Abstract
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.
Highlights
Ocean waves, referring to the ocean surface gravity waves driven by wind, are important physical processes in the study of multiscale coupled systems
More accurately predicting ocean waves is of great societal significance
The results show that model bias is a significant error source that has a largely adverse impact
Summary
Ocean waves, referring to the ocean surface gravity waves driven by wind, are important physical processes in the study of multiscale coupled systems. One error source is from an incomplete understanding of the physical processes, approximate expressions of the numerical discretization schemes and so on, which causes systematic errors that are usually referred to as wave model bias. The third error source is from the initial-condition uncertainties, which can grow due to the nonlinearity of the model equations during model forwarding In this sense, the modelsimulated waves do not represent the real world either. Inspired by previous work (e.g., Dee, 2005; Zhang et al, 2012), here, we use a simple data assimilation scheme with two wave models (WW3 and SWAN) to explore the influences of different error sources on WDA. The adverse impacts of wind forcing errors and initial-condition uncertainties as well as wave model bias on WDA are studied first, and two simple statistical methods for bias correction are developed to mitigate assimilation errors and improve wave analysis.
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