Advances in Meteorology

Volume 2016, Article ID 8950378, 12 pages

http://dx.doi.org/10.1155/2016/8950378

## Forecasting of Surface Currents via Correcting Wind Stress with Assimilation of High-Frequency Radar Data in a Three-Dimensional Model

Department of Civil Engineering & Ryan Institute, National University of Ireland, Galway, Ireland

Received 2 October 2015; Accepted 1 December 2015

Academic Editor: Hossein Tabari

Copyright © 2016 Lei Ren et al. 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.

#### Abstract

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.”

#### 1. Introduction

The interaction of air currents with the sea surface is of great importance for studying coastal surface currents. Energy transfer from wind to water contributes to generation of surface currents [1]. Wind exerts a stress on the ocean’s surface by turbulent transfer of momentum across the atmospheric boundary layer to generate ocean currents. Accurate definition of wind forcing in numerical models is obviously of great importance for developing reliable hindcast and forecast, as many model errors are derived from poor specification of boundary conditions [2]. In order to improve model forecasting ability, measured data from a HF radar system were assimilated into the model by updating the wind shear stress boundary condition.

In order to improve the forecasting ability of hydrodynamic modelling by taking advantage of available measurements such as radar surface currents and ocean currents from satellites, some researchers tried to enhance modelling performance using data assimilation techniques. Lewis et al. [3] corrected the shearing stress in model via assimilating Doppler radar current data into numerical ocean model. Minimum additional shearing stress was used to achieve a significant nudging of the model surface currents toward the basic characteristics of the observed filed of Doppler radar currents. Marmain et al. [4] assimilated HF radar surface currents in a model of the northwestern Mediterranean Sea. The wind forcing and lateral boundary-forcing components of a regional primitive equation numerical model were optimized by minimizing the difference between model-predicted and radar-derived surface currents. Barth et al. [5] assimilated HF radar surface currents in a nested model of the West Florida Shelf (WFS). They carried out ensemble simulation of the WFS model under different wind forcing in order to estimate the error covariance of the model state vector and the covariance between ocean currents and winds. Barth et al. [6] used an ensemble scheme to obtain improved surface winds by assimilating high-frequency radar surface currents in German Bight. In their research, the uncertainty of the driving wind field was represented by an ensemble of perturbed wind forcing.

Ren et al. [7] used pseudo measurements to update surface currents in a test domain with Direct Insertion data assimilation. The sensitivity tests show that the effects of Direct Insertion data assimilation strongly depend on the frequency of the data assimilation cycle. The higher the data assimilation cycle frequency, the stronger the influence of the Direct Insertion on model forecasting. In general, there are two types of data assimilation schemes in oceanography: sequential and variational data assimilation [8]. The difference between sequential and variational data assimilation schemes is as follows. The analysis equation of the former is obtained by a linear combination of measurement states and background states, such as Optimal Interpolation, nudging, and Ensemble Kalman Filter data assimilation algorithms [9–11]. The analysis equation of the latter is derived by minimizing a cost function, which is made up of two terms: one is the distance between the analysis states and the background states, and the other is the distance between the analysis states and the observation states [12–14], such as three-dimensional and four-dimensional variational data assimilation algorithms. These data assimilation schemes focus on directly correcting the surface currents by combining the measurements with model background states. Difficulties with data assimilation in coastal models are that it is not easy to obtain continuous measurements of surface currents over short time periods for sequential data assimilation and whilst wind forcing is of great influence on the generation of surface currents, it is not easy to measure high quality wind data over coastal waters.

The research presented in this paper is primarily concerned with aspects of data and techniques to enhance the forecasting capabilities of coastal hydrodynamic models. The paper briefly describes a three-dimensional hydrodynamic model of Galway Bay. The approach adopted uses surface current flow field data collected from a HF radar system to correct surface wind shear stress. Initially the radar data were compared against current measurements collected using an Acoustic Doppler Current Profile (ADCP) to benchmark the radar data.

One of the main goals of this study is to apply a data assimilation process that would be continuous for the following measurement period; this was achieved by implementing a method for correcting the model’s wind shear stress. This data assimilation method transfers the difference between model states and measurement states to correction of wind shearing stress by assimilating HF radar data. The DASS was calculated and time series of velocity components and vector maps during forecasting period are shown to assess the improvements of data assimilation in the model.

The structure of this paper is as follows: Section 2 introduces the measurements from the radar system and ADCP in Galway Bay. A three-dimensional hydrodynamic model is presented in Section 3. Assimilation of radar surface currents into model is given in Section 3, followed by results in Section 4. Conclusions of this work are listed in Section 5.

#### 2. Measurements

##### 2.1. HF Radar

A CODAR (Coastal Ocean Dynamics Application Radar) system is a type of portable, land-based HF radar system which can measure the near-surface ocean currents in a coastal area [15]. One such system has been deployed in Galway Bay on Ireland’s west coast (see Figure 1). The measurements obtained from the CODAR system are nearly real time. The rough ocean surface information is gained by the radar signal which scatters in many directions. When the radar signal scatters off a wave whose wave length is exactly equal to half of the transmitted signal wavelength [16, 17], the radar signal can return measurement information. A single HF radar station, or mast, determines the radial component of the surface currents relative to that station. Total surface currents velocities can be computed and displayed as vector maps by combining the radial surface current velocity components from two or more different masts. CODAR systems can provide rich datasets (in time and space), which can be used to explore the dynamical process of surface currents [18]. Operating frequency of the radars deployed in Galway Bay is 25 MHz. The temporal and spatial resolution is sixty minutes and 300 metres, respectively. The system is able to generate surface currents maps for the inner Galway Bay area (see Figure 1). The radar bandwidth is 500 kHz at both stations.