depends on four factors: the frequency with which currents occur, the magnitudes of their speed, the depth at which they occur, and their distance from the shore. In situ near-surface current data from SVP drifters and AVISO satellite altimeters are used. Our results indicate that Japan, Taiwan, Vietnam, the Philippines, Malaysia, Papua New Guinea, and Australia have promising potential for the development of ocean current power generation.
One factor we have ignored in the estimation of maximum available power is the backwater effect, including turbine and transmission efficiencies, losses from sup-porting structures, and wake interference. This is a practical issue of concern for ocean current power engineering design and has received attention recently (Garrett and Cummins2007,2008; Yang et al.2013). Numerical modeling results from the recent studies indicate that the maximum extractable energy strongly depends on the turbine hub height in the water column, and there is a limit to the available power because too many turbines will merely block the flow. Further investigation is suggested to be pursued along this line.
Further analysis is also required relative to Eq. (2) for the index calculation.“P”
and“U” in this equation are two interdependent parameters; a higher value of P will result in a higher value of U. Thus, more justification is needed to choose a best value. Some sort of sensitivity test relative to the values of the weights wiwill be performed in the future.
Fig. 10 Distribution of index I in East Asia from drifter-derived mean surface current.
Reproduced from Chang et al. (2015)
Use of Global Satellite Altimeter and Drifter Data… 173
Finally, other factors are potentially important and might need to be included in the index determination—factors such as sea state condition (rough vs. calm, this will have a huge impact on the engineering design and installation and maintenance cost), marine environment consideration (whether the sea area is a protected natural reserve), and socioeconomic factors (levelized cost of energy, which also depends on environmental concerns, permitting challenges, and comparisons between the cost of energy for non-renewable sources vs. renewable sources of power and timeline under consideration). These are some important tasks to be completed in the future.
Acknowledgements This research was completed with grants from the Ministry of Science and Technology of Taiwan, the Republic of China (MOST 104-2611-M-10-008). Peter C. Chu was supported by the Naval Oceanographic Office. We are grateful for the comments of anonymous reviewers. We thank Luca Centurioni of Scripps Institution of Oceanography for providing drifterdata.
Fig. 11 Selected sites in conditions of a L < 100 km, D < 2000 m, P > 30%, and U > 0.7 ms−1; b L < 50 km, D < 2000 m, P > 30%, and U > 0.7 ms−1; c L < 50 km, D > 1000 m, P > 30%, and U > 0.7 ms−1; and d L < 50 km, D < 1000 m, P > 50%, and U > 1.0 ms−1. Reproduced from Chang et al. (2015)
174 R.-S. Tseng et al.
References
Bahaj, A. S. (2011). Generating electricity from the oceans. Renewable and Sustainable Energy Reviews, 15, 3399–3416.
Bryden, H., Beal, L. M., & Duncan, L. M. (2005). Structure and Transport of the Agulhas current and its temporal variability. Journal of Oceanography, 61, 479–492.
Centurioni, L. R., Niller, P. P., & Lee, D.-K. (2004). Observations of inflow of Philippine sea surface water into the South China Sea through the Luzon Strait. Journal of Physical Oceanography, 34, 113–121.
Chang, Y.-C., Tseng, R.-S., & Centurioni, L. R. (2010). Typhoon-induced strong surface flows in the Taiwan Strait and Pacific. Journal of Oceanography, 66, 175–182.
Chang, Y.-C., Chen, G.-Y., Tseng, R.-S., Centurioni, L. R., & Chu, P. C. (2012). Observed near-surface currents under high wind speeds. Journal Geophysical Research, 117, C11026.
doi:10.1029/2012JC007996.
Chang, Y.-C., Tseng, R.-S., Chen, G.-Y., Chu, P. C., & Shen, Y.-T. (2013). Ship routing utilizing strong ocean currents. Journal of Navigation, 66, 825–835.
Chang, Y.-C., Chu, P. C., Tseng, R.-S., & Centurioni, L. R. (2014). Observed near-surface currents under four super typhoons. Journal of Marine Systems, 139, 311–319.
Chang, Y.-C., Tseng, R.-S., & Chu, P. C. (2015). Site selection of ocean current power generation from drifter measurements. Renewable Energy, 80, 737–745.
Chen, F. (2010). Kuroshio power plant development plan. Renewable and Sustainable Energy Reviews, 14, 2655–2668.
Chu, P. C. (2009). Statistical characteristics of the global surface current speeds obtained from satellite altimeter and scatterometer data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1), 27–32.
Davidson, F. J. M., Allen, A., Brassington, G. B., Breivik, Ø, Daniel, P., Kamachi, M., et al.
(2009). Applications of GODAE ocean current forecasts to search and rescue and ship routing.
Oceanography, 22 (3), 176–181.
Ducet, N., Le Traon, P.-Y., & Reverdin, G. (2000). Global high resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and -2. Journal Geophysical Research, 105, 19477–19498.
Duerr, A. E. S., & Dhanak, M. R. (2012). An assessment of the hydrokinetic energy resource of the Florida Current. IEEE Journal of Oceanic Engineering, 37(2), 281–293.
Edenhofer, O., & Kalkuhl, M. (2011). When do increasing carbon taxes accelerate global warming? A note on the green paradox. Energy Policy, 39, 2208–2212.
Finkl, C. W., & Charlier, R. (2009). Electrical power generation from ocean currents in the Straits of Florida: Some environmental considerations. Renewable and Sustainable Energy Reviews, 13, 2597–2604.
Garrett, C., & Cummins, P. (2007). The efficiency of a turbine in a tidal channel. Journal of Fluid Mechanics, 588, 243–251.
Garrett, C., & Cummins, P. (2008). Limits to tidal current power. Renewable Energy, 33, 2485– 2490.
Hanson, H. P., Skemp, S. H., Alsenas, G. M., & Coley, C. E. (2010). Power from the Florida Current a new perspective on an old vision. Bulletin of the American Meteorological Society, 91, 861–866.
Hsin, Y.-C., Qiu, B., Chiang, T.-L., & Wu, C.-R. (2013). Seasonal to interannual variations in the intensity and central position of the surface Kuroshio east of Taiwan. Journal Geophysical Research, 118, 4305–4316.
Johns, W. E., Lee, T. N., Zhang, D., Zantopp, R., Liu, C.-T., & Yang, Y. (2001). The Kuroshio east of Taiwan: Moored transport observations from WOCE PCM-1 array. Journal of Physical Oceanography, 31, 1031–1053.
Kashino, Y., Espana, N., Syamsudin, F., Richards, K. J., Jensen, T., Dutrieux, P., et al. (2009).
Observations of the North Equatorial Current, Mindanao Current, and the Kuroshio Current
Use of Global Satellite Altimeter and Drifter Data… 175
system during the 2006/07 El Nino and 2007/08 La Nina. Journal of Oceanography, 65, 325– 333.
Kawabe, M. (2005). Variations of the Kuroshio in the southern region of Japan: Conditions for large meander of the Kuroshio. Journal of Oceanography, 61, 529–537.
Le Traon, P.-Y., Dibarboure, G., & Ducet, N. (2001). Use of a high-resolution model to analyze the mapping capabilities of multiple-altimeter missions. Journal of Atmospheric and Oceanic Technology, 18, 1277–1288.
Lissaman, P. B. S. (1979). The Coriolis program. Oceanus, 22, 23–28.
Lukas, R., Firing, E., Hacker, P., Richardson, P. L., Collins, C. A., Fine, R., et al. (1991).
Observations of the Mindanao current during the western equatorial Pacific Ocean circulation study. Journal Geophysical Research, 96(C4), 7089–7104.
Lumpkin, R., & Johnson, G. C. (2013). Global ocean surface velocities from drifters: Mean, variance, El Niño-Southern Oscillation response, and seasonal cycle. Journal of Geophysical Research: Oceans, 118(6), 2992–3006.
Lutjeharms, J. R. E. (2006). The Agulhas Current (1st ed.). Berlin: Springer.
Maximenko, N., Niiler, P., Centurioni, L., Rio, M.-H., Melnichenko, O., Chambers, D., et al.
(2009). Mean dynamic topography of the ocean derived from satellite and drifting buoy data using three different techniques. Journal of Atmospheric and Oceanic Technology, 26(9), 1910–1919.
Munk, W. H. (1950). On the wind-driven ocean circulation. Journal of Meteorology, 7, 79–93.
Niiler, P. P. (2001). The world ocean surface circulation. International Geophysics, 77, 193–204.
In G. Siedler, J. Church & J. Gould (Eds.), Ocean circulation and climate: Observing and modeling the global ocean. San Diego, Calif: Academic Press.
Niiler, P. P., Sybrandy, A. S., Bi, K., Poulain, P. M., & Bitterman, D. (1995). Measurements of the water following capability of holey-sock and TRISTAR drifters. Deep-Sea Research, 42A, 1951–1964.
Pascual, A., Faugere, Y., Larnicol, G., & Le Traon, P.-Y. (2006). Improved description of the ocean mesoscale variability by combining four satellite altimeters. Geophysical Research Letters, 33, L02611. doi:10.1029/2005GL024633.
Ponta, F. L., & Jacovkis, P. M. (2008). Marine-current power generation by diffuser-augmented floating hydro-turbines. Renewable Energy, 33(4), 665–673.
Poulain, P.-M., Gerin, R., Mauri, E., & Pennel, R. (2009). Wind effects on drogued and undrogued drifters in the eastern Mediterranean. Journal of Atmospheric and Oceanic Technology, 26, 1144–1156. doi:10.1175/2008JTECHO618.1.
Qiu, B., & Chen, S. (2006). Decadal variability in the large-scale sea surface heightfield of the South Pacific Ocean: Observations and causes. Journal of Physical Oceanography, 36(9), 1751. doi:10.1175/JPO2943.1.
Rio, M.-H., & Hernandez, F. (2004). A mean dynamic topography computed over the world ocean from altimetry, in situ measurements, and a geoid model. Journal Geophysical Research, 109, C12032. doi:10.1029/2003JC002226.
Stommel, H. (1948). The westward intensification of wind-driven ocean currents. Transactions American Geophysical Union, 29, 202–206.
Stramma, L., & Lutjeharms, J. (1997). The flowfield of the subtropical gyre of the South Indian Ocean. Journal Geophysical Research, 99, 14053–14070.
Twidell, J., & Weir, A. (2006). Renewable energy resources. Taylor and Francis.
VanZwieten Jr., J. H., Duerr, A. E. S., Alsenas, G. M., & Hanson, H. P. (2013). Global ocean current energy assessment: An initial look. In Proceedings of the 1st Marine Energy Technology Symposium (METS13), April 10–11, Washington D.C.
VanZwieten, J. H., Meyer, I., & Alsenas, G. M. (2014). Evaluation of HYCOM as a tool for ocean current energy assessment. In Proceedings of the 2nd Marine Energy Technology Symposium (METS14) April 15–18, 2014, Seattle, WA.
Yang, X., Haas, K. A., & Fritz, H. M. (2014). Evaluating the potential for energy extraction from turbines in the gulf stream system. Renewable Energy, 12–21.
176 R.-S. Tseng et al.
Yang, X., Haas, K. A., Fritz, H. M., French, S. P., Shi, X., Neary, V. S., et al. (2015a). National geodatabase of ocean current power resource in USA. Renewable and Sustainable Energy Reviews, 44, 496–507.
Yang, Y. J., Jan, S., Chang, M.-H., Wang, J., Mensah, V., Kuo, T.-H., et al. (2015b). Mean structure and fluctuations of the Kuroshio east of Taiwan from in-situ and remote observations.
Oceanography, 28(4), 74–83.
Yang, Z., Wang, T., & Copping, A. E. (2013). Modeling tidal stream energy extraction and its effects on transport processes in a tidal channel and bay system using a three-dimensional coastal ocean model. Renewable Energy, 50, 605–613.
Use of Global Satellite Altimeter and Drifter Data… 177
Mapping the Ocean Current Strength and Persistence in the Agulhas to Inform Marine Energy Development
I. Meyer, L. Braby, M. Krug and B. Backeberg
Introduction
Renewable energy technology has undergone tremendous development over the last three decades and has found great commercial success in the onshore and offshore wind, solar, and biomass spheres. Of the renewable energy technologies, ocean energy technology is the least developed, and due to the vastness of the resource, many facets are yet to be fully understood. Energy in the world’s oceans is found in either kinetic (i.e. waves, tides, or currents) or potential (i.e. thermal or salinity gradients) forms, and all forms are being investigated to generate useful electric power.
I. Meyer (✉)
Centre for Renewable and Sustainable Energy Studies, Stellenbosch University, Stellenbosch, South Africa e-mail: imke.meyer90@gmail.com; imke@sun.ac.za M. Krug
Earth Observations, Natural Resources and the Environment, CSIR, Stellenbosch, South Africa
e-mail: mkrug@csir.co.za B. Backeberg
Coastal Systems, Natural Resources and the Environment,
Council for Scientific and Industrial Research, Stellenbosch, South Africa e-mail: bbackeberg@csir.co.za
L. Braby ⋅ M. Krug ⋅ B. Backeberg
Nansen-Tutu Centre for Marine Environmental Research, Department of Oceanography, University of Cape Town, Cape Town, South Africa
e-mail: laurabraby@gmail.com B. Backeberg
Nansen Environmental and Remote Sensing Center, Bergen, Norway
© Springer International Publishing AG 2017 179
The focus of this study is ocean current energy, the kinetic energy available in large-scale open-ocean geostrophic surface currents, and specifically the Agulhas Current. Western boundary ocean currents have become an area of focus (Duerr and Dhanak2012; Chang et al.2015), and the Agulhas Current is of specific interest in the Southern Hemisphere (Meyer et al.2014; VanZwieten et al.2014,2015). Each ocean current has its own features but most western boundary currents have similar characteristics. Western boundary currents are narrow, intense,flow poleward, and are driven by the zonally integrated wind stress curl of the adjacent basins (Lut-jeharms2006).
Western boundary currents generally exhibit their strongest flow near the ocean’s surface. In recent years, interest in these currents has evolved closer to commercial development, so the physical characteristics of the currents and their possible impacts on power generation need to be identified and fully understood.
Ocean current resource characterisation studies have been performed for the Gulf Stream in the United States (Duerr and Dhanak 2012; Haas et al.2013) and the Kuroshio Current near Japan and Taiwan (Chen 2010). Studies of the Agulhas Current on the East Coast of South Africa (e.g. Lutjeharms2006; Beal and Bryden 1999; Bryden et al. 2005) have focused predominantly on understanding open-ocean oceanographic and climate-related processes. Few studies focus on characterising the Agulhas Current for ocean energy extraction technologies; in particular, the ocean current dynamics near the continental shelf region where technology deployment is possible are poorly understood.
Western boundary currents have the potential to be more reliable sources of energy than erratic winds because of their inherent reliability, persistence, and strength. Further, water is approximately 1,000 times denser than air resulting in high energy density in the oceans. Recent investigations by Haas et al. (2013) have shown that the Gulf Stream could potentially have an average power dissipation of 18.6 GW or 163 TWh/yr (serving the electricity needs of approximately 16 million households). According to the Ocean Energy Council,“Ocean currents are one of the largest untapped renewable energy resource on the planet. Preliminary surveys show a global potential of over 450,000 MW, representing a market of more than US$550 billion” (Renewable Energy Caribbean 2014).
The Agulhas Currentflows southward along South Africa’s East Coast, as a fast and narrow stream, and transports on average 70 million cubic metres of water per second (Bryden et al.2005). Studies of the northern extent (north of 35ºS) of the current have shown that its course closely follows the narrow continental shelf (Gründlingh 1983), meandering less than 15 km from its mean path, and that the core of the current lies within 31 km from the coast almost 80% of the time (Bryden et al. 2005). The intensity of the current, its close proximity to the coast, and its relative stability make the Agulhas Current one of the more attractive ocean cur-rents in the world to exploit for energy extraction.
However, the stable trajectory of the current is intermittently interrupted by per-turbations known as Natal Pulses—large solitary meanders that form at the Natal Bight, a region between 29 and 30°S, and propagate downstream in the Agulhas Current at ±10 km/day (Lutjeharms and Roberts1988). Fluctuations in the Agulhas
180 I. Meyer et al.
Current path associated with these meanders do not display the same frequency characteristics at all latitudes (Rouault and Penven2011), because of the dissipation mechanisms of the Natal Pulses as they propagate downstream. Variability in the current and its velocities occurs across of a range of temporal and spatial scales (Lutjeharms2006), and understanding, monitoring, and predicting these are vital for the effective use of the Agulhas Current as a renewable energy resource.
One of the most effective ways to monitor ocean currents over large spatial areas at a relatively high temporal frequency is through the use of satellite measurements.
While ocean currents cannot be directly measured from space at present (Dohan and Maximenko 2010), surface current information can be derived from a range of remotely sensed observations to study and monitor the ocean circulation. At the larger scales (tens of kilometres), geostrophic currents, which occur as a result of pressure and Coriolis forcing, often drive most of the circulation. Wind stress at the ocean’s surface also drives transport that can be estimated using the Ekman theory (Ekman1905). Satellite observations of ocean surface winds and sea surface height (SSH) have therefore widely been used over the last two decades to study and monitor ocean circulation (Robinson 2004). Other remote-sensing observations such as Sea Surface Temperature (SST) and sea surface roughness can also be used routinely and systematically to derive ocean current information. The Agulhas Current is associated with strong signatures in SSH, SST, and sea surface rough-ness, all of which have been exploited successfully to study the variability of the Agulhas Current as demonstrated by Rouault et al. (2010), Rouault and Penven (2011), and Krug and Tournadre (2012). When used in synergy with the global network of in situ surface drifters, satellites can provide improved global obser-vations of the sea surface velocity.
However, satellite measurements are limited to the surface and for the purpose of marine energy extraction, it is important to have information about the vertical structure of the water column. Measurements of the vertical structure of the ocean are even sparser. To deal with the spatially and temporally incoherent observations of the oceans, we use numerical models combined with observations through a process called data assimilation. Realistic simulations of the Agulhas system are complicated by the highly nonlinear nature of the mesoscale variability governing the Agulhas Current (Biastoch et al.2008). Even if a model is capable of representing the mean circulation and variability of the region, inaccuracies in the initial state estimate inhibit the forecast skill of the model up to the decadal time scale (Meehl et al.2009).
Data assimilation provides the means to estimate a physically consistent three-dimensional (3D) estimate of the ocean state, combining a dynamical forecast model and observations together with their relative errors. Due to inaccurate numerics and boundary conditions, model solutions are imperfect. By repeatedly assimilating data, models may be constrained to provide a more realistic estimate of the ocean state. Such data-assimilative models of the ocean play a vital role in predicting ocean currents as well as in understanding the 3D structure and its variability.
By combining state-of-the-art satellite remote-sensing observations with data-assimilative (predictive) ocean models, this study aims to identify areas of energeticflow along South Africa’s East Coast for the purpose of marine energy
Mapping the Ocean Current Strength and Persistence… 181
extraction and to examine the associated current characteristics. The ability and usefulness of the satellite remote-sensing observations and predictive models to monitor and predict current velocities and their variability will be assessed by comparing them with in situ velocity measurements from Acoustic Doppler Current Profilers (ADCPs) for the period from 2009 to 2010. In doing so, the impact of the current behaviour on the potential power production will be quantified, and the present day state-of-the-art tools used to accurately monitor and predictfluctuations in the Agulhas Current that affect power production will be critically examined.
The focus area for the analysis lies between the latitudes of 31 and 34°S as indicated in Fig.1. The coastal proximity and strength of the Agulhas Current in the southeast Agulhas Current region make it the most suitable region for energy exploitation. Farther south, the Agulhas Current flows too far from the coast to allow for efficient energy recovery. Farther north, the current strength is decreased.
This chapter examines the Agulhas Current characteristics and attempts to quantify how its behaviour will affect potential power production. In the following section, the available data and data types are described, followed by an investi-gation of current strength and variability, the usefulness of the various data sets, and the implications for possible energy production. The technical, environmental, and social impacts of harnessing energy from the Agulhas Current are also considered.
Data and Methods
The data sets described in the following sections are used to determine the physical characteristics of the Agulhas Current. The sections also address the relative use-fulness of each data set towards reducing the barriers of entry into the ocean current energy market.
Fig. 1 Position of ADCP deployments with 100, 200, and 500 m isobaths
182 I. Meyer et al.
GlobCurrent Data Set
In this study, we use the combined 15-m-depth GlobCurrent Version 2 product, which is available from the GlobCurrent project (http://www.globcurrent.org/). This data set consists of 13 years of global gridded ocean currentfields and is provided at a 0.12° spatial resolution and 3-hour time interval. The combined current in the GlobCurrent data set is computed as the sum of the geostrophic and Ekman components of the flow. In the GlobCurrent product, geostrophic currents are derived from satellite observations of SSH from multiple altimeters, while the Ekman currents (driven by local wind forcing) are estimated using Lagrangian ocean current information collected from surface drifters and Argofloats. A detailed description of the method used to derive the GlobCurrent geostrophic and Ekman ocean currents is provided by Rio et al. (2014).
Confirming the validity of using satellite data to monitor the behaviour of the Agulhas Current is crucial to reducing the costs of monitoring the operations of a potential ocean current plant as well as monitoring upstream events that can affect the potential power output of a plant.
Global Hybrid Coordinate Ocean Model
3D ocean forecast data from a global Hybrid Coordinate Ocean Model (HYCOM) are used in this study. These data are freely available from the HYCOM consortium (hycom.org), a multi-institutional effort sponsored by the National Ocean Partner-ship Program, as part of the U.S. Global Ocean Data Assimilation Experiment, to develop and evaluate a data-assimilative hybrid isopycnal-sigma-pressure (gener-alised) coordinate ocean model.
The numerical model is configured for the global ocean, and computations are carried out on a Mercator grid between 78°S and 47°N at 1/12° (± 7 km) resolu-tion. There are 32 vertical layers, and the model’s bathymetry is derived from a quality-controlled Naval Research Laboratory Digital Bathymetry Data Base 2-minute resolution data set. Surface forcing data are from the Navy Operational Global Atmospheric Prediction System and include wind stress, wind speed, heat flux (using bulk formula), and precipitation.
The data assimilation scheme used is the Navy Coupled Ocean Data Assimi-lation system (Cummings2005), which uses the model forecast as a first guess in a Multi-Variate Optimal Interpolation scheme and assimilates available along-track satellite altimeter observations (obtained via the NAVOCEANO Altimeter Data Fusion Center), satellite and in situ SST as well as available in situ vertical tem-perature and salinity profiles from Expendable BathyThermographs, ARGO floats, and moored buoys. The surface measurements are projected to the model interior using the Modular Ocean Data Assimilation System (Fox et al.2002).
Mapping the Ocean Current Strength and Persistence… 183
On a daily basis, 5-day hindcasts and 5-day forecasts are produced. The raw data are interpolated to 33fixed horizontal levels, which are 0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1,000, 1,100, 1,200, 1,300, 1,400, 1,500, 1,750, 2,000, 2,500, 3,000, 3,500, 4,000, 4,500, 5,000, and 5,500 m.
U- and v-component velocities from 1 December, 2009 to 31 January, 2013 were downloaded and subset to 25–35ºE and 27–36ºS. The data were generated by two experiments. The first experiment (expt_90.8) ended on 2 January, 2011, after which expt_90.9 was used. The two experiments are subtly different in that the top layer in expt_90.9 was 1 m thick (as opposed to 3 m in expt_90.8). This difference is not expected to affect our analysis.
The ability to predict the behaviour of the Agulhas Current will be advantageous for the integration of any future power plants into the national power pool. Accurate forecasts at a high temporal resolution will ensure the maximum utilisation of an ocean current power plant.
Acoustic Doppler Current Profilers
Between 2005 and 2010, the South African electricity utility, Eskom, conducted a series of in situ current measurements along the eastern shores of South Africa as part of a preliminary assessment of the Agulhas Current as a source of energy. The in situ ocean currents were measured using moored ADCPs at selected sites along the continental shelf and in water depths ranging from 96 to 60 m. All ADCPs sampled ocean current velocities throughout the water column in 2-m-high vertical bins. Bins from different deployments were concatenated by linking together measurements from the closest bin (nearest bin approach). A summary of this ADCP data is provided in Table1.
It is observed that the ADCP measurements (Table1) were taken at the periphery of the current. Rouault and Penven (2011) found that near the location of the East London, the landward edge of the Agulhas Current is generally lies 20 km from the shore and above the 100 m isobath. Note that the dates on which each data set was recorded do not coincide and this can possibly lead to a bias towards one site.
Between 2012 and mid-2013, an additional two ADCPs were deployed at a mid-shelf and offshore location and resulted in an 18-month period of continuous data in the region of 28.8°E and 32.5°S. The details of the captured data are outlined in Table2. The data were collected using Teledyne RDI ADCPs with a 60-min temporal resolution. Viable data for the mid-shelf location range from 84 to 10 m below the sea surface and for the offshore location, and from 238 to 22 m below the sea surface.
184 I. Meyer et al.
Current Strength and Variability
Comparison GlobCurrent, HYCOM, ADCPs
To compare the three ocean velocity products, Principal Component Analysis (PCA) was applied to the ADCP velocity data as well as the GlobCurrent and HYCOM data at the same locations (Cape Morgan, East London, and Fish River) and depths. PCA decomposes data in terms of orthogonal basis functions tofind time series and spatial patterns (Wold et al.1987). The two eigenvectors contain most of the details about the data. They were computed and plotted as 95% con-fidence interval ellipses in Fig.2and represent the two dominant directional modes of the measured current velocities at the three selected locations, indicating the dominant current direction as well as its lateral variation.
Figure2 provides a good overview of the Agulhas Current time-averaged strength as well as its overall variability. Comparisons between the in situ satellite and numerical model output data sets show distinct differences. From the in situ Table 1 Details of in situ ADCP measurements
ADCP site name
Instrument type
Longitude (E)
Latitude (S)
Water depth (m)
Record length Sampling interval (h) Cape
Morgan CM305
RDI 300 28.83183 32.50733 89 2009/12/05-2010/03/03 1
Cape Morgan CM306
RDI 300 28.83179 32.50725 87 2010/03/03-2010/09/13 1
Fish River FR308
RDI 300 27.29750 33.70335 88 2009/12/04-2010/03/04 1
Fish River FR309
RDI 300 27.29745 33.71332 91 2010/03/04-2010/09/03 1
East London EL314
RDI 300 28.00866 32.15145 82 2009/12/04-2010/03/03 1
East London EL315
RDI 300 28.08651 33.15140 85 2010/03/03-2010/09/13 1
Table 2 Deployment series 2: details of available ADCP data Location ADCP type/bin
resolution (m)
Distance from shore (km)
Time period Sounding
depth (m)
Mid-shelf RDI 300/2 14 2012/01/24-2013/06/30 91
Offshore (edge of shelf)
RDI 150/6 18 2012/01/24-2013/06/30 255
Mapping the Ocean Current Strength and Persistence… 185
ADCP data, it is seen that the Cape Morgan location is the most energetic and has the strongest major velocity component. This finding is reiterated by the GlobCurrent data but not by the HYCOM data.
Comparing the ADCP and GlobCurrent ellipses (Fig.2a, black and red, respectively), it is evident that although the direction offlow is similar, there are significant differences between the two data sets at all three locations. The ADCP data indicate a much stronger south-westward flowing velocity component with larger lateral variations compared to the GlobCurrent data.
The HYCOM velocity map (Fig.2b) indicates that the data-assimilative mod-elling system is able to produce high mean velocities, and comparing the HYCOM ellipses to the ADCP ellipses suggests that the mean south-westward component is better represented than in GlobCurrent, but the lateral variability seems to be reduced in HYCOM.
In agreement with the current ellipses (Fig.2a), the major and minor velocity components summarised in Table3 confirm that the GlobCurrent data underesti-mate the ADCP measured current velocity by ±60%. While there is a slight improvement in HYCOM, the data-assimilative modelling system still underesti-mates the measured ADCP velocities (Table4). It is important to note the differ-ences between satellites remotely sensed, modelled, and in situ observed data because these differences could lead to incorrect site selection and evaluations for energy production. Further, if the HYCOM data set is used as afirst step towards identifying energetic regions prior to deploying in situ measurement devices, this data set would lead to incorrect assumptions about the most energetic region.
Further, the significant under prediction seen in the GlobCurrent data set can result in termination of further exploration.
Fig. 2 aMap of Agulhas time-averaged currents from the GlobCurrent product with overlaid current ellipses from ADCP data (black, at 20 m or in the upper layer best suited for energy production) and GlobCurrent data (red, at 15 m depth), and b map of Agulhas time-averaged currents from the HYCOM product with overlaid current ellipses from ADCP data (black, at 20 m or in the upper layer best suited for energy production) and HYCOM data (red, at same depth as ADCPs)
186 I. Meyer et al.