Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1035-2020
https://doi.org/10.5194/gmd-13-1035-2020
Development and technical paper
 | 
10 Mar 2020
Development and technical paper |  | 10 Mar 2020

Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20

Jiangyu Li and Shaoqing Zhang

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Cited articles

Abdalla, S., Bidlot, J., and Janssen, P.: Assimilation of ERS and ENVISAT wave data at ECMWF, Envisat & Ers Symposium,, p. 572, 2013. 
Almeida, S., Rusu, L., and Guedes Soares, C.: Data assimilation with the ensemble Kalman filter in a high-resolution wave forecasting model for coastal areas, J. Operational Oceanogr., 9, 103–114, 2016. 
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. 
Babanin, A. V., Ganopolski, A., and Phillips, W. R. C.: Wave-induced upper-ocean mixing in a climate model of intermediate complexity, Ocean Model., 29, 189–197, 2009. 
Bauer, E., Hasselmann, K., Young, I. R., and Hasselmann, S.: Assimilation of wave data into the wave model WAM using an impulse response function method, J. Geophys. Res., 101, 3801–3816, 1996. 
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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.