Forecasting of Surface Currents via Correcting Wind Stress with Assimilation of High-Frequency Radar Data in a Three-Dimensional Model
This paper details work in assessing the capability of a hydrodynamic model to forecast surface currents and in applying data assimilation techniques to improve model forecasts. A three-dimensional model Environment Fluid Dynamics Code (EFDC) was forced with tidal boundary data and onshore wind data...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2016/8950378 |
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| Summary: | This paper details work in assessing the capability of a hydrodynamic model to forecast surface currents and in applying data assimilation techniques to improve model forecasts. A three-dimensional model Environment Fluid Dynamics Code (EFDC) was forced with tidal boundary data and onshore wind data, and so forth. Surface current data from a high-frequency (HF) radar system in Galway Bay were used for model intercomparisons and as a source for data assimilation. The impact of bottom roughness was also investigated. Having developed a “good” water circulation model the authors sought to improve its forecasting ability through correcting wind shear stress boundary conditions. The differences in surface velocity components between HF radar measurements and model output were calculated and used to correct surface shear stresses. Moreover, data assimilation cycle lengths were examined to extend the improvements of surface current’s patterns during forecasting period, especially for north-south velocity component. The influence of data assimilation in model forecasting was assessed using a Data Assimilation Skill Score (DASS). Positive magnitude of DASS indicated that both velocity components were considerably improved during forecasting period. Additionally, the improvements of RMSE for vector direction over domain were significant compared with the “free run.” |
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| ISSN: | 1687-9309 1687-9317 |