Improved statistical prediction of surface currents based on historic HF-radar observations

Фролов, С. М.; Paduan, Jeffrey D.; Cook, Michael S.; Bellingham, James

doi:10.1007/s10236-012-0553-5

Cited by 38 publications

(39 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The techniques applied include optimal interpolation (e.g., Breivik and Saetra, 2001), variational approaches (e.g., Sperrevik et al, 2015), and empirical methods without the use of a free model run (e.g., Wahle and Stanev, 2011;Frolov et al, 2012). Extensive validation of the performance of the HF radar data assimilation is provided by Barth et al (2008) using ADCP data, Yaremchuk et al (2016) using data from drifters, and Sperrevik et al (2015) using both drifters and ADCP data.…”

Section: The Variables Of Interest For Coastal Applications Includementioning

confidence: 99%

Ocean forecasting for the German Bight: from regional to coastal scales

Stanev¹,

Schulz‐Stellenfleth²,

Staneva³

et al. 2016

Ocean Sci.

View full text Add to dashboard Cite

Abstract. This paper describes recent developments based on advances in coastal ocean forecasting in the fields of numerical modeling, data assimilation, and observational array design, exemplified by the Coastal Observing System for the North and Arctic Seas (COSYNA). The region of interest is the North and Baltic seas, and most of the coastal examples are for the German Bight. Several pre-operational applications are presented to demonstrate the outcome of using the best available science in coastal ocean predictions. The applications address the nonlinear behavior of the coastal ocean, which for the studied region is manifested by the tidal distortion and generation of shallow-water tides. Led by the motivation to maximize the benefits of the observations, this study focuses on the integration of observations and modeling using advanced statistical methods. Coastal and regional ocean forecasting systems do not operate in isolation but are linked, either weakly by using forcing data or interactively using two-way nesting or unstructured-grid models. Therefore, the problems of downscaling and upscaling are addressed, along with a discussion of the potential influence of the information from coastal observatories or coastal forecasting systems on the regional models. One example of coupling coarse-resolution regional models with a fineresolution model interface in the area of straits connecting the North and Baltic seas using a two-way nesting method is presented. Illustrations from the assimilation of remote sensing, in situ and high-frequency (HF) radar data, the prediction of wind waves and storm surges, and possible applications to search and rescue operations are also presented. Concepts for seamless approaches to link coastal and regional forecasting systems are exemplified by the application of an unstructured-grid model for the Ems Estuary.

show abstract

Section: The Variables Of Interest For Coastal Applications Includementioning

confidence: 99%

Ocean forecasting for the German Bight: from regional to coastal scales

Stanev¹,

Schulz‐Stellenfleth²,

Staneva³

et al. 2016

Ocean Sci.

View full text Add to dashboard Cite

show abstract

“…In addition to a lot of scientific work related to the study and characterization of physical ocean processes, there are several other applications of HFR in research and marine management. Some of the examples provided in Paduan and Washburn (2013) include direct applications of HFR data to search and rescue (SAR) (Ullman et al, 2006) or oil-spill mitigation (e.g., Abascal et al, 2009;Frolov et al, 2012), marine traffic information (Breivik and Saetra, 2001), water quality assessment (e.g., Kim et al, 2009) and biological oceanography (e.g., Nishimoto and Washburn, 2002;Brzezinski and Washburn, 2011).…”

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

confidence: 99%

“…Orfila et al (2015) used a Genetic Algorithm to identify mathematical expressions that best forecast the time series of statistically significant EOFs. Solabarrieta et al (2016) applied the linear autoregressive models described in Frolov et al (2012) to perform an analysis of their spatio-temporal performances in a multi-year experiment in the Southeastern Bay of Biscay. A neural network based approach to obtain short term forecasts for currents and water levels from HFR data was presented in Wahle and Stanev (2011).…”

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

confidence: 99%

“…Barrick et al (2012) used OMA decomposition (Kaplan and Lekien, 2007) and then a set of temporal modes was fitted to the time series of OMA coefficients over a short training period. Frolov et al (2012) used Empirical Orthogonal Functions (EOF) decomposition of HFR fields and a vector autoregressive model on the leading EOFs time series for prediction, incorporating wind stress forecast from a regional atmospheric model. Orfila et al (2015) used a Genetic Algorithm to identify mathematical expressions that best forecast the time series of statistically significant EOFs.…”

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

confidence: 99%

See 1 more Smart Citation

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

et al. 2017

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

“…Karimi et al [35] found that ANN and ANFIS models gave similar forecasts and outperformed auto-regression moving average models (ARMA) for all the prediction intervals. Frolov et al [36] developed a statistical model for predicting surface currents based on historical HFR observations and forecasted winds. Frolov et al [36] found that the minimal length of the HF-radar data required to train an accurate statistical model was between one and two years, depending on the accuracy desired.…”

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

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

Improved statistical prediction of surface currents based on historic HF-radar observations

Cited by 38 publications

References 14 publications

Ocean forecasting for the German Bight: from regional to coastal scales

Ocean forecasting for the German Bight: from regional to coastal scales

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

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

Contact Info

Product

Resources

About