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Advances in Meteorology
Volume 2016 (2016), Article ID 4305204, 11 pages
http://dx.doi.org/10.1155/2016/4305204
Research Article

Development of the Nonstationary Incremental Analysis Update Algorithm for Sequential Data Assimilation System

1Faculty of Earth Systems and Environmental Sciences, Chonnam National University, Gwangju, Republic of Korea
2Korea Institute of Atmospheric Prediction Systems, Seoul, Republic of Korea
3Seoul National University, Seoul, Republic of Korea

Received 21 March 2016; Revised 22 August 2016; Accepted 10 October 2016

Academic Editor: Takashi Mochizuki

Copyright © 2016 Yoo-Geun Ham et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study introduces a modified version of the incremental analysis updates (IAU), called the nonstationary IAU (NIAU) method, to improve the assimilation accuracy of the IAU while keeping the continuity of the analysis. Similar to the IAU, the NIAU is designed to add analysis increments at every model time step to improve the continuity in the intermittent data assimilation. However, unlike the IAU, the NIAU procedure uses time-evolved forcing using the forward operator as corrections to the model. The solution of the NIAU is superior to that of the forward IAU, of which analysis is performed at the beginning of the time window for adding the IAU forcing, in terms of the accuracy of the analysis field. It is because, in the linear systems, the NIAU solution equals that in an intermittent data assimilation method at the end of the assimilation interval. To have the filtering property in the NIAU, a forward operator to propagate the increment is reconstructed with only dominant singular vectors. An illustration of those advantages of the NIAU is given using the simple 40-variable Lorenz model.