infrastructure is required to support management, transport, distributed access to, and analysis of these datasets by potentially thousands of users. Researchers who face this challenge include the climate modeling community, which performs long-duration computations accompanied by frequent output of very large files that must be further analyzed. We describe the Earth System Grid-I prototype, which brings together advanced analysis, replica management, data transfer, request management, and other technologies to support high-performance, interactive analysis of replicated data. We present performance results that demonstrate our ability to manage the location and movement of large datasets from the user's desktop. We report on experiments conducted over SciNET at SC'2000, where we achieved peak performance of 1.55 Gb/s and sustained performance of 512.9 Mb/s for data transfers between Texas and California. Finally, we describe the development of the next-generation Earth System Grid-II (ESG-II) project. Important issues for ESG-II include security requirements for production environments, efficient data filtering and transport, metadata services for discovery of relevant climate datasets, and sophisticated request or workflow management for complex tasks.

Results from numerical simulations of the Colorado Front Range downslope windstorm of 9 December 1992 are presented. Although this case was not characterized by severe surface winds, the event caused extreme clearair turbulence (CAT) aloft, as indicated by the severe structural damage experienced by a DC-8 cargo jet at 9.7 km above mean sea level over the mountains. Detailed measurements from the National Oceanic and Atmospheric Administration/Environmental Research Laboratories/Environmental Technology Laboratory Doppler lidar and wind profilers operating on that day and from the Defense Meteorological Satellite Program satellite allow for a uniquely rich comparison between the simulations and observations. Four levels of grid refinement were used in the model. The outer domain used National Centers for Environmental Prediction data for initial and boundary conditions. The finest grid used 200 m in all three dimensions over a 48 km by 48 km section. The range of resolution and domain coverage were sufficient to resolve the abundant variety of dynamics associated with a time-evolving windstorm forced during a frontal passage. This full range of resolution and model complexity was essential in this case. Many aspects of this windstorm are inherently three-dimensional and are not represented in idealized models using either 2D or so-called 2D‐3D dynamics. Both the timing and location of wave breaking compared well with observations. The model also reproduced cross-stream wavelike perturbations in the jet stream that compared well with the orientation and spacing of cloud bands observed by satellite and lidar. Model results also show that the observed CAT derives from interactions between these wavelike jet stream disturbances and mountain-forced internal gravity waves. Due to the nearly east‐west orientation of the jet stream, these two interacting wave modes were orthogonal to each other. Thermal gradients associated with the intense jet stream undulations generated horizontal vortex tubes (HVTs) aligned with the mean flow. These HVTs remained aloft while they propagated downstream at about the elevation of the aircraft incident, and evidence for such a vortex was seen by the lidar. The model and observations suggest that one of these intense vortices may have caused the aircraft incident. Reports of strong surface gusts were intermittent along the Front Range during the period of this study. The model showed that interactions between the gravity waves and flow-aligned jet stream undulations result in isolated occurrences of strong surface gusts in line with observations. The simulations show that strong shears on the upper and bottom surfaces of the jet stream combine to provide an episodic ‘‘downburst of turbulence.’’ In the present case, the perturbations of the jet stream provide a funnel-shaped shear zone aligned with the mean flow that acts as a guide for the downward transport of turbulence resulting from breaking gravity waves. The physical picture for the upper levels is similar to the surface gusts described by Clark and Farley resulting from vortex tilting. The CAT feeding into this funnel came from all surfaces of the jet stream with more than half originating from the vertically inclined shear zones on the bottom side of the jet stream. Visually the downburst of turbulence looks similar to a rain shaft plummeting to the surface and propagating out over the plains leaving relatively quiescent conditions behind.

A good physical understanding of the initiation, propagation, and spread of crown fires remains an elusive goal for fire researchers. Although some data exist that describe the fire spread rate and some qualitative aspects of wildfire behavior, none have revealed the very small timescales and spatial scales in the convective processes that may play a key role in determining both the details and the rate of fire spread. Here such a dataset is derived using data from a prescribed burn during the International Crown Fire Modelling Experiment. A gradient-based image flow analysis scheme is presented and applied to a sequence of high-frequency (0.03 s), high-resolution (0.05‐0.16 m) radiant temperature images obtained by an Inframetrics ThermaCAM instrument during an intense crown fire to derive wind fields and sensible heat flux. It was found that the motions during the crown fire had energy-containing scales on the order of meters with timescales of fractions of a second. Estimates of maximum vertical heat fluxes ranged between 0.6 and 3 MW m 22 over the 4.5-min burn, with early time periods showing surprisingly large fluxes of 3 MW m 22. Statistically determined velocity extremes, using five standard deviations from the mean, suggest that updrafts between 10 and 30 m s21, downdrafts between 210 and 220 m s21, and horizontal motions between 5 and 15 m s21 frequently occurred throughout the fire. The image flow analyses indicated a number of physical mechanisms that contribute to the fire spread rate, such as the enhanced tilting of horizontal vortices leading to counterrotating convective towers with estimated vertical vorticities of 4 to 10 s 21 rotating such that air between the towers blew in the direction of fire spread at canopy height and below. The IR imagery and flow analysis also repeatedly showed regions of thermal saturation (infrared temperature . 7508C), rising through the convection. These regions represent turbulent bursts or hairpin vortices resulting again from vortex tilting but in the sense that the tilted vortices come together to form the hairpin shape. As the vortices rise and come closer together their combined motion results in the vortex tilting forward at a relatively sharp angle, giving a hairpin shape. The development of these hairpin vortices over a range of scales may represent an important mechanism through which convection contributes to the fire spread. A major problem with the IR data analysis is understanding fully what it is that the camera is sampling, in order physically to interpret the data. The results indicate that because of the large amount of after-burning incandescent soot associated with the crown fire, the camera was viewing only a shallow depth into the flame front, and variabilities in the distribution of hot soot particles provide the structures necessary to derive image flow fields. The coherency of the derived horizontal velocities support this view because if the IR camera were seeing deep into or through the flame front, then the effect of the ubiquitous vertical rotations almost certainly would result in random and incoherent estimates for the horizontal flow fields. Animations of the analyzed imagery showed a remarkable level of consistency in both horizontal and vertical velocity flow structures from frame to frame in support of this interpretation. The fact that the 2D image represents a distorted surface also must be taken into account when interpreting the data. Suggestions for further field experimentation, software development, and testing are discussed in the conclusions. These suggestions may further understanding on this topic and increase the utility of this type of analysis to wildfire research.

The Earth’s climate—its average weather and frequency of extreme events—greatly affects the conditions of all living creatures. Human conditions, for example, are strongly influenced by the availability of fresh water, the ambient temperature and humidity, and extreme phenomena such as heat and cold waves, droughts and floods, and tornadoes and hurricanes. Dramatic changes in climatic phenomena (e.g. a Medieval warm period or the prehistoric Ice Ages) also have been recorded, either in human chronicles or in the natural history of the Earth itself.

Drawing to a close after five years of funding from DOE's ASCR and BER program offices, the SciDAC-2 project called the Earth System Grid (ESG) Center for Enabling Technologies has successfully established a new capability for serving data from distributed centers. The system enables users to access, analyze, and visualize data using a globally federated collection of networks, computers and software. The ESG software - now known as the Earth System Grid Federation (ESGF) - has attracted a broad developer base and has been widely adopted so that it is now being utilized in serving the most comprehensive multi-model climate data sets in the world. The system is used to support international climate model intercomparison activities as well as high profile U.S. DOE, NOAA, NASA, and NSF projects. It currently provides more than 25,000 users access to more than half a petabyte of climate data (from models and from observations) and has enabled over a 1,000 scientific publications.

The fifth Coupled Model Intercomparison Project (CMIP5) will involve the global production and analysis of petabytes of data. The Program for Climate Model Diagnosis and Intercomparison (PCMDI), with responsibility for archival for CMIP5, has established the global “Earth System Grid Federation” (ESGF) of data producers and data archives to support CMIP5. ESGF will provide a set of globally synchronised views of globally distributed data – including some large cache replicants which will be persisted for (at least) decades. Here we describe the archive requirements and key aspects of the resulting architecture. ESGF will stress international networks, as well as the data archives themselves – but significantly less than would have been the case of a centralised archive. Developing and deploying the ESGF has exploited good will and best efforts, but future developments are likely to require more formalised architecture and management.

The Community Earth System Model (CESM) is a widely used community model for studying the climate system on the Earth. The CESM model is both data and computationally intensive, making it difficult for users to set up and run CESM simulations using local resources. In this paper, we describe an integrated climate modeling environment that supports CESM simulations on the TeraGrid, comprehensive model metadata description, and automatic archival of model data and metadata for easy community access. This system builds upon and integrates several existing efforts -- the Purdue CCSM modeling portal, the Earth System Grid, the Earth System Modeling Framework, and the Earth System Curator. We present the design and implementation of our prototype system as well as an end-to-end usage scenario which is broken down into three workflows: model execution, data publishing, and metadata collection/publishing. The system will be used to support research and education on climate systems. We describe our plan and early efforts to engage users and obtain their feedback.

This report summarizes work carried out by the ESG-CET during the period October 1, 2009 through March 31, 2009. It includes discussion of highlights, overall progress, period goals, collaborations, papers, and presentations. To learn more about our project, and to find previous reports, please visit the Earth System Grid Center for Enabling Technologies (ESG-CET) website. This report will be forwarded to the DOE SciDAC program management, the Office of Biological and Environmental Research (OBER) program management, national and international collaborators and stakeholders (e.g., the Community Climate System Model (CCSM), the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5), the Climate Science Computational End Station (CCES), the SciDAC II: A Scalable and Extensible Earth System Model for Climate Change Science, the North American Regional Climate Change Assessment Program (NARCCAP), and other wide-ranging climate model evaluation activities).

In this paper, we discuss recent development and implementation efforts by the Earth System Grid (ESG) concerning its security infrastructure. ESG's requirements are to make user logon as easy as possible and to facilitate the integration of security services and Grid components for both developers and system administrators. To meet that goal, we leverage existing primary authentication mechanisms, deploy a "lightweight" but secure OpenID WebSSO, deploy a "lightweight" X.509-PKI, and use autoprovisioning to ease the burden of security configuration management. We are close to completing the associated development and deployment.

The Earth System Grid (ESG) II project, funded by the Department of Energy’s Scientific Discovery through Advanced Computing program, has transformed climate data into community resources. ESG II has accomplished this goal by creating a virtual collaborative environment that links climate centers and users around the world to models and data via a computing Grid, which is based on the Department of Energy’s supercomputing resources and the Internet. Our project’s success stems from partnerships between climate researchers and computer scientists to advance basic and applied research in the terrestrial, atmospheric, and oceanic sciences. By interfacing with other climate science projects, we have learned that commonly used methods to manage and remotely distribute data among related groups lack infrastructure and under-utilize existing technologies. Knowledge and expertise gained from ESG II have helped the climate community plan strategies to manage a rapidly growing data environment more effectively. Moreover, approaches and technologies developed under the ESG project have impacted datasimulation integration in other disciplines, such as astrophysics, molecular biology and materials science.

There is a critical need to organize, preserve, and make accessible the increasing number of digital holdings that represent intellectual capital. This intellectual capital contains scientific records that are the basis for current research, future scientific advances, and education source materials for use by the public, educators, scientists and engineers now and for the foreseeable future. Chronopolis is a proposed model facility that enables long term support of irreplaceable and important national data collections, ensuring that: 1) standard reference datasets remain available to provide critical science reference material; 2) collections can expand and evolve over time, as well as weather evolution in the underlying technologies; and 3) preservation "of last resort" is available for critical disciplinary and interdisciplinary digital resources at risk of being lost.

Large heterogeneous online repositories of scientific information have the potential to change the way science is done today. In order for this potential to be realized, numerous challenges must be addressed concerning access to and interoperability of the online scientific data. In our work, we are using semantic web technologies to improve access and interoperability by providing a framework for collaboration and a basis for building and distributing advanced data simulation tools. Our initial scientific focus area is the solar terrestrial physics community. In this paper, we will present our work on the Virtual Solar Terrestrial Observatory (VSTO). We will present the emerging trend of the virtual observatory – a virtual integrated evolving scientific data repository – and describe the general use case and our semantically-enabled architecture. We will also present our specific implementation and describe the benefits of the semantic web in this setting. Further, we speculate on the future of the growing adoption of semantic technologies in this important application area of scientific cyberinfrastructure and semantically enabled scientific data repositories.

This chapter provides a high-level overview of visualization in the areas of weather and climate research. The approach is to provide a general sense of the nature of the research problems, the models used to conduct numerical experiments, commonly used visualization tools and techniques, and the underlying data that must be processed, analyzed, visualized, compared, and ultimately understood. When appropriate, some of the real-world challenges and problems that one must surmount while trying to visualize complex geoscientific data in real scientific applications are noted. A number of examples are presented that highlight some specific research thrusts, the visualization approaches taken, and what these efforts represent in terms of future requirements and needs. The examples are intended to be illustrative of current practice as well as challenges in weather and climate visualization. The examples shown in this chapter represent undertakings by close-knit teams of visualization specialists and domain researchers. The chapter concludes by projecting a few years into the future and speculating about future visualization needs and possible avenues for future visualization research and software development.

This document is the report from the Visualization breakoutsession at the SCaLeS (Science Case for Large-scale Simulation) workshopheld in 2003 in Bethesda, MD. The document presents current researchchallenges in visualization of large-scale scientific data.

Scientific visualization is the transformation of abstract information into images, and it plays an integral role in the scientific process by facilitating insight into observed or simulated phenomena. Visualization as a discipline spans many research areas from computer science, cognitive psychology and even art. Yet the most successful visualization applications are created when close synergistic interactions with domain scientists are part of the algorithmic design and implementation process, leading to visual representations with clear scientific meaning. Visualization is used to explore, to debug, to gain understanding, and as an analysis tool. Visualization is literally everywhere--images are present in this report, on television, on the web, in books and magazines--the common theme is the ability to present information visually that is rapidly assimilated by human observers, and transformed into understanding or insight. As an indispensable part a modern science laboratory, visualization is akin to the biologist's microscope or the electrical engineer's oscilloscope. Whereas the microscope is limited to small specimens or use of optics to focus light, the power of scientific visualization is virtually limitless: visualization provides the means to examine data that can be at galactic or atomic scales, or at any size in between. Unlike the traditional scientific toolsmore » for visual inspection, visualization offers the means to ''see the unseeable.'' Trends in demographics or changes in levels of atmospheric CO{sub 2} as a function of greenhouse gas emissions are familiar examples of such unseeable phenomena. Over time, visualization techniques evolve in response to scientific need. Each scientific discipline has its ''own language,'' verbal and visual, used for communication. The visual language for depicting electrical circuits is much different than the visual language for depicting theoretical molecules or trends in the stock market. There is no ''one visualization too'' that can serve as a panacea for all science disciplines. Instead, visualization researchers work hand in hand with domain scientists as part of the scientific research process to define, create, adapt and refine software that ''speaks the visual language'' of each scientific domain.« less

In the emerging world of Grid Computing, shared computational, data, other distributed resources are becoming available to enable scientific advancement through collaborative research and collaboratories. This paper describes the increasing role of ontologies in the context of Grid Computing for obtaining, comparing and analyzing data. We present ontology entities and a declarative model that provide the outline for an ontology of scientific information. Relationships between concepts are also given. The implementation of some concepts described in this ontology is discussed within the context of the Earth System Grid II (ESG)[1].

The international climate community is expected to generate hundreds of petabytes of simulation data within the next five to seven years. This data must be accessed and analyzed by thousands of analysts worldwide in order to provide accurate and timely estimates of the likely impact of climate change on physical, biological, and human systems. Climate change is thus not only a scientific challenge of the first order but also a major technological challenge. In order to address this technological challenge, the Earth System Grid Center for Enabling Technologies (ESG-CET) has been established within the U.S. Department of Energy's Scientific Discovery through Advanced Computing (SciDAC)-2 program, with support from the offices of Advanced Scientific Computing Research and Biological and Environmental Research. ESG-CET's mission is to provide climate researchers worldwide with access to the data, information, models, analysis tools, and computational capabilities required to make sense of enormous climate simulation datasets. Its specific goals are to (1) make data more useful to climate researchers by developing Grid technology that enhances data usability; (2) meet specific distributed database, data access, and data movement needs of national and international climate projects; (3) provide a universal and secure web-based data access portal for broad multi-model data collections; and (4) provide a wide-range of Grid-enabled climate data analysis tools and diagnostic methods to international climate centers and U.S. government agencies. Building on the successes of the previous Earth System Grid (ESG) project, which has enabled thousands of researchers to access tens of terabytes of data from a small number of ESG sites, ESG-CET is working to integrate a far larger number of distributed data providers, high-bandwidth wide-area networks, and remote computers in a highly collaborative problem-solving environment.

The recent release of the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report (AR4) has generated significant media attention. Much has been said about the U.S. role in this report, which included significant support from the Department of Energy through the Scientific Discovery through Advanced Computing (SciDAC) and other Department of Energy (DOE) programs for climate model development and the production execution of simulations. The SciDAC-supported Earth System Grid Center for Enabling Technologies (ESG-CET) also played a major role in the IPCC AR4: all of the simulation data that went into the report was made available to climate scientists worldwide exclusively via the ESG-CET. At the same time as the IPCC AR4 database was being developed, the National Center for Atmospheric Research (NCAR), a leading U.S. climate science laboratory and a ESG participant, began publishing model runs from the Community Climate System Model (CCSM), and its predecessor the Parallel Coupled Model (PCM) through ESG. In aggregate, ESG-CET provides seamless access to over 180 terabytes of distributed climate simulation data to over 6,000 registered users worldwide, who have taken delivery of more than 250 terabytes from the archive. Not only does this represent a substantial advance in scientific knowledge, it is also a major step forward in how we conduct the research process on a global scale. Moving forward, the next IPCC assessment report, AR5, will demand multi-site metadata federation for data discovery and cross-domain identity management for single sign- on of users in a more diverse federation enterprise environment. Towards this aim, ESG is leading the effort in the climate community towards standardization of material for the global federation of metadata, security, and data services required to standardize, analyze, and access data worldwide.