Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Lei Ren,Michael Hartnett,Nanyang Chu,Zhan Hu

doi:10.3390/rs12172841

Abstract

Numerical models and remote sensing observation systems such as radars are useful for providing information on surface flows for coastal areas. Evaluation of their performance and extracting synoptic characteristics are challenging and important tasks. This research aims to investigate synoptic characteristics of surface flow fields through undertaking a detailed analysis of model results and high frequency radar (HFR) data using self-organizing map (SOM) and empirical orthogonal function (EOF) analysis. A dataset of surface flow fields over thirteen days from these two sources was used. A SOM topology map of size 4 × 3 was developed to explore spatial patterns of surface flows. Additionally, comparisons of surface flow patterns between SOM and EOF analysis were carried out. Results illustrate that both SOM and EOF analysis methods are valuable tools for extracting characteristic surface current patterns. Comparisons indicated that the SOM technique displays synoptic characteristics of surface flow fields in a more detailed way than EOF analysis. Extracted synoptic surface current patterns are useful in a variety of applications, such as oil spill treatment and search and rescue. This research provides an approach to using powerful tools to diagnose ocean processes from different aspects. Moreover, it is of great significance to assess SOM as a potential forecasting tool for coastal surface currents.

Highlights

Surface currents primarily driven by winds can flow for thousands of kilometers and can reach depths of hundreds of meters
Since coverage of high frequency radar (HFR) surface currents varied in space and time and to ensure reliable analysis in this rSeisnecaerccho,vseurrafgaeceofcuHrFreRnstsurofnalcye actuprroeinnttss vaalwriaeydsincosvpearceedabnydtthime He FanRdsytosteenmsudruerrienlgiatbhlee aannaallyyssiiss pinerthioisdrweseeraercshel,escutrefdacaencduurrseendtsinonthlye afot lploowinitnsgalawnaaylyssciosv. eHreedreb, y31t2h-ehHsuFRrfasycestveemctdour rfiinelgdtshwe aitnha1ly1s1i7s opbesrieordvawtioernepsoeilnectsteidn taontdalufoserdGianlwthaey fBoallyowwienreg uasneadlyfsoirs.bHotehreS,O3M12-ahndsuErfOaFceavneaclytosresf.ieSludrsfawciethve1c1t1o7r fiobelsdesrveaxttiroanctpedoifnrtosminmtootdaellforersGulatlswaat ythBeasyamweerpeouinsetsdwfoerreboutshedS.OBMothanodbsEeOrvFeadnaanlydsseism
The goal of the self-organizing map (SOM) technique is to partition an incoming dataset of arbitrary dimension into a two-dimensional discrete feature map and to display this transformation adaptively in a topologically oRredmeortee dSenfas.s2h0i1o9,n1.1,ExxFtOraRcPteEdERSROEMVIEpWatterns are arranged in a two-dimensional array such that s8imofila26r patterns are located nearby and dissimilar patterns are distant [71]

Summary

Introduction

Surface currents primarily driven by winds can flow for thousands of kilometers and can reach depths of hundreds of meters. Their movements carry heat and mass from place-to-place about the Earth system. Understanding, mining, and application of these surface flow field data are a new challenge for researchers [2]. Several researchers have recently undertaken investigations on measured surface flow fields. They have used surface radar data to validate model results, to improve modeling performance through data assimilation, to establish statistical forecasting models, and to characterize the physical process of surface water bodies [3,4,5,6]

Objectives

Results

Discussion

Conclusion