An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods

Yonghan Choi; Gyu-Ho Lim; Dong-Kyou Lee; Xiang-Yu Huang

doi:10.3402/tellusa.v66.21584

An adjoint sensitivity-based data assimilation method and its comparison with existing variational methods

Tellus

Volume 66 (2014): Issue 1

By: Yonghan Choi, Gyu-Ho Lim, Dong-Kyou Lee and Xiang-Yu Huang

Open Access

|Dec 2014

Abstract

An adjoint sensitivity-based data assimilation (ASDA) method is proposed and applied to a heavy rainfall case over the Korean Peninsula. The heavy rainfall case, which occurred on 26 July 2006, caused torrential rainfall over the central part of the Korean Peninsula. The mesoscale convective system (MCS) related to the heavy rainfall was classified as training line/adjoining stratiform (TL/AS)-type for the earlier period, and back building (BB)-type for the later period. In the ASDA method, an adjoint model is run backwards with forecast-error gradient as input, and the adjoint sensitivity of the forecast error to the initial condition is scaled by an optimal scaling factor. The optimal scaling factor is determined by minimising the observational cost function of the four-dimensional variational (4D-Var) method, and the scaled sensitivity is added to the original first guess. Finally, the observations at the analysis time are assimilated using a 3D-Var method with the improved first guess. The simulated rainfall distribution is shifted northeastward compared to the observations when no radar data are assimilated or when radar data are assimilated using the 3D-Var method. The rainfall forecasts are improved when radar data are assimilated using the 4D-Var or ASDA method. Simulated atmospheric fields such as horizontal winds, temperature, and water vapour mixing ratio are also improved via the 4D-Var or ASDA method. Due to the improvement in the analysis, subsequent forecasts appropriately simulate the observed features of the TL/AS- and BB-type MCSs and the corresponding heavy rainfall. The computational cost associated with the ASDA method is significantly lower than that of the 4D-Var method.

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DOI: https://doi.org/10.3402/tellusa.v66.21584 | Journal eISSN: 3035-9554

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Language: English

Page range: 21584 - 21584

Submitted on: May 29, 2013

Accepted on: Nov 19, 2013

Published on: Dec 1, 2014

Published by: Stockholm University Press

In partnership with: Paradigm Publishing Services

Keywords:

4D-Var,

© 2014 Yonghan Choi, Gyu-Ho Lim, Dong-Kyou Lee, Xiang-Yu Huang, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 66 (2014): Issue 1