Environmental science applications with Rapid Integrated Mapping and analysis System (RIMS)

The Rapid Integrated Mapping and analysis System (RIMS) has been developed at the University of New Hampshire as an online instrument for multidisciplinary data visualization, analysis and manipulation with a focus on hydrological applications. Recently it was enriched with data and tools to allow more sophisticated analysis of interdisciplinary data. Three different examples of specific scientific applications with RIMS are demonstrated and discussed. Analysis of historical changes in major components of the Eurasian pan-Arctic water budget is based on historical discharge data, gridded observational meteorological fields, and remote sensing data for sea ice area. Express analysis of the extremely hot and dry summer of 2010 across European Russia is performed using a combination of near-real time and historical data to evaluate the intensity and spatial distribution of this event and its socioeconomic impacts. Integrative analysis of hydrological, water management, and population data for Central Asia over the last 30 years provides an assessment of regional water security due to changes in climate, water use and demography. The presented case studies demonstrate the capabilities of RIMS as a powerful instrument for hydrological and coupled human-natural systems research

Urgent computing of storm surge for North Carolina's coast

Forecasting and prediction of natural events, such as tropical and extra-tropical cyclones, inland flooding, and severe winter weather, provide critical guidance to emergency managers and decision-makers from the local to the national level, with the goal of minimizing both human and economic losses. This guidance is used to facilitate evacuation route planning, post-disaster response and resource deployment, and critical infrastructure protection and securing, and it must be available within a time window in which decision makers can take appropriate action. This latter element is that which induces the need for urgency in this area. In this paper, we outline the North Carolina Forecasting System (NCFS) for storm surge and waves for coastal North Carolina, which is threatened by tropical cyclones about once every three years. We initially used advanced cyberinfrastructure techniques (e.g., opportunistic grid computing) in an effort to provide timely guidance for storm surge and wave impacts. However, our experience has been that a distributed computing approach is not robust enough to consistently produce the real-time results that end users expect. As a result, our technical approach has shifted so that the reliable and timely delivery of forecast products has been guaranteed by provisioning dedicated computational resources as opposed to relying on opportunistic availability of external resources. Our experiences with this forecasting effort is discussed in this paper, with a focus on Hurricane Irene (2011) that impacted a substantial portion of the US east coast from North Carolina, up along the eastern seaboard, and into New England

Urgent computing of storm surge for North Carolina's coast

Forecasting and prediction of natural events, such as tropical and extra-tropical cyclones, inland flooding, and severe winter weather, provide critical guidance to emergency managers and decision-makers from the local to the national level, with the goal of minimizing both human and economic losses. This guidance is used to facilitate evacuation route planning, post-disaster response and resource deployment, and critical infrastructure protection and securing, and it must be available within a time window in which decision makers can take appropriate action. This latter element is that which induces the need for urgency in this area. In this paper, we outline the North Carolina Forecasting System (NCFS) for storm surge and waves for coastal North Carolina, which is threatened by tropical cyclones about once every three years. We initially used advanced cyberinfrastructure techniques (e.g., opportunistic grid computing) in an effort to provide timely guidance for storm surge and wave impacts. However, our experience has been that a distributed computing approach is not robust enough to consistently produce the real-time results that end users expect. As a result, our technical approach has shifted so that the reliable and timely delivery of forecast products has been guaranteed by provisioning dedicated computational resources as opposed to relying on opportunistic availability of external resources. Our experiences with this forecasting effort is discussed in this paper, with a focus on Hurricane Irene (2011) that impacted a substantial portion of the US east coast from North Carolina, up along the eastern seaboard, and into New England

Reference model for a data grid approach to address data in a dynamic SDI

A grid is concerned with the integration, virtualization, and management of services and resources in a distributed, heterogeneous environment that supports virtual organizations across traditional administrative and organizational domains. Spatial data infrastructures (SDI) aim to make spatial data from multiple sources available and usable to as wide an audience as possible. The first SDIs of the 1990s followed a top–down approach with the focus on data production and centralization. In recent years, SDIs have seen a huge increase in the number of participants, necessitating a more dynamic bottom-up approach. While much research has been done on web services and SDIs, research on the use of data grids for SDIs is limited. In this paper an emergency response scenario is presented to illustrate how the data grid approach can be used as a decentralized platform for address data in a dynamic SDI. Next, Compartimos (Spanish for ‘we share’) is presented, a reference model for an address data grid in an SDI based on the Open Grid Services Architecture (OGSA). Compartimos identifies the essential components and their capabilities required for a decentralized address data grid in a dynamic SDI. It deviates from the current centralized approach, allows data resources to come and go and node hosts to grow and shrink as necessary. An address data grid in an SDI is both a novel application for data grids as well as a novel technology in SDI environments and thus advances the mutual understanding between data grids and SDIs. In conclusion, additional research required for address data grids in SDIs is discussed.South African Department of Trade and Industry. The original publication is available at www.springerlink.comhttp://www.springerlink.com/content/1384-6175/nf201