Data Analysis And Data Assimilation About Summer Surface Current Over Qingdao Coastal Sea

This paper combines surface current measured by High Frequency Radar deployed in Qingdao Coastal sea, meteorologic variables recorded by buoy, sea level from tide gauge and current of mooring ADCP,try to analyze the summer surface current over Qingdao near-shore and Jiaozhou Bay area, then simulate the tidal dynamics in study area and assimilate these summer surface current into numerical model.Before the data assimilation, these surface currents are validated. We also compare the HF radar current and Buoy current, find that their averaged difference is about 10cm/s, and they are highly statistically correlated, meaning that HF radar currents are credible, and able to capture the feature of the near shore current.In order to analyze the characteristics of Qingdao near-shore current, data analysis with all available dataset are performed. The result of current analysis reveals the influence from tide, wind, topography and sea level on surface current, particularly, it demonstrates the new feature of the spatial distribution of tidal ellipse. Both the wind dataset and current dataset are helpful to know that local wind control the variability of surface residual current. Besides, the integration of current data sea level and wind, shows that two factors control the outflow and inflow near the mouth of Jiaozhou Bay:Pressure gradient induced by different seal level in and out of the Bay; Nonlinear tidal stress which varies with the spring and neap。The divergence and convergence calculated from HF radar current reveals the area where upwelling takes place frequently, the sea temperature recorded by buoy show that the local divergence(convergence) influence the diurnal variability.Secondly, Surface current data assimilation system over Qingdao Coastal sea is built basing on FVCOM, HF radar current and Ensemble Kalman Filter. If the feature of Qingdao near-shore current are taken into account, we improve the key part of our data assimilation system, including:1, Based on Ensemble Kalman Filter(ENKF) method, we adopt different assimilation method (Traditional ENKF and quasi-ENKF),then compare the improvement of forecasting error for these two method and the improvement of the spatial structure of the forecasting current. If the difference about integration efficiency are also considered, quasi-ENKF is the best choice for this data assimilation system.2,The assimilation effect are sensible to the background error estimate, therefore, we select three different methods to generate background error:Monte Carlo Method; Canadian Quick Covariance (CQC) Method and Data Uncertainty Engine(DUE) Method. The applicability of these three methods depend on their assimilation effect. Please notice that, the character of tidal current in study area are taken into consideration.By series of data assimilation experiments, we test our improvement of the system. The standard for assimilation effect is the ability to decrease forecasting error. We finally determine the best assimilation method and best background error estimate method.i.e. quasi-ENKF and CQC method. Besides, the sensibility of ensemble size is also studied in our paper, and find that the it is better to restrict ensemble size below 50.