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Title: Automating climate science: large ensemble simulations on the TeraGrid with the GriPhyN virtual data system.

Abstract

Ensemble simulations are a promising technique for identifying the signal of atmospheric response to extra-tropical sea surface temperature variability with high statistical significance. The basic idea is to perform multiple simulations from slightly different initial conditions and then to study the average signal of the ensemble. A significant obstacle to performing such ensemble simulations is the bookkeeping required to prepare, execute, and track the progress of hundreds of different computations. We describe an ensemble simulation experiment in which the Fast Ocean Atmosphere Model was run on the U.S. TeraGrid. In this experiment, we used the GriPhyN Virtual Data System to manage our ensemble simulations and their execution on distributed resources, achieving dramatic (order-of-magnitude) reductions in turnaround time relative to previous manual experiments.

Authors:
; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
971131
Report Number(s):
ANL/MCS/CP-119404
TRN: US201003%%583
Resource Type:
Conference
Resource Relation:
Conference: 2nd IEEE International Conference on e-Science and Grid Computing; Dec. 4, 2006 - Dec. 6, 2006; Amsterdam, Netherlands
Country of Publication:
United States
Language:
ENGLISH
Subject:
54 ENVIRONMENTAL SCIENCES; ACCOUNTING; CLIMATES; SEAS; SIMULATION

Citation Formats

Nefedova, V., Jacob, R., Foster, I., Liu, Z., Liu, Y., Deelman, E., Mehta, G., Su, M.-H., Vahi, K., Univ. of Chicago, Univ. of Wisconsin at Madison, Computation Inst., and Univ. of Southern California. Automating climate science: large ensemble simulations on the TeraGrid with the GriPhyN virtual data system.. United States: N. p., 2006. Web. doi:10.1109/E-SCIENCE.2006.261116.
Nefedova, V., Jacob, R., Foster, I., Liu, Z., Liu, Y., Deelman, E., Mehta, G., Su, M.-H., Vahi, K., Univ. of Chicago, Univ. of Wisconsin at Madison, Computation Inst., & Univ. of Southern California. Automating climate science: large ensemble simulations on the TeraGrid with the GriPhyN virtual data system.. United States. doi:10.1109/E-SCIENCE.2006.261116.
Nefedova, V., Jacob, R., Foster, I., Liu, Z., Liu, Y., Deelman, E., Mehta, G., Su, M.-H., Vahi, K., Univ. of Chicago, Univ. of Wisconsin at Madison, Computation Inst., and Univ. of Southern California. Sun . "Automating climate science: large ensemble simulations on the TeraGrid with the GriPhyN virtual data system.". United States. doi:10.1109/E-SCIENCE.2006.261116.
@article{osti_971131,
title = {Automating climate science: large ensemble simulations on the TeraGrid with the GriPhyN virtual data system.},
author = {Nefedova, V. and Jacob, R. and Foster, I. and Liu, Z. and Liu, Y. and Deelman, E. and Mehta, G. and Su, M.-H. and Vahi, K. and Univ. of Chicago and Univ. of Wisconsin at Madison and Computation Inst. and Univ. of Southern California},
abstractNote = {Ensemble simulations are a promising technique for identifying the signal of atmospheric response to extra-tropical sea surface temperature variability with high statistical significance. The basic idea is to perform multiple simulations from slightly different initial conditions and then to study the average signal of the ensemble. A significant obstacle to performing such ensemble simulations is the bookkeeping required to prepare, execute, and track the progress of hundreds of different computations. We describe an ensemble simulation experiment in which the Fast Ocean Atmosphere Model was run on the U.S. TeraGrid. In this experiment, we used the GriPhyN Virtual Data System to manage our ensemble simulations and their execution on distributed resources, achieving dramatic (order-of-magnitude) reductions in turnaround time relative to previous manual experiments.},
doi = {10.1109/E-SCIENCE.2006.261116},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2006},
month = {Sun Jan 01 00:00:00 EST 2006}
}

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