Observation and modeling of tide- and wind-induced surface currents in Galway Bay

Ren, Lei; Nash, Stephen; Hartnett, Michael

doi:10.1016/j.wse.2015.12.001

Cited by 35 publications

(28 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These approaches offer further interesting possibilities for understanding the three-dimensional coastal circulation (O'Donncha et al, 2014;Cianelli et al, 2015;Ren et al, 2015a;Jordà et al, 2016). It can also extend HFR applications on biological, geochemical and several environmental issues, since ecological quantities or pollutants can be located deeper in the water columns and not only follow surface dynamics.…”

Section: Applications Of Hfr Measurements In the Framework Of The Eurmentioning

confidence: 99%

HF Radar Activity in European Coastal Seas: Next Steps toward a Pan-European HF Radar Network

et al. 2017

Self Cite

View full text Add to dashboard Cite

High Frequency Radar (HFR) is a land-based remote sensing instrument offering a unique insight to coastal ocean variability, by providing synoptic, high frequency and high resolution data at the ocean atmosphere interface. HFRs have become invaluable tools in the field of operational oceanography for measuring surface currents, waves and winds, with direct applications in different sectors and an unprecedented potential for the integrated management of the coastal zone. In Europe, the number of HFR networks has been showing a significant growth over the past 10 years, with over 50 HFRs currently deployed and a number in the planning stage. There is also a growing literature concerning the use of this technology in research and operational oceanography. A big effort is made in Europe toward a coordinated development of coastal HFR technology and its products within the framework of different European and international initiatives. One recent initiative has been to make an up-to-date inventory of the existing HFR operational systems in Europe, describing the characteristics of the systems, their operational products and applications. This paper offers a comprehensive review on the present status of European HFR network, and discusses the next steps toward the integration of HFR platforms as operational components of the European Ocean Observing System, designed to align and integrate Europe's ocean observing capacity for a truly integrated end-to-end observing system for the European coasts.

show abstract

Section: Applications Of Hfr Measurements In the Framework Of The Eurmentioning

confidence: 99%

HF Radar Activity in European Coastal Seas: Next Steps toward a Pan-European HF Radar Network

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…This configuration has been found to ensure that the wind effects can be transferred from surface to deeper layers in 3D models [40]. To assess the effects of data used for wind forcing, both NUIG onshore wind data and modeled offshore HR wind data were separately used to force the model.…”

Section: Numerical Modelmentioning

confidence: 99%

The Effect of Wind Forcing on Modeling Coastal Circulation at a Marine Renewable Test Site

2017

Self Cite

View full text Add to dashboard Cite

Abstract:The hydrodynamic circulation in estuaries is primarily driven by tides, river inflows and surface winds. While tidal and river data can be quite easily obtained for input to hydrodynamic models, sourcing accurate surface wind data is problematic. Inaccurate wind data can lead to inaccuracies in the surface currents computed by three-dimensional hydrodynamic models. In this research, a high-resolution wind model was coupled with a three-dimensional hydrodynamic model of Galway Bay, a semi-enclosed estuary on the west coast of Ireland, to investigate the effect of wind forcing on model accuracy. Two wind-forcing conditions were investigated: (1) using wind data measured onshore on the NUI Galway campus (NUIG) and (2) using offshore wind data provided by a high resolution wind model (HR). A scenario with no wind forcing (NW) was also assessed. The onshore wind data varied with time but the speed and direction were applied across the full model domain. The modeled offshore wind fields varied with both time and space. The effect of wind forcing on modeled hydrodynamics was assessed via comparison of modeled surface currents with surface current measurements obtained from a High-Frequency (HF) radar Coastal Ocean Dynamics Applications Radar (CODAR) observation system. Results indicated that winds were most significant in simulating the north-south surface velocity component. The model using high resolution temporally-and spatially-varying wind data achieved better agreement with the CODAR surface currents than the model using the onshore wind measurements and the model without any wind forcing.

show abstract

“…The radar system has a high spatial resolution of 300 m and maps surface flow fields every sixty minutes [50]. Measurements of surface currents from HFR system in Galway Bay have been validated with ADCP data in detail by O'Donncha et al [29] and Ren et al [38]. Due to environmental conditions, spatial coverage of surface flow fields varies over time.…”

Section: Hfr Systemmentioning

confidence: 99%

“…The reason for including historical current data is that the variation of surface flow fields is consistent in time and space, and the development of surface flows at the present time step is significantly related to previous states. Additionally, surface currents in the Galway Bay area have significant tidal trends [37,38]. To explore and improve the forecast efficiency of ANN models, influences of the length of historical current data are investigated in details in Section 3.2.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Ren

Hartnett

2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Accurate and timely information of surface currents is crucial for various operations such as search and rescue, marine renewable energy extraction and oil spill treatment. Conventional approaches to study coastal surface currents are numerical models and observation platforms such as radars and satellites. However, both have limits. To efficiently obtain high accuracy short-term forecasting states of oceanic parameters of interest, a robust soft computing approach-Artificial Neural Networks (ANN)-was applied to predict surface currents in a tide-and wind-dominated coastal area. Hourly observed surface currents from a Coastal Ocean Dynamic Application Radar (CODAR) system, and tide and wind data from forecasting models were used to establish ANN models for Galway Bay area. One of the fastest algorithms, resilient back propagation, was used to adapt all weights and biases. This study focused on investigating the sensitivity of an ANN model to a series of different input datasets. Results indicate that correlation between ANN forecasts and observation was greater than 0.9 for both surface velocity components with one-hour lead time. Strong correlation (≥0.75) was obtained between predicted results and radar data for both surface velocity components with three-hour lead time at best. However, forecasting accuracy deteriorated rapidly with longer lead time. By comparison with previous data assimilation models, in this research, best performance was achieved from ANN model's peak times of the tidally dominant surface velocity component. The forecasts presented in this research show clear improvements over previous attempts at short-term forecasting of wind-and tide-dominated currents using ANN.

show abstract

Observation and modeling of tide- and wind-induced surface currents in Galway Bay

Cited by 35 publications

References 15 publications

HF Radar Activity in European Coastal Seas: Next Steps toward a Pan-European HF Radar Network

HF Radar Activity in European Coastal Seas: Next Steps toward a Pan-European HF Radar Network

The Effect of Wind Forcing on Modeling Coastal Circulation at a Marine Renewable Test Site

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Contact Info

Product

Resources

About