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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2019-243
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-243
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 07 Oct 2019

Submitted as: development and technical paper | 07 Oct 2019

Review status
A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Mitigation of Model Bias Influences on Wave Data Assimilation with Multiple Assimilation Systems Using WaveWatch III v5.16 and SWAN v41.20

Jiangyu Li1,4 and Shaoqing Zhang1,2,3,4 Jiangyu Li and Shaoqing Zhang
  • 1Key Laboratory of Physical Oceanography, MOE, China; Ocean University of China, Qingdao, 266100, China
  • 2Pilot National Laboratory for Marine Science and Technology (QNLM), Qingdao, 266100, China
  • 3International Laboratory for High-Resolution Earth System Model and Prediction (iHESP), Qingdao, 266100, China
  • 4The College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China

Abstract. High-quality wave prediction with a numerical wave model is of societal value. To initialize the wave model, wave data assimilation (WDA) is necessary to combine the model and observations. Due to imperfect numerical schemes and approximated physical processes, a wave model is always biased in relation to the real world. In this study, two assimilation systems are first developed using two nearly independent wave models; then, “perfect” and “biased” assimilation frameworks based on the two assimilation systems are designed to reveal the uncertainties of WDA. A series of “biased” assimilation experiments is conducted to systematically examine the adverse impact of model bias on WDA. A statistical approach based on the results from multiple assimilation systems is explored to carry out bias correction, by which the final wave analysis is significantly improved with the merits of individual assimilation systems. The framework with multiple assimilation systems provides an effective platform to improve wave analyses and predictions and help identify model deficits, thereby improving the model.

Jiangyu Li and Shaoqing Zhang

Jiangyu Li and Shaoqing Zhang

Data sets

data and code L. Jiangyu and S. Zhang https://doi.org/10.5281/zenodo.3445580

Model code and software

data and code L. Jiangyu and S. Zhang https://doi.org/10.5281/zenodo.3445580

Jiangyu Li and Shaoqing Zhang

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Latest update: 28 Feb 2020
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Short summary
Two assimilation systems developed using two nearly-independent wave models are used to study the influences of various error sources including mode bias on wave data assimilation; a statistical method is explored to make full use of the merits of individual assimilation systems for bias correction, thus improving wave analysis greatly. This study opens a door to further our understanding of physical processes in waves and associated air-sea interactions for improving wave modeling.
Two assimilation systems developed using two nearly-independent wave models are used to study...
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