In this paper, we will discuss how NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) is distributing large volumes of `structured' data using Daily Surface Weather Data and a corresponding Climatological Summaries Dataset (Daymet) as an example.

Data of all sizes, generated by simulation and observation (i.e., instruments and satellites) activities, should be collected, stored, and organized, along with associated tools and research results, so that they are easily discoverable and accessible. Most observational data capture conditions at an exact point in time and are thus not reproducible, therefore it is imperative that initial data be captured and stored correctly the first time. In this paper, we will discuss how NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) is preparing, storing, and distributing large volumes of multi-dimensional scientific data using Daily Surface Weather Data and a corresponding Climatological Summaries Dataset (Daymet) as an example.

Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization theories and principles are instantiated in practice. This is especially relevant when domain experts, and not visualization researchers, design visualizations for analysis of their data or for broader dissemination of scientific knowledge. There is very little research on exploring the synergistic capabilities of cross-domain collaboration between domain experts and visualization researchers. To fill this gap, in this paper we describe the results of an exploratory study of climate data visualizations conducted in tight collaboration with a pool of climate scientists. The study analyzes a large set of static climate data visualizations for identifying their shortcomings in terms of visualization design. The outcome of the study is a classification scheme that categorizes the design problems in the form of a descriptive taxonomy. The taxonomy is a first attempt for systematically categorizing the types, causes, and consequences of design problems in visualizations created by domain experts. We demonstrate the use of the taxonomy for a number of purposes, such as, improving the existing climate data visualizations, reflecting on the impact of the problems for enabling domain experts in designing better visualizations, and also learning about the gaps and opportunities for future visualization research. We demonstrate the applicability of our taxonomy through a number of examples and discuss the lessons learnt and implications of our findings.

Abstract Inter‐comparison and similarity analysis to gauge consensus among multiple simulation models is a critical visualization problem for understanding climate change patterns. Climate models, specifically, Terrestrial Biosphere Models (TBM) represent time and space variable ecosystem processes, like, simulations of photosynthesis and respiration, using algorithms and driving variables such as climate and land use. While it is widely accepted that interactive visualization can enable scientists to better explore model similarity from different perspectives and different granularity of space and time, currently there is a lack of such visualization tools. In this paper we present three main contributions. First, we propose a domain characterization for the TBM community by systematically defining the domain‐specific intents for analyzing model similarity and characterizing the different facets of the data. Second, we define a classification scheme for combining visualization tasks and multiple facets of climate model data in one integrated framework, which can be leveraged for translating the tasks into the visualization design. Finally, we present SimilarityExplorer, an exploratory visualization tool that facilitates similarity comparison tasks across both space and time through a set of coordinated multiple views. We present two case studies from three climate scientists, who used our tool for a month for gaining scientific insights into model similarity. Their experience and results validate the effectiveness of our tool.

Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual analytics solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science.

Geospatial catalogue services have been widely used to facilitate the discovery of geospatial data. By importing geospatial feature semantics in catalogue services, it is possible to provide semantics-supported feature discovery. This paper presents an approach to develop a semantic feature catalogue service by combining the Open Geospatial Consortium (OGC) Catalogue Services for the Web (CSW) with distributed Web Feature Services (WFS) services. Feature semantics are registered in CSW by extending the ebRIM information model. The approach not only facilitates the discovery at the feature type level, but also allows feature instances to be returned through the catalogue interface.

OGC protocols have been widely accepted by the geospatial/land science communities, We call this community geospatial community. Others communities such as atmosphere, ocean, and modeling science adopt the other set of data access protocols. We name these protocols as geoscience protocols. Although these two protocols have been used wildly, but unfortunately, there is no interoperability between them. For bridging this cross-protocol and cross-community gap, CSISS have developed a catalog middleware to mediate client/server interactions between OGC catalog clients and THREDDS servers. A prototype system has been implemented to demonstrate the concept and approach.

Abstract Geospatial science is the science and art of acquiring, archiving, manipulating, analyzing, communicating, modeling with, and utilizing spatially explicit data for understanding physical, chemical, biological, and social systems on the Earth's surface or near the surface. In order to share distributed geospatial resources and facilitate the interoperability, the Open Geospatial Consortium (OGC), an industry–government–academia consortium, has developed a set of widely accepted Web‐based interoperability standards and protocols. Grid is the technology enabling resource sharing and coordinated problem solving in dynamic, multi‐institutional virtual organizations. Geospatial Grid is an extension and application of Grid technology in the geospatial discipline. This paper discusses problems associated with directly using Globus‐based Grid technology in the geospatial disciplines, the needs for geospatial Grids, and the features of geospatial Grids. Then, the paper presents a research project that develops and deploys a geospatial Grid through integrating Web‐based geospatial interoperability standards and technology developed by OGC with Globus‐based Grid technology. The geospatial Grid technology developed by this project makes the interoperable, personalized, on‐demand data access and services a reality at large geospatial data archives. Such a technology can significantly reduce problems associated with archiving, manipulating, analyzing, and utilizing large volumes of geospatial data at distributed locations. Copyright © 2008 John Wiley & Sons, Ltd.

System of Agents for Forest Observation Research with Advanced Hierarchies (SAFORAH) is a grid-enabled environment that makes optimum use of distributed data storage facilities. It supports EO data collaboration among various Canadian research organizations and provides ready access to the most current, consistent and reliable forest resource information through various network environments. SAFORAH was built with an extensible data grid network based on the Globus Toolkit and transparently presents the distributed data storage facilities across multiple organizations and government agencies as one single data archive to users to facilitate easy EO data archiving and distribution. Currently, four Canadian Forestry Centres located in Victoria British Columbia, Cornerbrook Newfoundland, Edmonton, Alberta and Laurentian Quebec, and Environment Canada’s Canadian Wildlife Service are operationally connected via Canarie to the SAFORAH data grid. The connection to a large petabyte storage facility under development at the University of Victoria (UVic) was also established. SAFORAH has been modified to provide data services based on the OGC standard using NWGISS to support the distribution of comprehensive, integrated and thematic EO data for forest applications. SAFORAH has been used in the EOSD project to support the national monitoring of Canada’s forests. This use has demonstrated that SAFORAH is a practical tool for collaborative research to facilitate the sharing of EO data. The extension of SAFORAH to handle OGC requests has enabled the distribution of landcover product tiles from multiple storage sites seamlessly to national and international users. The web address for SAFORAH is www.saforah.org.

The seamless access to large quantities of distributed, heterogeneous geospatial data/ information is still a critical issues to Earth science exploration and applications now. Using Grid computing and geospatial standards technologies, we devise an open, reusable, optimized, secured and service-oriented computing environment for semi-automatically archiving, sharing and serving geospatial data/information in the way that user required. The environment uses a Grid service- oriented architecture based on the Globus toolkits. The most important of them are the data/information retrieval mediator (DIRM) and data/information serving mediator (DISM). DIRM is based on our refined geospatial metadata for describing geospatial data/information and services and Globus replica manger service for managing replicas of massive data. DISM consists of Grid-enabled geospatial standard services for providing on-the-fly user-customable data. This environment offers an open, secure, ubiquitous and standard-compliant geospatial computing platform to provide Earth science communities with access to massive geospatial data for high-performance, data- and computing-intensive scientific research and applications.

It is now common for academic institutes, government sectors and commercial corporations all over the world to engage in collaborative, complex applications. Most of them require international and interoperable access to large quantities of distributed, heterogeneous geospatial data, services, computing abilities and other facilities. The open grid services architecture (OGSA) implemented through the globus toolkit, provides an applicable and convenient software infrastructure for securely sharing computational resources within a virtual organization. In this paper, based on the globus toolkit, we propose an open, reusable, optimized, standards-compliant, and grid-enabled geospatial software system for archiving, managing, querying and serving NASA earth observing system data. We apply grid technologies to OGC Web services to make them grid-enabled and deploy them into the system. These services can be invoked either by OGC users or by grid users in the grid environment. Any OGSA-compliant grid services can be deployed into the system, and can invoke or be invoked by our or other grid services in our virtual organization to securely access each other's data and other computational resources after we authorize access. A catalogue service federation (CSF) with an OGC standard interface is developed to interact with other grid or non-grid geospatial catalogue services for querying and serving much more geospatial data. An intelligent grid service mediator (iGSM) and replica optimization service (ROS), both of which are grid services, are proposed to distribute and optimize user requests for data and services in the system. All of the grid-enabled geospatial services can be reused to construct more complex and functional services by forming a service-chain

Catalogue service is crucial to the sharing and utilizing of geographic information. This paper provides a new architecture and its implementation of a geographic information catalogue service that can effectively manage distributed geographic data and services. The catalogue service is based on the Open Geospatial Consortium (OGC) Catalogue Service for Web Specification and the ebXML Registry Information Model (ebRIM). The ebRIM is extended with ISO 19115 (including draft part 2) and ISO 19119 international standards for better description of geographic information. The adoption of the ebRIM and international standards will greatly advance the interoperability of the catalogue service and the sharing of the abundant geographic information. The catalogue service is Gridenabled to facilitate Grid’s abilities of on-demand, ubiquitous access to distributed computing, data, and services resources. The Grid-enabled catalogue service is GSI-supported which enables it to interoperate with other Grid Services in a secure way. By being integrated with RLS, MDS and other Gridenabled OGC Services, this Grid-enabled catalogue service prototype provides secure discovery, retrieval, and management of geographic data and services.