Execution management in the GRID, for sensitivity studies of global climate simulations
Recent trends in climate modeling find in GRID computing a powerful way to achieve results by sharing geographically distributed computing and storage resources. In particular, ensemble prediction experiments are based on the generation of multiple model simulations to explore, statistically, the ex...
| Authors: | , , , , |
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| Format: | article |
| Publication Date: | 2009 |
| Country: | España |
| Institution: | Universidad de Cantabria (UC) |
| Repository: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Language: | English |
| OAI Identifier: | oai:repositorio.unican.es:10902/31246 |
| Online Access: | https://hdl.handle.net/10902/31246 |
| Access Level: | Open access |
| Keyword: | CAM model Climate models El Niño phenomenon GRID computing Workflow management |
| Summary: | Recent trends in climate modeling find in GRID computing a powerful way to achieve results by sharing geographically distributed computing and storage resources. In particular, ensemble prediction experiments are based on the generation of multiple model simulations to explore, statistically, the existing uncertainties in weather and climate forecast. In this paper, we present a GRID application consisting of a state-of-the-art climate model. The main goal of the application is to provide a tool that can be used by a climate researcher to run ensemble-based predictions on the GRID for sensitivity studies. One of the main duties of this tool is the management of a workflow involving long-term jobs and data management in a user-friendly way. In this paper we show that, due to weaknesses of current GRID middleware, this management is complex task. Those weaknesses made necessary the development of a robust workflow adapted to the requirements of the climate application. As an illustrative scientific challenge, the application is applied to study the El Niño phenomenon, by simulating an El Niño year with different forcing conditions and analyzing the precipitation response over south-American countries subject to flooding risk. |
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