Towards Dynamically Adaptive Weather Analysis and Forecasting in LEAD

Plale, Beth; Gannon, Dennis; Reed, D. A.; Graves, Sara; Droegemeier, Kelvin K.; Wilhelmson, B.; Ramamurthy, Mohan K.

doi:10.1007/11428848_81

Cited by 47 publications

(39 citation statements)

References 6 publications

(5 reference statements)

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“…The NSF large Information Technology Research (ITR) project Linked Environment for Sharing Atmospheric Information (LEAD, http://lead.ou.edu/; [15] [16]) is the most related work to ours since detecting weather event is also one of the functional categories in the project which is closely related to the example of tracking dynamics of geospatial phenomena used in this study. The project's proposed architecture shares many similarities with our approach regarding to serviceoriented architecture and using workflow technology.…”

Section: Related Work and Discussionmentioning

confidence: 99%

“…To the best of our knowledge, currently no visual workflow composition and execution environment is provided in LEAD portal. LEAD either adopts Business Processing Execution Language (BPEL) or Open GCE Runtime Engine (OGRE) scripting language to specify a workflow [16] [15]. Users need to either use a generic BPEL composition environment to specify a workflow or write an OGRE script in a text editor and then switch to a workflow execute environment to execute the workflow.…”

Section: Related Work and Discussionmentioning

confidence: 99%

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Tracking Dynamics of Geospatial Phenomena in Distributed and Heterogeneous Environments Using Scientific Workflow and Web Services Technologies

Zhang

2006

2006 Fifth International Conference on Grid and Cooperative Computing (GCC'06)

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We propose to use W3C Web Services and recently emerging scientific workflow technologies for geospatial data processing in distributed and heterogeneous computation environments. The Web service technology provides platform independence and greater interoperability; the scientific workflow technology provides software reuse and visual programming. We demonstrate the feasibility of the proposed approach using storm dynamics tracking from weather radar data as an example. Technical details of algorithm development, Web services publishing, workflow composition and execution for the demonstrative example are discussed.

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Section: Related Work and Discussionmentioning

confidence: 99%

Section: Related Work and Discussionmentioning

confidence: 99%

Tracking Dynamics of Geospatial Phenomena in Distributed and Heterogeneous Environments Using Scientific Workflow and Web Services Technologies

Zhang

2006

2006 Fifth International Conference on Grid and Cooperative Computing (GCC'06)

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“…Plale et al [8] use the amount of variance in an ensemble of weather forecast simulations to collect additional data and direct additional computational resources to the areas where additional simulation runs are needed. Flikkema et al [9] uses data models to filter observations at the sensors.…”

Section: Related Workmentioning

confidence: 99%

Feature Clustering for Data Steering in Dynamic Data Driven Application Systems

Pawling

Madey

2009

Lecture Notes in Computer Science

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Abstract. In this paper, we describe how feature clustering on realworld cell-phone data can be used to locate the impact area of emergency events. We first examine the effect of two emergency events on the call activity in the areas surrounding the events. We then investigate how the time series of the affected areas behave relative to the time series of their respective neighboring areas. Finally, we examine the differences in hierarchical clusterings of the time series of the affected areas and neighboring areas. 1

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“…if a component is predicted to fail), collect more data on the current state of the component; apply more detailed simulations based on the new data; 2. Uncertainty-directed: focus data collection on those areas where there is uncertainty (used in [3] and [4]). 3.…”

Section: Autonomous Agentsmentioning

confidence: 99%

Intelligent Management of Data Driven Simulations to Support Model Building in the Social Sciences

Kennedy

Theodoropoulos

2006

Computational Science – ICCS 2006

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Abstract. Artificial intelligence (AI) can contribute to the management of a data driven simulation system, in particular with regard to adaptive selection of data and refinement of the model on which the simulation is based. We consider two different classes of intelligent agent that can control a data driven simulation: (a) an autonomous agent using internal simulation to test and refine a model of its environment and (b) an assistant agent managing a data-driven simulation to help humans understand a complex system (assisted model-building). In the first case the agent is situated in its environment and can use its own sensors to explore the data sources. In the second case, the agent has much less independent access to data and may have limited capability to refine the model on which the simulation is based. This is particularly true if the data contains subjective statements about the human view of the world, such as in the social sciences.For complex systems involving human actors, we propose an architecture in which assistant agents cooperate with autonomous agents to build a more complete and reliable picture of the observed system.

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Towards Dynamically Adaptive Weather Analysis and Forecasting in LEAD

Cited by 47 publications

References 6 publications

Tracking Dynamics of Geospatial Phenomena in Distributed and Heterogeneous Environments Using Scientific Workflow and Web Services Technologies

Tracking Dynamics of Geospatial Phenomena in Distributed and Heterogeneous Environments Using Scientific Workflow and Web Services Technologies

Feature Clustering for Data Steering in Dynamic Data Driven Application Systems

Intelligent Management of Data Driven Simulations to Support Model Building in the Social Sciences

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