|
Award Abstract #0121263
ITR/AP+IM: Poseidon - Rapid Real-Time Interdisciplinary Ocean Forecasting: Adaptive Sampling and Adaptive Modeling in a Distributed Environment
| NSF Org: |
CNS
Division of Computer and Network Systems
|
|
|
| Initial Amendment Date: |
September 24, 2001 |
|
| Latest Amendment Date: |
September 8, 2004 |
|
| Award Number: |
0121263 |
|
| Award Instrument: |
Continuing grant |
|
| Program Manager: |
Frederica Darema
CNS Division of Computer and Network Systems
CSE Directorate for Computer & Information Science & Engineering
|
|
| Start Date: |
September 15, 2001 |
|
| Expires: |
December 31, 2004 (Estimated) |
|
| Awarded Amount to Date: |
$1430000 |
|
| Investigator(s): |
Nicholas Patrikalakis nmp@mit.edu (Principal Investigator)
Allan Robinson (Co-Principal Investigator)
James McCarthy (Co-Principal Investigator)
Henrik Schmidt (Co-Principal Investigator)
|
|
| Sponsor: |
Massachusetts Institute of Technology
77 MASSACHUSETTS AVE
Cambridge, MA 02139 617/253-1000
|
|
| NSF Program(s): |
ITR MEDIUM (GROUP) GRANTS
|
|
| Field Application(s): |
0000099 Other Applications NEC
|
|
| Program Reference Code(s): |
SMET, HPCC, 9250, 9218, 9152, 1655
|
|
| Program Element Code(s): |
1687
|
ABSTRACT
EIA-0121263
Patrikalakis, Nicholas M
MIT
ITR/AP+IM: Poseidon- Rapid Real-Time Interdisciplinary Ocean Forecasting: Adaptive Sampling and Adaptive Modeling in a Distributed Environment
Progress in understanding the complex coupled physics, biology and acoustics of the oceans is accelerating via research on realistic nonlinear multiscale interdisciplinary processes, interactions and variabilities.
To cope with the variabilities of such economies in space and time, dynamical model structures must evolve during the prediction, i.e., by adaptive modeling. The objective of this project is to enable by an effective union of information technologies and ocean sciences, efficient mulitscale interdisciplinary ocean prediction with real-time objective adaptive sampling, assimilation of multiple streams of interdisciplinary data, and autonomous adaptive modeling
Please report errors in award information by writing to: awardsearch@nsf.gov.
|
