A Visualization Study of Vortexes Extracted from theTremendous Ocean Simulation Data: An Application in Funka Bay
Satoshi NAKADA1, Yoichi ISHIKAWA2, Takashi UENAKA1, Naohisa SAKAMOTO1, Koji KOYAMADA1,Toshiyuki AWAJI3, and Sei-Ichi SAITOH412

1

Center for the Promotion of Excellence in Higher Education, Kyoto University, Kyoto University, Japan
Data Research Center for Marine-Earth Sciences, Japan Agency for Marine-Earth Science and Technology, Japan3
Department of Geophysics, Kyoto University, Japan
4
Faculty of Fisheries Sciences, Hokkaido University, Japan

Introduction

International activities toward realizing smart fishing [1] areevolving to reduce their operational risks, greenhouse gas
emissions and energy loss in response to recent hikes in theprice of crude oil [2]. The state-of-the-art ocean simulation withspatiotemporally high resolutions can provide the detailed andaccurate now/forecast of ocean currents, temperature and sealevel, and so on, which might help to improve the fishingefficiency [3]. The helpful information has to be correctly
informed to the fishermen in order to achieve the smart fishingeven if the now/forecasts of ocean states are perfectly accurate.
However, the tremendous data of ocean states is daily producedby the operational forecast simulation aided by data assimilation
system. Therefore, it is necessary that the beneficial information
for good fishing operations is promptly extracted from delugeddata sources of the ocean states and showed to the fishermen intime.
In this study, we prove a comprehensive visualization
method to effectively show the fruitful information using
realistic ocean datasets produced by a land-sea coupled modelwith spatiotemporally high resolution. We select Funka Bay
located in Northern Japan, Hokkaido (Figure 1) as a pilot study
area from a viewpoint of aquaculture. Funka Bay is a typicalenclosed bay and produces vast production of scallops and kelps
around the coastal ocean every year, and Alaska pollack in theinner region of the bay. It is known that their production isclosely related to the flow fields or seasonal eddies formed in
the bay. We visualize the characteristics of the eddies using thethree-dimensional visualization technique [4,5] to examine theflow pattern, spatial scale, lifespan, three-dimensional structure,and so on.

2Methodology

2.1

Data sets

The datasets used in this study are calculated by a coupled
land-oecan model consisting of both hydrometeological model[6] and a three-dimensional OGCM developed at Kyoto
University [7]. The coupled model was used to reproduce more
realistic land-ocean forecasts using the initial condition and thespun-up calculation from beginning of 2006 following Nakada
et al. (2012) [5].
The analysis period of 20082009 was selected since asignificant contrast in summer-time vortexes associated with thesurface salinity was found then which should be influenced by
the runoffs into the bay; the summer-time salinity in 2009 wasmuch less than in 2008.

Re-analysis and predicted meteorological datasets GPVMSM were used to be input to the coupled model, which aremean hourly air temperature, precipitation, cloud cover, relative
humidity, and wind speed. The data were interpolated onto grids
at an interval of 500×500 m in the land computational domainand 1.5 km in the ocean domain using the spline method on thewatershed. The coupled model was validated by using
observational data and showed both quantitative and qualitative
reproducibility of the temporal variations in the bay after severalcalibrations.
2.2

Visualization
To detect an important flow feature, vortices, in the ocean,
we employ a vortex detection algorithm depicting streamlines
around vortex cores coupled with the entire vortex area [5].
According to [5], approaches for detection of a typicaloceanographic feature, in particular, a vortex were evaluated interms of accuracy, clarity and usability.
The vortex detection algorithm consists of two parts. The
first part is vortex core detection. A geometry-based vortex coredetection method is used to detect vortex cores. The second partis closed streamline detection around vortex cores. Finally, thecombinations of vortex core regions and their surrounding
closed streamline regions form the entire vortex regions.

Figure 1 Map of the study area showing watersheds aroundFunka Bay. The typical summertime eddy is shown with thesimulated salinity field in 23 June 2008.

3

Results and Discussions

Figure 2 shows a good example of the flow field in the bay,
indicating the asymmetric twin vortex simulated by the coupledmodel. The northern vortex corresponds to the summertime
clockwise circulation indicated by the surface current velocity
indicates (Figure 1). The twin vortex is drawn by thesurrounding streamlines that starts from vortex cores. The
extension and density of the streamlines indicates the spatial
scales of the vortexes and their strength of divergence andconvergence, respectively (Figure 2a). The northern vortex is
big and shows the high-density streamlines suggesting the weak
divergence or nearly circulation. On the other hand, southern
vortex is small and strong divergence because of low density ofthe streamlines. The big vortex exhibiting shape of a mushroom(Figure 2b) indicates the deep structure (100 m) and the shear
flow field representing the typical baloclinic structure.
These characteristics of twin vortex are important
information associated with the transportation of scallops eggs
and/or leave and actually required for aquaculture management
instructed by fishery researchers of prefectural institutes. In thecase of understanding the characteristics using conventional
approaches, we generally need some graphics such as maps offlow fields at each depth and vertical section. However,
employing our visualization method, one three-dimensionalgraphic can intuitively exhibit the qualitative spatial structure of
the vortex structure. It is also found that our simulated results
were well consistent with the observational data derived by R/V
Ushio-Maru, Hokkaido University. This fact confirms that ourvisualization and simulation can satisfactorily provide the useful
information for the good fishing or aquaculture grounds.
4Conclusion

This study showed that a comprehensive visualization may
effectively provide the helpful information toward the smart
fishing using tremendous data of the daily ocean states produced
by a land-sea coupled model with spatiotemporally highresolution. The information can contain the spatial structuresand characteristics of the vortex and be effectively extracted
from the simulated data representing many ocean properties.
Our results underline that the development of visualization of
the operational simulation data is essential as same as theincrease of the simulation ability in order to achieve the prompt
and accurate transmission of the information.

Acknowledgements
This work was supported by “Hakodate Marine Bio ClusterProject” in the knowledge Cluster Program from 2009 and theGrand-in-Aid for University and Society Collaboration from theMinistry of Education, Culture, Sports, Science and Technology(MEXT), Japan.

References
[1]

[2]

[3]

[4]

[5]

[6]

[7]

Figure 2 (a) An example of visualization of the simulated
eddies depicted by stream lines and colored salinity field in
Funka Bay. (b) Overhead views of the eddy in 23 June, 2008.

Aps, R., Fetissov, M., Lassen, H., Smart management of the Baltic
Sea fishery system: Myth or reality?, Baltic International
Symposium (BALTIC), 2010 IEEE/OES US/EU, 1-9, 2010.
Driscoll, J. & Tyedmers, P, Fuel use and greenhouse gas emission
implications of fisheries management: the case of the New
England Atlantic herring fisher, Marine Policy, 34, 353‒359, 2010.
GODAE Coastal and Shelf seas Working Group, Towards theassessment and demonstration of the value of GODAE results forcoastal and shelf seas models and forecasting systems, 79pp, 2007.
Globus, A., C. Levit and T. Lasinski, A Tool for Visualizing theTopology of Three-Dimensional Vector Fields, Proceedings
Visualization ’91, San Diego, California, October 22–25, pp. 33–40, 1991.
Guo, D., Evangelinos, C., Patrikalakis, N.M., Flow featureextraction in oceanographic visualization. In: Computer Graphics
International Conference, IEEE Computer Society Press, Greece,
pp. 162–173, 2004.
Nakada, S., Y. Ishikawa, T. Awaji, T. In, S. Shima, T. Nakayama,
T. Isada, S. Saitoh, Modeling Runoff into a Region Of Freshwater
Influence for Improved Ocean Prediction: An Application in
Funka Bay, Hydrological Research Letter, 6, 47–52, 2012.
Ishikawa Y, Awaji T, Toyoda T, In T, Nishina K, Nakayama T,Shima S, Masuda S, High-resolution synthetic monitoring by a 4dimensional variational data assimilation system in thenorthwestern North Pacific, Journal of Marine Systems, 78, 237–
248, 2009.