Interdisciplinary Data Assimilation via Error Subspace Statistical Estimation

Friday, May 3, 2002 - 9:00am - 9:45am
Keller 3-180
Pierre Lermusiaux (Harvard University)
A methodology for efficient interdisciplinary 4-d data assimilation with nonlinear models, error subspace statistical estimation (ESSE), is overviewed. ESSE is based on evolving an error subspace, of variable size, that spans and tracks the scales and processes where dominant errors occur. With this approach, the suboptimal reduction of errors is itself optimal. ESSE schemes for minimum error variance filtering and smoothing are outlined, and relationships to adaptive filters described. Presently, the error subspace is initialized by decomposition on multiple scales and evolved in time by an ensemble of stochastic model iterations. The ensemble size is controlled by convergence criteria and a posteriori data residuals are employed for adaptive learning of the dominant errors.

In addition to have been used in real-time data assimilative operations including error forecasting and adaptive sampling since 1996, ESSE has been valuable for scientific studies in several regions. Two recent investigations are discussed: the coupled biochemical-physical dynamics in Massachusetts Bay during late summer 1998 and the physical-acoustical data assimilation and prediction of uncertainties in the New England continental shelfbreak region. For the bio-physics, the use of first-order dynamical balance for the initialization of biological fields and calibration of parameters is presented. Different sub-regions of trophic enrichment and accumulation are synthesized and a few coastal processes and dynamical balances are outlined. For the physics-acoustics, the results provide insights into the relations between physical and acoustical fields, and their uncertainties.