Extending the square root method to account for additive forecast noise in ensemble methods

Raanes, P. N., Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600 and Bertino, L. (2015) Extending the square root method to account for additive forecast noise in ensemble methods. Monthly Weather Review, 143 (10). pp. 3857-3873. ISSN 0027-0644 doi: 10.1175/MWR-D-14-00375.1

Abstract/Summary

A square root approach is considered for the problem of accounting for model noise in the forecast step of the ensemble Kalman filter (EnKF) and related algorithms. The primary aim is to replace the method of simulated, pseudo-random additive so as to eliminate the associated sampling errors. The core method is based on the analysis step of ensemble square root filters, and consists in the deterministic computation of a transform matrix. The theoretical advantages regarding dynamical consistency are surveyed, applying equally well to the square root method in the analysis step. A fundamental problem due to the limited size of the ensemble subspace is discussed, and novel solutions that complement the core method are suggested and studied. Benchmarks from twin experiments with simple, low-order dynamics indicate improved performance over standard approaches such as additive, simulated noise, and multiplicative inflation.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/90170
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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