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Remote Sens. 2017, 9(9), 932; https://doi.org/10.3390/rs9090932

Hindcasting and Forecasting of Surface Flow Fields through Assimilating High Frequency Remotely Sensing Radar Data

1
Department of Civil Engineering, National University of Ireland Galway, H91 TK33 Galway, Ireland
2
Ryan Institute, H91 TK33 Galway, Ireland
*
Author to whom correspondence should be addressed.
Received: 4 July 2017 / Revised: 24 August 2017 / Accepted: 6 September 2017 / Published: 8 September 2017
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
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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. View Full-Text
Keywords: remote sensing; nudging; data assimilation; surface currents; CODAR; forecasting; hindcasting; Galway Bay; radars remote sensing; nudging; data assimilation; surface currents; CODAR; forecasting; hindcasting; Galway Bay; radars
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ren, L.; Hartnett, M. Hindcasting and Forecasting of Surface Flow Fields through Assimilating High Frequency Remotely Sensing Radar Data. Remote Sens. 2017, 9, 932.

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