Linked Environments for Atmospheric Discovery (LEAD) is a large-scale cyberinfrastructure effort in support of mesoscale meteorology. One of the primary goals of the infrastructure is support for real-time dynamic, adaptive response to severe weather. In this paper we revisit the conception of dynamic adaptivity as appeared in our 2005 DDDAS workshop paper, and discuss changes since the original conceptualization, and lessons learned in working with a complex service oriented architecture in support of data driven science.
%0 Conference Paper
%1 Ramakrishnan:iccs:2007
%A Ramakrishnan, Lavanya
%A Simmhan, Yogesh
%A Plale, Beth
%B International Conference on Computational Science (ICCS)
%D 2007
%E Shi, Yong
%E van Albada, Geert
%E Dongarra, Jack
%E Sloot, Peter
%I Springer Berlin / Heidelberg
%K LEAD, escience, iu, peer reviewed workflow,
%P 1122--1129
%R 10.1007/978-3-540-72584-8_147
%T Realization of Dynamically Adaptive Weather Analysis and Forecasting in LEAD: Four Years Down the Road
%V 4487
%X Linked Environments for Atmospheric Discovery (LEAD) is a large-scale cyberinfrastructure effort in support of mesoscale meteorology. One of the primary goals of the infrastructure is support for real-time dynamic, adaptive response to severe weather. In this paper we revisit the conception of dynamic adaptivity as appeared in our 2005 DDDAS workshop paper, and discuss changes since the original conceptualization, and lessons learned in working with a complex service oriented architecture in support of data driven science.
@inproceedings{Ramakrishnan:iccs:2007,
abstract = {Linked Environments for Atmospheric Discovery (LEAD) is a large-scale cyberinfrastructure effort in support of mesoscale meteorology. One of the primary goals of the infrastructure is support for real-time dynamic, adaptive response to severe weather. In this paper we revisit the conception of dynamic adaptivity as appeared in our 2005 DDDAS workshop paper, and discuss changes since the original conceptualization, and lessons learned in working with a complex service oriented architecture in support of data driven science.},
added-at = {2023-04-07T07:37:58.000+0200},
author = {Ramakrishnan, Lavanya and Simmhan, Yogesh and Plale, Beth},
biburl = {https://www.bibsonomy.org/bibtex/23fdec500edf7f5c3d88cbe2261230974/vinayaka2000},
booktitle = {International Conference on Computational Science (ICCS)},
doi = {10.1007/978-3-540-72584-8_147},
editor = {Shi, Yong and van Albada, Geert and Dongarra, Jack and Sloot, Peter},
interhash = {f1ff0296e96775398fb553065564f9e5},
intrahash = {3fdec500edf7f5c3d88cbe2261230974},
keywords = {LEAD, escience, iu, peer reviewed workflow,},
note = {[CORE A]},
owner = {Simmhan},
pages = {1122--1129},
publisher = {Springer Berlin / Heidelberg},
series = {LNCS},
timestamp = {2023-04-07T07:37:58.000+0200},
title = {Realization of Dynamically Adaptive Weather Analysis and Forecasting in LEAD: Four Years Down the Road},
volume = 4487,
year = 2007
}