Voting-based Ensemble-averaging Visualization for ...

Voting-based Ensemble-averaging Visualization for Water Mass Distribution Kun Zhao1, Satoshi Nakada2, Naohisa Sakamoto2, Koji Koyamada2 1

Graduate School of Engineering, Kyoto University, Japan

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Institute for Liberal Arts and Sciences, Kyoto University, Japan

Abstract The distribution of water masses has become one of the most important topics in recent oceanic research. Water masses flow dynamically and have interannual variability, hence the distribution can change dramatically even over short time periods and may differ from year to year. In this paper, we use a high-resolution ocean dataset, which contains multiple ocean variables, to visualize the details of the water mass. Because water mass can be defined by multiple ocean variables (e.g. temperature, salinity), we develop a multi-variate visualization system, which allows us to extract the time-varying distributions of water masses from multiple variables. The visualization is then adjusted by multiple ocean specialists because directly applying the existing definition to extract the water mass would result in incorrect rendering results. This leads to another problem that different ocean specialists may have different perspectives on the distribution of water masses so that the adjustments would also be different. To solve this problem, an ensemble average process is performed for the adjusted rendering results from multiple ocean specialists. To increase the authenticity, we also add a voting scheme to the system so that a majority rule can be applied to the ensemble-averaging result. As the application of the proposed voting-based ensemble-averaging visualization system, we first show the interannual variability of the significant water mass and then visualize the dynamic behavior for the period of interest in different years. We also highlight a mixing phenomenon that has a strong influence on the distribution of the water mass. As a result, we can obtain a clear and accurate visualization of the water mass distribution.

Keywords oceanography, ocean flows, ensemble-averaged visualization

1 Introduction In the recent oceanic visualization research, the distribution of water mass has become one of the most important topics. A water mass in the ocean is a large water body spreading in a certain region which holds the characteristics of relative interior homogeneity and conservativeness, while possessing obvious exterior differentia from the surrounding water [15]. Since the water mass has dynamic activity and interannual variability, the distribution for the significant water mass (e.g. Oyashio water) could be dramatic even for a short time period and totally different for different years. Such an interannual variability of water mass dynamics is highly related to the rich fishery grounds, global warming and other oceanic phenomenon [1, 18, 19]. Traditional approaches to extract the water mass are mainly focusing on the visualization of the static distribution [1, 6, 7, 9, 13, 15, 16]. However, the features of interannual variability and dynamic activity means that the analysis for the dynamic behaviors of water mass distribution is required. Moreover, to visualize the distribution of water masses, observed data is always used in the traditional approach. Because the observed data has a low temporal and spatial resolution, the visualization result cannot show the dynamic activity and 3-D distribution. Furthermore, the traditional approach always classifies different water masses based on a unique definition (e.g. [13]). However, even though the water mass keeps a relative interior homogeneity and conservativeness, the interannual variability would make water mass definition also varies for each year. Hence, the adjustments from the ocean specialists are needed to extract the correct distribution of water masses. As a result, the usage of a high-resolution ocean data, and an efficient visualization, which could provide a result based on the ocean specialists’ adjustments are needed to visualize the complex distribution of water mass. In this paper, to show the details of the water mass clearly, we use a multi-variate highresolution ocean simulation dataset, with a location of the northwestern Pacific near Japan. The

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dataset is simulated with a high temporal precise (one time step for 3 hours) to provide a detailed information for the dynamics of water mass. To extract the distribution of the water mass from the simulated ocean dataset, we develop a multi-variate visualization system, which allows us to investigate the time-varying distributions of ocean physical properties additionally from the adjustments of ocean specialists. Such adjustments are very important to extract the correct water mass distribution but it would also lead to another problem that, different ocean specialists would have a different perspective for the distribution of water masses. To solve this problem, we achieve an ensemble-averaged visualization based on the adjusted rendering results. A voting scheme is also implemented to the system so that the majority rule can also be applied to the ensembleaveraging visualization results. In the experiments, we first show the interannual variability for the significant water mass and then visualize the dynamic behaviors for the interested period for different years. We also show a mixture phenomenon, which makes a strong influence on the water mass distribution, for out interested water masses to obtain a detailed analysis. Consequently, we can obtain a clear and accurate visualization study for the water mass distribution.

2 Related Works Numerous studies have been conducted to visualize the static distribution of water masses [1, 6, 7, 9, 13, 15, 16]. However, no visualization study of the interannual variability of water mass dynamics has been performed in the northwestern Pacific Ocean near Japan. These traditional approaches have all used observed data. Because observed data does not have a high temporal resolution (usually, the time-scale range from one month to one week), the dynamic behavior of the distribution of different water masses has been difficult to observe. In addition, the traditional approaches always utilize surface rendering to visualize the distribution of the water masses [7, 15, 16]. This is done because 2-D rendering can be performed easily and smoothly. Contour lines are always used to define the features at the surface. However, 2-D renderings do not provide clear images of the distributions at depth. In our previous work [6], we have implemented a particle-based volume rendering to visualize the distribution of the water mass. Due to the large data size for the ocean data, a compression method [21][22] is developed to reduce the data size. As a result, the proposed system can efficiently extract a dynamic distribution of the water mass in 3-D. However, in this work we only used the definition [13] to extract different water masses and did not considered the uncertainty feature, which could lead to an inaccurate distribution. To overcome this shortcoming, we have found the ensemble-based visualization could be an efficient approach. In general, the traditional ensemble-based visualization means the visualization approaches that aim to visually analyze the ensemble datasets. Hear the ensemble represents a collection of related dataset [23]. This kind of dataset can always be found in some scientific simulation fields, which are repeatedly conducted with slightly different input parameters. Such datasets may have many similarities but may still be different for some local parts. The ensemblebased visualization approach is to explore the similarities, differences or other features among the ensemble datasets to analyze the simulation result. In fact, the ensemble-based visualizations have been used in many recent studies. Smith Kayne M. et al. [20] clustered stochastic simulation data by qualitative phenomenon and then computed average flow quantities within each cluster to visualize the qualitative features of the stochastic simulations. Kristin Potter et al. [14] presented Ensemble-Vis, which is a collection of overview and statistical displays that are interactively linked. This framework is used to explore relationships in dynamic systems with the ensemble data. Jibonananda Sanyal et al. [2] developed a tool named Noodles for operational meteorologists to visualize ensemble uncertainty of the numerical weather model. Similar approaches are found in other studies [12]. Such approaches are implemented to handle the ensemble dataset, and the rendering results demonstrate the efficiency of this application of ensemble-based visualizations. In the present paper, even though the input datasets is the static datasets (not ensemble), the adjustments from several ocean specialists are made and the adjusted results are different from each other. Hence, we regard the adjusted visualization results as the ensembles and perform an ensemble-based visualization to extract the water mass distribution.

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3 Visualization System The volume data used in our visualization system is from a high-precision ocean simulation that is calculated by a coupled land-ocean model consisting of both a hydrometeorological model [4] and a three-dimensional OGCM (Ocean General Circulation Model) developed at Kyoto University [5]. The area for this simulation is the northwestern Pacific Ocean near Japan (Figure 1). This data requires approximately 2.7 GB for all of the simulated properties for one time step. The dataset contains eight time steps for each day (one time step represents three hours). The complete dataset requires approximately 1 TB for each year and contains data from 2008 to 2012. The resolution of the data is 381×345×78, which represents a total depth of 5750 m. The depth direction uses a rectilinear grid instead of a uniform grid. Because the upper ocean has more intensive activity, a denser grid is used for the upper ocean (especially for the area shallower than 500 m), and a coarser grid is used for the deep ocean area. In addition, our research is mainly focused on the upper ocean.

Fig. 1 The location of the simulation data. Because the dataset is in a rectilinear structure in the depth direction, we first interpolate the data to construct a uniform mesh for the depth (Figure 2). This step ensures that the rendering result will provide precise depth information and also keep a fast rendering speed (see details in section 3.1). According to the ocean specialists, the upper ocean area (up to about 500 m for the depth) is the possible water mass distribution area and a 5m resolution would be ideal to extract the range of water mass. As a result, the depth direction is interpolated into 100 uniform grids to represent the upper ocean area. Consequently, the data resolution is changed to 381×345×100.

Fig. 2 The framework of the proposed visualization system After the data interpolation, we develop a multi-variate visualization system to extract the water mass distribution from multiple variables (see details in section 3.1). This system allows users to investigate the time-varying distributions after adjustments are made by the ocean specialists. A voting-based ensemble-averaging visualization is also provided to generate a more

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authentic rendering of the distribution of the water mass (see details in section 3.2 and 3.3). Then ocean specialists will make some revision on the transfer function and the above rendering process will be repeated. Such adjustment, voting-based ensemble-averaging visualization, feedback loops are performed multiple times to make sure a most accurate water mass distribution can be found. The overview of the proposed visualization system is shown in Figure 2.

3.1 Multi-variate Visualization We identify and partition the oceanic water masses using the interpolated time-varying ocean data. The oceanographic structure in the study area is characterized by two major water types: the subarctic Oyashio Water (OW) and the Tsugaru Warm Water (TW). The flow and physical conditions of the Oyashio Water are strongly affected by the Tsugaru Warm Water (TW). Because a water mass is relatively homogeneous, we can define the water mass with multiple ocean properties. The water masses in our study area are defined according to Ana L. R. et al. [13] (left figure of Figure 3). Hence, we can classify different water masses with different colors and opacities based on these definitions and use this classification as our transfer function. Before the rendering process, the volume data and transfer function are stored as multi-dimensional textures in the GPU. Because the rectilinear mesh is converted into uniform mesh in the preprocess, the texture can be directly mapped as the uniform mesh. We then render the multi-variate volume based on the transfer function with the ray casting rendering algorithm [8]. In this way, we can obtain a rendering result for the distribution of the water masses.

Fig. 3 Left: Definitions of Oyashio Water (OW) and Tsugaru Warm Water (TW) from Ana L. R. [13]. The color is used as the transfer function in the visualization process. Right: transfer function design based on the scatter plots of salinity and temperature properties. The ocean data at 00:00, 1st, April, 2009 is used in this figure.

3.2 Ensemble averaging Even though the water mass is defined based on several ocean properties (Figure 3), such a definition may change in different years due to interannual variability. While the definition of Ana L. R [13] is based on observed data, our work uses simulation data. Direct application of this definition to the simulation data results in an erroneous water mass distribution (see Figure 9). As a result, the perspectives and feedbacks from ocean specialists are needed to adjust the transfer function. During the adjustment, our system provide the scatter plots of the related ocean properties (in the current work, they are salinity and temperature) to help the specialists to explore the water mass distribution (right figure of Figure 3). However, different specialists may have different definitions of the water mass, and the resulting visualizations would also be different. To solve this problem, we use an ensembleaveraged rendering to show the visualization results from different ocean specialists. With our multi-variate rendering technique, the color and opacity of each sampling point is calculated as the multiple transfer functions that are assigned by the ocean specialists. We can then obtain an averaged rendering result by superimposing the rendering results defined by different specialists on each other using alpha blending. Obviously, the parts that are superimposed more closely are the more realistic parts of the water mass.

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Fig.4 Ensemble-averaging Visualization

3.3 Voting System The ensemble-averaging result can tell a more likely distribution for the water mass, but such average might also bring the minority opinion, which might be an error, to the visualization result. To overcome this shortcoming, a voting system is added to process the ensemble-averaging result. In this voting system, a voting number is need to be set. With this voting number, only the parts with the superimposing that is larger than voting number are drawn. Assuming we have five rendering results from five transfer function (TF1 ~ TF5), Figure 5 gives a simple example for this voting process.

Fig .5 Voting-based ensemble-averaging visualization results. The above show five rendering results from five different transfer functions (TF1 ~ TF5). The below shows the voting-based ensemble-averaging results with different voting numbers. As Figure 5 shows, when the voting number is set as 5, only the parts with five times of superimposing are drawn (the red triangle and the blue circle.) When the voting number is set as 4, four times of superimposing are drawn and the visible range becomes larger (the green cube also becomes visible). When the voting number is set as 0, the result just shows the simple averaging of the five rendering results. Assume the orange triangle as the error distribution of the water mass from a minority opinion, a proper voting number (larger than 3 in this case) can successfully filter out such part.

4 Applications and Results We apply our proposed visualization system to the high-resolution ocean dataset to visualize the interannual dynamic distribution of the water mass. We first generate an overview visualization to show the interannual variability. We then choose a period of interest to show the dynamic behaviors and study the interannual variability of the water mass dynamics for this period in detail. The experiment is conducted with an Intel Core i7-2820QM CPU (2.3 GHz), an NVidia GeForce GTX580M 2 GB GPU, 16 GB of system memory, and the Ubuntu 12.04 LTS operating system.

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4.1 Overview of Interannual Variability The main focus of this study is the Oyashio Water. The Oyashio Water is a cold subarctic ocean water mass that flows south and circulates counterclockwise in the western North Pacific Ocean. This water contains abundant nutrients and a large amount of plankton. Oyashio Water has a large effect on the distribution of fisheries because the amount of plankton in the water is closely linked to the performance of the fisheries [7, 9]. In our study, we mainly observe two types of Oyashio Water: the Oyashio Water (OW) system and the Coastal Oyashio Water (C-OW) system. To show the interannual variability for the entire time period, we first generate an overview rendering for March, April and May of 2008 to 2012. The period from March to May is the most active period for the Oyashio Water. For each month, we first average the volume data for the entire month (approximately 240 time steps). We then calculate the ensemble-averaged visualization for each month. We first use the transfer function from the definition of Ana L. R. [13] (Figure 3) to render the averaged volume data for each month and then show the rendering results to five ocean specialists. The rendering result for April 2011 is shown in Figure 6. The ocean specialists then adjust the transfer function for each month to obtain a rendering that agrees with his expectations. The five rendering results for April 2011 after the adjustments are also shown in Figure 6 (from TF1 to TF5; the opacity shows the velocity property). Clearly, the results from the different specialists are different. We then calculate the ensemble average for the five adjusted rendering results; the ensemble-averaged visualization result with a voting number of 0 is shown in the left figure of Figure 7. This figure shows the possible distribution for the water mass. We then set a voting number of 4 and the result is shown in the right figure of Figure 7. We calculate the ensembleaveraged visualization with a voting number of 4 for each month using the same approach to show the interannual variability of the Oyashio Water (Figure 8).

Ana L. R.’s definition

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Fig. 6 Rendering results using the transfer functions from the definitions of Ana L. R. [13] and five other ocean specialists for April 2011.

Fig. 7 Ensemble averaging with voting number of 0 (left) and 4 (right).

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OW in 2008 (Mar., Apr. and May)




OW in 2012 (Mar., Apr. and May) Fig. 8 Interannual variability of Oyashio Water from the year 2008 to 2012.For each year, the 3 figures show the ensemble-averaged rendering results for the month of March, April and May. The blue color shows the Oyashio Water area, and the grey color shows the land.

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The results clearly show the interannual variability of the Oyashio Water. In 2008, the OW distributes along the coast to form the C-OW, and the offshore OW flowed south in May, which is the typical pattern of the OW. However, the 2009 map of the OW shows that the C-OW was poorly developed in March and April. The OW is present in March, and the OW area extended into the offshore and coastal regions in May. The pattern of the OW in 2010 shows some C-OW is developed but the volume is very little, as shown by the paler blue color. The OW distributions in 2011 and 2012 indicate typical patterns similar to that of 2008, but their volume is lower than in 2008. We are particularly interested in the difference between 2009 and 2011, especially for April, because the results from this period show completely different distributions of the Oyashio Water.

4.2 Interannual Variability of Water Mass Dynamics In this section, our system is intended to observe the dynamic distribution of water masses. With the proposed voting-based ensemble-averaging rendering technique, the proper transfer function functions can be found. Then, we perform the rendering process at each time step to generate an animated rendering of the dynamic behavior of the water mass. The time-varying data are pre-loaded into the system memory for the time period of interest. During the rendering process, the pre-loaded time-varying data are transferred into the GPU step by step to provide the animated rendering result. We render the consecutive time steps for the periods of interest (April 2009 and 2011) as animations to analyze the dynamic distribution of the water mass. We choose the time period from 1-8 April and the rendering is performed with a voting number of 4. During the animation, the user can change the viewpoint and zoom to obtain a clear view of the dynamics of the water mass. Figure 9 shows the snapshots of the animation results for 1-8 April 2009. Figure 10 shows the snapshots of the animation results for 2011 for the same period. 2009.04.01, 00:00

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Fig. 9 The snapshots for the time-varying visualization of dynamic distribution of Oyashio Water from 1-8 April 2009. The area of Oyashio Water is shown in blue, and the land is shown in gray. 2011.04.01, 00:00

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Fig. 10 The dynamic distribution of Oyashio Water from 1-8 April 2011. The animation clearly shows that the dynamic flow of the Oyashio Water in these two years is completely different. In April 2009, the Oyashio Water flows southward, but the area of flow is narrow. The flow is relatively smooth and does not extend along the coast of Hokkaido. In addition, the Oyashio Water rarely flows around Funka Bay. Funka Bay is a rich fishery because a large amount of Oyashio Water flows into this area every year. The activity of Oyashio Water in April 2009 shows that the distribution of fishery grounds would be affected by the flow. In contrast, in April 2011, the Oyashio Water clearly flowed southward. The area of flow is much broader than in 2009. In addition, the activity of Oyashio Water around Funka Bay is much more dramatic than in 2009. Moreover, changing of view point or zooming operation (right two figures

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in Figure 9 and Figure 10) are supported due to the 3-D volume rendering. This feature can help user to observe the details of the water mass distribution, which the 2-D based or observed data based traditional visualization approach cannot provide. Moreover, we can observe that the direction of flow of the Oyashio Water changed near the Tsugaru Straight. In this location, the Tsugaru Warm Water flows into this area and mixes with the Oyashio Water, which changes the flow direction of the Oyashio Water. The Tsugaru Warm Water is a branch of the East Korea Warm Current that flows into the Pacific Ocean; it is the only major warm water mass in the Sea of Japan. After flowing through the Tsugaru Strait, the Tsugaru Warm Current passes along the eastern coast of Honshu and mixes with the Oyashio Water. Even though the Oyashio Water contains abundant nutrients, it is cold and is not suitable for fish. After mixing with the Tsugaru Warm Water, the Oyashio Water attains a suitable temperature for fish. As a result, mixing of the Tsugaru Warm Water and Oyashio Water has a strong effect on the oceanic conditions and especially the fisheries [16]. The dynamic variations of this mixture are important for research on the Oyashio Water. In fact, the mixing process of the Oyashio Water and Tsugaru Warm Water on short time scale (a few days or hours) is still unknown because previous studies investigated the seasonality or interannual variability of the water masses using observational data with spatiotemporally low resolution (e.g., [13]). After visualizing the Oyashio Water, we generated a visualization of the mixing of the Oyashio Water and the Tsugaru Warm Water. To visualize the mixing, we color the Oyashio Water blue and the Tsugaru Warm Water green. We calculate the ensemble-averaged visualization using a voting number of 4. In this case, the definition of Ana L. R. [13] result in an incorrect distribution obviously. For example, in 2011, Tsugaru Warm Water appears in an area that is separated from the main flow area, which is inaccurate (the red frame in the left figure of Figure 11). After the voting-based ensemble average based on the ocean specialists’ adjustments, the error is corrected. 2011.04.01, 00:00

Before Adjustment

2011.04.01, 00:00

After Adjustment

Fig. 11 The adjustment based on the ocean specialist’s feedback. The right figure shows the voting-based ensemble-averaging visualization result (voting number = 4). Using the ensemble averaging, we render the detailed mixing behaviors for the periods of 1-8 April of 2009 and 2011. The snapshots of the animation are shown in Figure 12 and Figure 13. These results show that in both 2009 and 2011, the Tsugaru Warm Water flows along the western coast of Honshu and bifurcates into the northward flowing Tsushima Warm Current, which flows along the western coast of Hokkaido, and the eastward flowing Tsugaru Warm Current (TsWC). The Tsugaru Warm Water that flows through the Tsugaru Straight enters the western Pacific, turns southward along the eastern coast, and merges with the Oyashio Water. Because the distribution of the Oyashio Water is different during these two years, the mixing conditions are also different. The mixing is much more dramatic in 2011 than in 2009. In 2011, the characteristics of the two water masses are maintained even as they merge in the eddy. Then, the green color of the Tsugaru Warm Water becomes paler, and blue color of the Oyashio Water disappears, indicating mixing of the two water masses. In the last half of the animation, we change the viewpoint to observe the water masses below the sea level. The images show the 3D pattern of the TW. The thick TW flows along the coast along with bowl-shaped eddies that fluctuate and merge with the thin OW. The TW was apparently 130 m thicker than the OW, which was approximately 30 m thick (Figure 14). Because the rendering is performed in real time, we can change the viewpoint and zoom in or out during the

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animation (Figure 14). This merging phenomenon is not observed in the 2009 results, which demonstrates the interannual variability of the water mass. 2009.04.01, 00:00

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Fig. 12 The mixing of the Oyashio Water and the Tsugaru Warm Water on 1-8 April, 2009. The Oyashio Water is shown in blue, and the Tsugaru Warm Water is shown in green. The land is shown in gray. 2011.04.01, 00:00

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Fig. 13 The mixing of the Oyashio Water and the Tsugaru Warm Water on 1-8 April 2011. (a)

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Fig. 14 (a) The depth scale for the visualization result. (b) View from below the sea level. (c) A zoomed view of the area around Tsugaru Straight.

5 Discussion The aforementioned results show that even though the previous definition of the water mass [13] would result in an incorrect distribution, the proposed voting-based ensemble-averaging visualization provides a realistic visualization result based on the adjustments made by several ocean specialists. During the rendering, we can also change the viewpoint of the 3D rendering to observe the details of the results. Because the ocean data has high temporal resolution (three hour time steps), the dynamic behaviors are accurately rendered by the animation. We show these visualization results to some experts working on ocean science. They give very positive comments that, with the proposed system, the detailed and relatively correct distribution of water masses can be visualized which is much helpful for the visual analysis. Traditionally, the seeking for accurate water mass distribution is always a very thorny problem due to the uncertainty feature of the water mass. The proposed system provides a good approach to solve such problem. We also let them to make a comparison with the rendering result from our previous approach [6], which only applied Ana. L. R.’s definition to extract the water mass distribution. They give the feedback that the proposed method can provide a more authentic result than the previous approach. Moreover, they also confirmed the validity of the dataset by comparing our result with some

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observational dataset [4]. The comparing result also shows that, our voting-based ensembleaveraging method can provide a high precise water mass distribution that is similar with the observational result. On the other hand, there are also some limitations for our current work. First, to visualize the dynamic distribution of the water mass, the time-varying ocean data are pre-loaded into the system memory for the time period of interest. If the user need to observe a long time period, there might be not enough system memory to store the time-varying data. In our future, we would like to add some special process to efficiently handle the time-varying rendering process. Moreover, in the current work, we did not consider the expertise and confidence of the ocean specialists, and just use the majority rule to averagely process the adjusted results from them. Even though water mass distribution is uncertain, high expertise can provide more authentic result. In our future work, the specialists with high expertise are supposed to have a more weight in the ensemble average process. The weight can be reflected as the opacity for the adjusted result used in the averaging. In this manner, how to apply the majority rule to the averaged result should become the issue. Furthermore, in the future work, we would like to build our system with the linked view feature. The linked view is supposed to be very helpful to facilitate the interactive exploration process. In that manner, since the system is renewed, we may need to perform the experiments again and new results are also possible to be found. The visualization of the interannual variability of the water mass is important for many research fields. For example, the result shows that in 2009, the flow of Oyashio Water was different than in other years. Sakurai et al. [18] showed that the Oyashio Water contains melted ice from the Sea of Okhostk. Temperature variability has a strong effect on the amount of Oyashio Water. Global warming is becoming increasingly serious, and the mean temperature increases every year. The El Niño-Southern Oscillation phenomenon is also related to the global temperature [19]. As a result, the analysis of the interannual variability of the Oyashio Water is applicable to research on global warming and the El Niño-Southern Oscillation phenomenon. The detailed analysis of the mixing of Oyashio Water and Tsugaru Warm Water is also important for oceanic research and especially for the exploration for rich fisheries. This is because the mixing region always contains nutrients and is closely linked with fisheries [15].

6 Conclusion In this paper, we generated a voting-based ensemble-averaging visualization of the interannual dynamics of the water mass dynamics of the northwestern Pacific Ocean near Japan. To illustrate the details of the water mass, we used a multi-variate high-resolution ocean dataset from the northwestern Pacific Ocean. The dataset is simulated with a high temporal resolution (time steps of three hours) to provide detailed information about the dynamics of the water mass. To extract the distribution of the water mass from the simulated ocean dataset, we developed a multi-variate visualization system that allows us to investigate the time-varying distributions of the ocean’s physical properties after adjustments are made by ocean specialists. Such adjustments are very important to extract the correct water mass distribution but it would also lead to another problem that, different ocean specialists would have different perspectives for the distribution of water masses. To solve this problem, we achieved an ensemble-averaged visualization based on the adjusted rendering results. A voting scheme is also implemented to the system so that the majority rule can also be applied to the ensemble-averaging visualization results to provide a more authentic distribution of water masses. In the experiments, we have shown the interannual variability of the significant water mass and then visualized the dynamic behavior for the period of interest in different years. We also highlighted a mixing phenomenon that has a strong influence on the distribution of the water mass. The visualization results and user feedbacks verified that the proposed system can obtain a clear and accurate visualization of the water mass distribution.

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Acknowledgment This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grants-in-Aid for JSPS Fellows (Grant Number 26･837), and was partially supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Grat-in-Aid for Data Integration and Analysis System (DIAS), Grant-in-Aid for Research Programs on Climate Change Adaptation (RECCA) and by the Japan Science and Technology Agency (JST), A-STEP project ("The research and development of fusion visualization technology", AS2415031H).

References 1. Ichiro Yasuda, Yoshiro Watanabe, On the relationship between the Oyashio front and saury fishing grounds in the north-western Pacific: a forecasting method for fishing ground locations, Fisheries Oceanography, Volume 3, Issue 3, pages 172-181, 1994. 2. Jibonananda Sanyal, Song Zhang, Jamie Dyer, Andrew Mercer, Philip Amburn and Robert J. Moorhead: Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty, IEEE Transactions on Visualization and Computer Graphics, Vol. 16, No. 6, pages 1421-1430, 2010. 3. Brasseur, P., P. Bahurel, L. Bertino, F. Birol, J.-M. Brankart, N. Ferry, S. Losa, E. Remy, J. Schröter, S. Skachko, C.-E. Testut, B. Tranchant, P.-J. van Leeuwen, and J. Verron, Data assimilation in operational ocean forecasting systems: the MERCATOR and MERSEA developments, Quart. J. R. Meteor. Soc., 131, pages 3561-3582, 2006. 4. 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, pages 47-52, 2012. 5. Ishikawa Y, Awaji T, Toyoda T, In T, Nishina K, Nakayama T, Shima S, Masuda S, High-resolution synthetic monitoring by a 4-dimensional variational data assimilation system in the northwestern North Pacific, Journal of Marine Systems, 78, pages 237-248, 2009. 6. Kun Zhao, Satoshi Nakada, Naohisa Sakamoto, Koji Koyamada, Chandrajit Bajaj, Yoichi Ishikawa, Toshiyuki Awaji, Teiji In, Sei-Ichi Saitoh, "A Visualization of the Dynamic Behaviors of the Mixture of Water Mass Using Ocean Simulation Data for Northwestern Pacific near Japan", International Journal of Modeling, Simulation, and Scientific Computing, Vol.4, Issue supp.01, 2013. 7. Harley E. Hurlburt, Alan J. Wallcraft, William J. Schmitz Jr., Patrick J. Hogan, E. Joseph Metzger, Dynamics of the Kuroshio/Oyashio current system using eddy-resolving models of the North Pacific Ocean, Journal of Geophysical Research, Vol. 101, No. C1, pages 941-976, 1996. 8. P. Sabella, A Rendering Algorithm for Visualizing 3D Scalar Fields, Computer Graphics, Vol.22, No.4, pages 51-58, 1988. 9. Leonid M. Mitnik, Vyacheslav B. Lobanov, Investigation of Oyashio-Kuroshio frontal zone using alos PALSAR images and ancillary information. In proceeding of: 2011 IEEE International Geoscience and Remote Sensing Symposium, 2011. 10. Mitsuyo Onishi and Kiyotaka Ohtani, Volume Transport of the Tsushima Warm Current, West of Tsugaru Strait Bifurcation Area, Journal of Oceanography, Vol. 53, pages 27-34. 1997 11. Miyake, H., I. Tanaka, and T. Murakami, Outflow of water from Funka Bay, Hokkaido, during early spring, Journal of Oceanography, 44, doi: 10.1007/BF02302640, pages 163-170, 1988. 12. Robert Sisnerosa, Jian Huangb, George Ostrouchovc, Sean Ahernc B. and David Semeraroa: Contrasting Climate Ensembles: A Model-based Visualization Approach for Analyzing Extreme Events, Procedia Computer Science, Volume 18, pages 2347-2356, 2013. 13. Ana Luisa Rosa, Yutaka Isoda, Kazuyuki Uehara and Tomokazu Aiki: Seasonal Variations of Water System Distribution and Flow Patterns in the Southern Sea Area of Hokkaido Japan, Journal of Oceanography, Vol. 63, pages 573-588, 2007. 14. Kristin Potter, Andrew Wilson, Peer-Timo Bremer, Dean Williams, Charles Doutriaux, Valerio Pascucci and Chris R. Johhson: Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data, IEEE Workshop on Knowledge Discovery from Climate Data: Prediction, Extremes, pages 233-240, 2009. 15. Guan-Jie Yang, and Ke-Qin Wu, A Computational Topology Approach to Automatic Partition and Visualization of Oceanic Water Masses. IEEE CAD/Graphics, page 493-496, 2007. 16. Keisuke Mizuno Dr., Short term variability in the Tsugaru warm current and its effect on fisheries, GeoJournal, Volume 16, Issue 1, pp 65-71, 1988. 17. Séférian, Roland, Daniele Iudicone, Laurent Bopp, Tilla Roy, Gurvan Madec: Water Mass Analysis of Effect of Climate Change on Air–Sea CO2 Fluxes: The Southern Ocean. J. Climate, 25, pages 3894-3908, 2012.

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18. Sakurai, Yasunori. "An overview of the Oyashio ecosystem." Deep Sea Research Part II: Topical Studies in Oceanography Vol. 54, No. 23, pages 2526-2542, 2007. 19. Meehl, G. A.; Teng, H.; Branstator, G. Future changes of El Niño in two global coupled climate models. Climate Dynamics. Vol. 26, No. 6, pages 549, 2006. 20. Smith, Kayne M.; Banks, David C.; Druckman, Neil; Beason, Kevin; Hussaini, M. Yousuff: Clustered Ensemble Averaging: A Technique for Visualizing Qualitative Features of Stochastic Simulations, Journal of Computational and Theoretical Nanoscience, Volume 3, Number 5, pages 752-760, 2006. 21. Zhao.K, Sakamoto. N, Koyamada. K., "A Temporal Coherence Based Framework for Visualizing TimeVarying Unstructured Volume with PBVR", In Proceedings of Asia Simulation Conference (AsiaSim2012), pp.163-174 (Part 1), 2012, 22. Zhao.K, Sakamoto. N, Koyamada. K., "A Volume Compression Scheme Based on Block Division with Fast Cubic B-spline Evaluation", In Proceedings of Asia Simulation Conference (AsiaSim2012), PP.373388 (Part 3), 2012, 23. Madhura N. Phadke, Lifford Pinto, Oluwafemi Alabi, Jonathan Harter, Russell M. Taylor, Xunlei Wu, Hannah Petersen, Steffen A. Bass, and Christopher G. Healey, Exploring Ensemble Visualization, Visualization and Data Analytics 2012, (San Francisco, California, 2012), vol. 8294, paper 0B, pp. 1–12.

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