2002/03/19 by Edward Ott, Brian R. Hunt, Ott, Edward +16 · 2 citations
Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Atmospheric and Oceanic Physics (physics.ao-ph) #Chaotic Dynamics (nlin.CD) #Climate variability and models #Data Analysis #FOS: Physical sciences #Geophysics (physics.geo-ph) #Meteorological Phenomena and Simulations #Oceanographic and Atmospheric Processes #Statistics and Probability (physics.data-an) #nlin.CD #physics.ao-ph #physics.data-an #physics.geo-ph
paper · pdf · doi:10.48550/arxiv.physics/0203058
Major Revision (66 pages, 11 figures). 3 appendices included
openalex publication_date 2002/03/19 · arxiv created 2003/07/30 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we introduce a new, local formulation of the ensemble Kalman Filter approach for atmospheric data assimilation. Our scheme is based on the hypothesis that, when the Earth's surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region. Ensemble Kalman Filters, in general, assume that the analysis resulting from the data assimilation lies in the same subspace as the expected forecast error. Under our hypothesis the dimension of this subspace is low. This implies that operations only on relatively low dimensional matrices are required. Thus, the data analysis is done locally in a manner allowing massively parallel computation to be exploited. The local analyses are then used to construct global states for advancement to the next forecast time. The method, its potential advantages, properties, and implementation requirements are illustrated by numerical experiments on the Lorenz-96 model. It is found that accurate analysis can be achieved at a cost which is very modest compared to that of a full global ensemble Kalman Filter.