Abstract
In order to improve the forecasting ability of numerical models, a sequential data assimilation scheme, nudging, was applied to blend remotely sensing high-frequency (HF) radar surface currents with results from a three-dimensional numerical, EFDC (Environmental Fluid Dynamics Code) model. For the first time, this research presents the most appropriate nudging parameters, which were determined from sensitivity experiments. To examine the influence of data assimilation cycle lengths on forecasts and to extend forecasting improvements, the duration of data assimilation cycles was studied through assimilating linearly interpolated temporal radar data. Data assimilation nudging parameters have not been previously analyzed. Assimilation of HF radar measurements at each model computational timestep outperformed those assimilation models using longer data assimilation cycle lengths; root-mean-square error (RMSE) values of both surface velocity components during a 12 h model forecasting period indicated that surface flow fields were significantly improved when implementing nudging assimilation at each model computational timestep. The Data Assimilation Skill Score (DASS) technique was used to quantitatively evaluate forecast improvements. The averaged values of DASS over the data assimilation domain were 26% and 33% for east–west and north–south velocity components, respectively, over the half-day forecasting period. Correlation of Averaged Kinetic Energy (AKE) was improved by more than 10% in the best data assimilation model. Time series of velocity components and surface flow fields were presented to illustrate the improvement resulting from data assimilation application over time.
Highlights
Accurate forecasting of surface currents in coastal areas is of great importance for operations such as search and rescue, fishing, and pollution monitoring
In order to develop a good data assimilation forecasting model for the Galway Bay domain using the HF radar data, the procedure can be described as a twofold process: firstly, appropriate using the HF radar data, the procedure can be described as a twofold process: firstly, appropriate nudging parameters were examined, and nudging parameters were examined, and appropriate values defined based on root-mean-square error (RMSE) values between surface currents and HF radar data appropriate values defined based on RMSE values between surface currents and HF radar data during during the hindcasting period
The results showed that improved surface current forecasting resulted from the assimilation model using shorter updating intervals
Summary
Accurate forecasting of surface currents in coastal areas is of great importance for operations such as search and rescue, fishing, and pollution monitoring. Ren et al [13] used a similar data assimilation approach to correct the wind stress in a three-dimensional model for Galway Bay using the HF radar surface currents Results indicated that both velocity components were considerably improved during the forecasting period. HF remotely sensing radar system which measures the near-surface ocean currents in a coastal area with fine temporal and spatial resolutions One such system, consisting of two radar masts, of was deployed on Galway Bay (see Figure 1) in 2011. CODAR the unfiltered total vector withsensing larger radar which measures the to near-surface ocean currents in a coastal area with fine nudging temporal data and spatialsystem coverage were employed build a data assimilation forecasting system using spatial resolutions.
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