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Award Abstract #0121182
ITR/AP: An Ensemble Approach to Data Assimilation in the Earth Sciences


NSF Org: CCF
Division of Computer and Communication Foundations
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Initial Amendment Date: September 13, 2001
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Latest Amendment Date: June 15, 2007
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Award Number: 0121182
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Award Instrument: Continuing grant
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Program Manager: Almadena Y. Chtchelkanova
CCF Division of Computer and Communication Foundations
CSE Directorate for Computer & Information Science & Engineering
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Start Date: September 15, 2001
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Expires: November 30, 2008 (Estimated)
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Awarded Amount to Date: $4369999
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Investigator(s): Dennis McLaughlin dennism@mit.edu (Principal Investigator)
Alan Willsky (Co-Principal Investigator)
Paola Malanotte-Rizzoli (Co-Principal Investigator)
Dara Entekhabi (Co-Principal Investigator)
Kerry Emanuel (Co-Principal Investigator)
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Sponsor: Massachusetts Institute of Technology
77 MASSACHUSETTS AVE
Cambridge, MA 02139 617/253-1000
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NSF Program(s): ITR MEDIUM (GROUP) GRANTS
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Field Application(s): 0000099 Other Applications NEC
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Program Reference Code(s): HPCC, 9216, 4444, 1687, 1661, 1652
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Program Element Code(s): 1687

ABSTRACT

ITR/AP: An Ensemble Approach to Data Assimilation in the Earth Sciences

New data sources are beginning to have a dramatic impact on our ability to understand the earth as an integrated system. Our prospects for dealing with the environmental issues of the 21st century -- climate change, population pressures on natural resources, and major modifications in global element cycles -- depend largely on this new information. However, our ability to process and interpret environmental data is not keeping pace with the dramatic increase in available information, especially information from airborne and orbital remote sensing platforms. If we are to realize the potential benefits of new sensing technologies we will need to develop intelligent environmental data assimilation procedures that are able to efficiently extract useful information about the earth from a diverse set of data sources.

Environmental data assimilation can be posed as a problem of estimating a large number of unobservable or highly uncertain variables (e.g. sea surface heights, atmospheric pressures, hydrologic fluxes, etc.) from a large number of related but noisy measurements (e.g. microwave radiances or backscatter detected by a satellite sensor). The estimation procedure relies on mathematical models that relate unknowns to measurements. Environmental estimation problems are challenging because the systems of interest: 1) are spatially distributed and highly variable over a wide range of space and time scales, 2) are difficult to describe with precision, 3) are often nonlinear, even chaotic, and 4) are often characterized by non-unique relationships between unknowns and measurements.

This project is concerned with very large problems (many measurements and many unknowns) which are not amenable to traditional data assimilation techniques but are of crucial interest to researchers in the earth sciences. An interdisciplinary team will develop a better understanding of the issues of dimensionality reduction and uncertainty propagation that are crucial to large-scale data assimilation. So-called ensemble methods provide a particularly informative way to identify these key features. A new generation of "intelligent" data assimilation methods will be developed that build on the understanding gained from the reduced problem. The applicability of these methods will be investigated on problems of broad interest in the earth sciences, including problems that 1) deal with coupled systems, 2) cut across traditional disciplines, and 3) work with remote sensing data sets.

This ITR project brings together acknowledged experts on environmental data assimilation. It is a group ITR project, rather than several individual projects, which cuts across earth science disciplines. The research will be coordinated with: 1) a seminar series, 2) joint supervision of Ph.D. students and post-doctoral researchers, 3) a Ph.D. mentoring program, 4) a selection of cross-cutting sample problems, and 5) co-authored publications.




PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Buehner, M., P.Malanotte-Rizzoli,A. J Busalacchi, and T. Inui. "Estimation of the tropical Atlantic circulation from altimetry data using a reduced-rank stationary Kalman filter," In "Interhemispheric water exchanges in the Atlantic Ocean", Elsevier Oceanographic Series, v.68, 2003, p. 49.

Buehner, M; Malanotte-Rizzoli, P. "Reduced-rank Kalman filters applied to an idealized model of the wind-driven ocean circulation," JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, v.108, 2003. 

Buehner, M; Malanotte-Rizzoli, P; Busalacchi, A; Inui, T. "Estimation of the tropical Atlantic circulation from altimetry data using a reduced-rank stationary Kalman filter," INTERHEMISPHERIC WATER EXCHANGE IN THE ATLANTIC OCEAN, v.68, 2003, p. 49-92. 

Caparrini, F; Castelli, F; Entekhabi, D. "Mapping of land-atmosphere heat fluxes and surface parameters with remote sensing data," BOUNDARY-LAYER METEOROLOGY, v.107, 2003, p. 605-633. 

Caparrini, F; Castelli, F; Entekhabi, D. "Estimation of surface turbulent fluxes through assimilation of radiometric surface temperature sequences," JOURNAL OF HYDROMETEOROLOGY, v.5, 2004, p. 145-159. 

Caparrini, F; Castelli, F; Entekhabi, D. "Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery," WATER RESOURCES RESEARCH, v.40, 2004. 

Choi, MJ; Chandrasekaran, V; Malioutov, DM; Johnson, JK; Willsky, AS. "Multiscale stochastic modeling for tractable inference and data assimilation," COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, v.197, 2008, p. 3492-3515. 

Choi, MJ; Chandrasekaran, V; Willsky, AS. "Maximum entropy relaxation for multiscale graphical model selection," 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, p. 1889-1892. 

Choi, MJ; Willsky, AS. "Multiscale Gaussian graphical models and algorithms for large-scale inference," 2007 IEEE/SP 14TH WORKSHOP ON STATISTICAL SIGNAL PROCESSING, VOLS 1 AND 2, 2007, p. 229-233. 

Crow, WT; Chan, S; Entekhabi, D; Hsu, A; Jackson, TJ; Njoku, E; O'Neill, P; Shi, J. "An observing system simulation experiment for hydros radiometer-only soil moisture and freeze-thaw products," IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, p. 2737-2740. 


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Last Updated:April 2, 2007