Probabilistic state estimation in regimes of nonlinear error growth

State estimation, or data assimilation as it is often called, is a key component of numerical weather prediction (NWP). Nearly all implementable methods of state estimation suitable for NWP are forced to assume that errors remain in regimes of linear error growth and retain distributions of Gaussian uncertainty, yet nonlinear systems like the atmosphere can readily allow regimes of nonlinear error growth and, in turn, produce distributions of non- Gaussian uncertainty. State-of-the-art, ensemble-based methods of state estimation suitable for NWP are examined to gauge the consequences and relevance of violating the linear error growth assumption. For quite generic sources of non-Gaussian uncertainty, the methods are observed to fail, as they must, and the obtained analyses become probabilistically unreliable before becoming inaccurate. The mispositioning of coherent features is identified as a specific, geophysically relevant source of non-Gaussian uncertainty that can easily cause the state-of-the-art methods of state estimation to fail. However, an understanding of relevant phenomenology sometimes allows these same methods to remain successful owing to an available redefinition of the involved errors. The redefinition is phrased as an alternative error model. It is recognized and exploited that non-Gaussian additive Eulerian errors can come from Gaussian Lagrangian position errors. A two-step, augmented state vector approach is developed that is suitable for use with coherent features and that relies only on implementable methods of state estimation.

Department dc:contributor.department

Massachusetts Institute of Technology. Technology, Dept. of Earth, Atmospheric, and Planetary Sciences.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Lawson, W. Gregory, 1975-

Advisor dc:contributor.advisor

• James A. Hansen.

• Technology, Earth, Atmospheric, and Planetary Sciences.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/30291
• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Identifier URI

http://dspace.mit.edu/handle/1721.1/30291

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/30291