TY - GEN
T1 - Watershed reanalysis
T2 - 7th IEEE International Conference on e-Science Workshops, eScienceW 201
AU - Duffy, Christopher
AU - Leonard, Lorne
AU - Bhatt, Gopal
AU - Yu, Xuan
AU - Giles, Lee
PY - 2011
Y1 - 2011
N2 - Reanalysis or retrospective analysis is the process of re-analyzing and assimilating climate and weather observations with the current modeling context. Reanalysis is an objective, quantitative method of synthesizing all sources of information (historical and real-time observations) within a unified framework. In this context, we propose a prototype for automated and virtualized web services software using national data products for climate reanalysis, soils, geology, terrain and land cover for the purpose of water resource simulation, prediction, data assimilation, calibration and archival. The prototype for model-data integration focuses on creating tools for fast data storage from selected national databases, as well as the computational resources necessary for a dynamic, distributed watershed prediction anywhere in the continental US. In the future implementation of virtualized services will benefit from the development of a cloud cyber infrastructure as the prototype evolves to data and model intensive computation for continental scale water resource predictions.
AB - Reanalysis or retrospective analysis is the process of re-analyzing and assimilating climate and weather observations with the current modeling context. Reanalysis is an objective, quantitative method of synthesizing all sources of information (historical and real-time observations) within a unified framework. In this context, we propose a prototype for automated and virtualized web services software using national data products for climate reanalysis, soils, geology, terrain and land cover for the purpose of water resource simulation, prediction, data assimilation, calibration and archival. The prototype for model-data integration focuses on creating tools for fast data storage from selected national databases, as well as the computational resources necessary for a dynamic, distributed watershed prediction anywhere in the continental US. In the future implementation of virtualized services will benefit from the development of a cloud cyber infrastructure as the prototype evolves to data and model intensive computation for continental scale water resource predictions.
UR - http://www.scopus.com/inward/record.url?scp=84856976382&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84856976382&partnerID=8YFLogxK
U2 - 10.1109/eScienceW.2011.32
DO - 10.1109/eScienceW.2011.32
M3 - Conference contribution
AN - SCOPUS:84856976382
SN - 9780769545981
T3 - Proceedings - 7th IEEE International Conference on e-Science Workshops, eScienceW 2011
SP - 61
EP - 65
BT - Proceedings - 7th IEEE International Conference on e-Science Workshops, eScienceW 2011
Y2 - 5 December 2011 through 8 December 2011
ER -