Extraction of Ocean Wave Spectra From Simulated Noisy Bistatic High-Frequency Radar Data

Abstract. High-frequency parts of ocean wave spectra are strongly coupled to the local wind. Measurements of ocean wave spectra can be used to estimate sea surface winds. In this study, two deep neural networks (DNNs) were used to estimate the wind speed and direction from the first five Fourier coefficients from buoys. The DNNs were trained by wind and wave measurements from more than 100 meteorological buoys during 2014–2018. It is found that the wave measurements can best represent the wind information ~1 h ago, because the wave spectra contain wind information a short period before. The overall root-mean-square error (RMSE) of estimated wind speed is ~1.1 m/s, and the RMSE of wind direction is ~14° when wind speed is 7~25 m/s. This model can not only be used for the wind estimation for compact wave buoys but also for the quality control of wind and wave measurements from meteorological buoys.

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Evaluating High-Frequency radar data assimilation impact in coastal ocean operational modelling

10.5194/os-2021-34 ◽

2021 ◽

Author(s):

Jaime Hernandez-Lasheras ◽

Baptiste Mourre ◽

Alejandro Orfila ◽

Alex Santana ◽

Emma Reyes ◽

...

Keyword(s):

High Frequency ◽

Radar Data ◽

Separation Distance ◽

Western Mediterranean ◽

Radial Velocities ◽

Optimal Interpolation ◽

Coverage Area ◽

High Frequency Radar ◽

Western Mediterranean Sea ◽

The Impact

Abstract. The impact of the assimilation of HFR (High-Frequency Radar) observations in a high-resolution regional model is evaluated, focusing on the improvement of the mesoscale dynamics. The study area is the Ibiza Channel, located in the Western Mediterranean Sea. The resulting fields are tested against trajectories from 13 drifters. Six different assimilation experiments are compared to a control run (no assimilation). The experiments consists in assimilating (i) Sea surface temperature, sea level anomaly and Argo profiles (generic observation dataset); the generic observation dataset plus (ii) HFR total velocities and (iii) HFR radial velocities. Moreover, for each dataset two different initialization methods are assessed: a) restarting directly from the analysis after the assimilation or b) using an intermediate initialization step applying a strong nudging towards the analysis fields. The experiments assimilating generic observations plus HFR total velocities with the direct restart provides the best results, improving by 53 % the average separation distance between drifters and virtual particles after the first 48 hours of simulation in comparison to the control run. When using the nudging initialization step, the best results are found when assimilating HFR radial velocities, with a reduction of the mean separation distance by around 48 %. Results show the capability of the Ensemble Optimal Interpolation data-assimilative system to correct surface currents not only inside but also beyond the HFR coverage area. The assimilation of radial observations benefits from the smoothing effect associated with the application of the intermediate nudging step.

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