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

TitleExtending the square root method to account for additive forecast noise in ensemble methods
Publication TypeJournal Article
Year of Publication2015
AuthorsRaanes, P, Carrassi, A, Bertino, L
JournalMonthly Weather Review
Volume143
ISSN0027-0644
Abstract

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

DOI10.1175/MWR-D-14-00375.1
Refereed DesignationRefereed
Author Address

NERSC