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Information‐based data selection for ensemble data assimilation
Author(s) -
Migliorini S.
Publication year - 2013
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.2104
Subject(s) - data assimilation , computer science , computation , subspace topology , numerical weather prediction , algorithm , ensemble learning , selection (genetic algorithm) , data mining , machine learning , artificial intelligence , meteorology , physics
Abstract Ensemble‐based data assimilation is rapidly proving itself as a computationally efficient and skilful assimilation method for numerical weather prediction, which can provide a viable alternative to more established variational assimilation techniques. However, a fundamental shortcoming of ensemble techniques is that the resulting analysis increments can only span a limited subspace of the state space, whose dimension is less than the ensemble size. This limits the amount of observational information that can effectively constrain the analysis. In this paper, a data selection strategy that aims to assimilate only the observational components that matter most and that can be used with both stochastic and deterministic ensemble filters is presented. This avoids unnecessary computations, reduces round‐off errors and minimizes the risk of importing observation bias in the analysis. When an ensemble‐based assimilation technique is used to assimilate high‐density observations, the data selection procedure allows the use of larger localization domains that may lead to a more balanced analysis. Results from the use of this data selection technique with a two‐dimensional linear and a nonlinear advection model using both in situ and remote sounding observations are discussed.