Visualization and Analysis of Currents and Temperature
Distribution Based on Ocean Data (Abstract)
Chi Zhang, Takashi Uenaka, Naohisa Sakamoto, and Koji Koyamada

Graduate School of Engineering, Kyoto University, Japan1Introduction

According to the existing theories, the ﬁshing hauls havea relationship with the distribution of temperatures and vortices. When warm currents meet cold currents, warm currents lift the nutrients in cold currents up to the shallow partof the sea, which makes a favorable environment for the reproduction of plankton, and thus the plankton-eating ﬁshwill be attracted. Besides, many kinds of ﬁsh move withcurrents, hence the ﬁsh density may be relatively higher at
the conﬂuence of warm currents and cold currents. Vortices also help to gather nutrients and ﬁsh. To ﬁnd the relationship between these factors, some visualization methods
need to be applied to our system. In this paper, we proposea system which is composed of overall and detailed visualization tools in order to verify the relationship between
these factors. As for the visualization tools, we develop aparallel coordinates plot to show the overview of the highdimensional ocean data, a streamline tool to show sea currents and a volume rendering tool to show the distributionof temperatures.
In the streamline tool, the 3-dimensional currents velocity ﬁeld is visualized by streamlines with the starting points
very close to vortices. Therefore, the features of these vortices are illustrated intuitively and we can ﬁnd some typicalvortices by comparison. The volume rendering tool is usedfor visualizing the overall distribution of temperatures. In
the volume rendering view, a powerful tool is developed foruser to customize the transfer function so as to emphasize
some signiﬁcant information and ignore the minor part. By
ortho-slices, the 2-dimensional distribution of temperatureswith diﬀerent depths can be shown respectively as 2D plots,
which is useful for us to understand the details of the internal distribution.
2Related Works

The study on the relationships between the ﬁshing haulsof squids in the sea area in Japan and the hydro-graphic conditions was ﬁrst proposed by Suzuki in 1963[3]. This study
explains why squids tend to migrate to "boundary zones"where diﬀerent water masses meet each other and create
proliﬁc ﬁshing areas. We try to get the same results as thisstudy but using modern techniques of visualization to makea system allowing users to view and select the useful information in the data conveniently. Not only the ﬂuid visualization, but also the ﬂow visualization has been a primordial subject for researchers in a wide range of scientiﬁcﬁelds. A lot of diﬀerent rendering techniques has been developed[1]. From all those techniques, we use the volume
rendering to visualize the distribution of temperatures in theocean. Vortices are important features in ﬂow research andvisualization. Studies concerning vortices detection havebeen introduced by Sadarjoen et al.[2] and the scale calculation method for vortices proposed by Guo et al.[4] is applied
to our system. Parallel coordinates plot is used in our system for providing an overview about the high-dimensional
ocean data. To solve the cluttering problem, the k-means al-gorithm, which is introduced by Hartigan [5] and improved
by Hamerly [6], is utilized to partition the input data into anumber of partitions.
3Proposed System

3.1 Overall Exploration of Data
Parallel coordinates plot is widely used for visualizing
and analyzing multivariate data. In a parallel coordinatesplot, each dimension of the input data is plotted as a vertical axis. Diﬀerent with the Cartesian coordinate system,
all of the axes are parallel with each other and a data pointis plotted as a polyline connecting the corresponding positions on these axes so that it is possible to show more than
three dimensions in a 2D plane. Therefore, parallel coordinates plot is a good way to provide the overview of theocean data. Users can pick up the data points that they wantto pay attention to by simply select the relative range on one
or more axes. Furthermore, when the size of the input data
is extremely large and the whole data can not be read into
the main memory, the out-of-core feature can be utilized
to process every part of the data sequentially. Only a partof the data is loaded at a time and after the calculation, thepresent data will be deleted from the memory and the next
part of data will be loaded and processed. This procedure
will be repeated until all of the data is processed. The highspeed k-means clustering method[6] can be used to solve
the cluttering problem that when the number of data points
is very large and it is diﬃcult for users to discern so manylines. The input data is partitioned to a number of clusters
and each cluster is plotted as a polygon in parallel coordinates, which accelerates the rendering as well.
3.2 Extraction of Vortices
In our system, the types of vortices are determined by thedirections of vortices. In a horizontal plane, the directionsof vortices can be divided into two cases: the direction of
the currents in a vortex points toward to its center or far awayfrom it. Similarly, in vertical direction, we can also divide
the direction of currents into two cases. Therefore, there are
four types of vortices in our system, which are detected bychecking the real parts and imaginary parts of the eigenvalues of the Jacobian matrix calculated from the vector ﬁeldof the currents in a vortex region.
To detect vortices from a vector ﬁeld, ﬁrstly we calculatea critical point where the velocity vector vanishes. Then,
we evaluate the Jacobian matrix J, which is a vector gradient tensor, from the vector ﬁeld at the critical point in a cell.
Then the eigenvalues of the matrix J are calculated. If there
are two conjugate complex eigenvalues, the critical point iscategorized as the center of a vortex.
Streamlines are used for visualizing sea currents in oursystem, especially the sea currents around vortices. By
streamlines plot, we view and analyze the shape and therange of vortices. We suppose that the scale of a vortex to bemainly determined by the range of it. The cross method[4]is used for detecting the range of a vortex and the radius of avortex can be ﬁgured out. By specifying the threshold valueof the radius, our system can pick up the vortices above acertain scale.
3.3

Distribution of Temperatures

Volume rendering is used to visualize three dimensional
data without imposing any geometric structure on it. It isapplied to our system to show the distribution of temperatures. Because the temperature data is converted from thevolume data with uniform grids, it is appropriate to use volume rendering to render the input data.
By adjusting the transfer function, it is possible to viewthe internal structure of the 3D object rendered by volumerendering. Besides, another method named orthoslice isalso utilized in our system to visualize the distribution oftemperatures at a certain depth. In our system, the proportion of depth can be speciﬁed by users. A proportionality
factor k ∈ [0, 1] is used for representing the proportion of
the height from the bottom of the sample ocean data. Assume that D is the total depth of the ocean and d is the depth
from the ocean surface, the equation used to calculate thedepth d will bed = (1 − k)DFig. 2. Extraction of Vortices(1)

Besides, the orthoslice and streamline can be shown simultaneously so that we can compare the position of vortices
and the distribution of temperatures to analyze the relationship between them.
4

Experimental Results

We select the ocean data near Aomori in Japan as thesample data. The overview of the input data is visualized
as clustered parallel coordinates, which is shown in Figure
1. The vortices with radii over 50 kilometers have been extracted and the result is illustrated as Figure 2. In this ﬁgure, vortices are plotted as streamlines and the small arrows
show the direction of the sea currents. The background is
the orthoslice used for visualizing the distribution of temperatures. As mentioned in the previous section, the proportionality factor k is customizable to set the proportionof depth. A smaller value of k makes the slice close to thesea surface, while a larger value of k makes the slice closeto the bottom of the sea. We set the value to 0.99 here toshow the temperatures distribution near the sea surface. Inorthoslice, the rainbow color map is utilized, meaning thatthe sea areas with higher temperatures are colored in red
while the sea areas with lower temperatures are colored inblue. Therefore, the areas in yellow or green are consideredas the areas where warm currents meet cold currents.
Figure 3 illustrates the distribution of temperatures byvolume rendering. The color map of orthoslice is the sameas that of orthoslice in Figure 2. The transfer function has
been adjusted to set lower opacity values to the areas withlower temperatures and higher opacity values to the areas
with higher temperatures so that only the areas with highertemperatures are emphasized and the internal structure ofrendered volume object is can be viewed.

Fig. 3. Temperatures Distribution by Volume Rendering

Fig. 4. Volume Rendering with Streamlines
Fig. 1. Overview by Clustered Parallel Coordinates

The volume rendering and streamlines can also be shown
together in one window as Figure 4. Diﬀerent with the ren-dering result in Figure 2, both of the volume rendering viewand streamlines are three dimensional. By adjusting thetransfer function of volume rendering, we can emphasize
the areas with the temperatures in a certain range and explore the vortices there.
5Conclusions and Future WorkIn our system, the vortices in the ocean data are extracted
and their scale is able to be ﬁgured out. With volume rendering, the streamlines of sea currents and the distributionof temperatures can be displayed together in a window sothat users can analyze the temperatures and vortices conveniently. In the future, the detecting and extraction of the upwelling with some typical types of vortices in three dimensions will be improved. Besides, the movement and changesof vortices will be traced and visualized in some properways. If the relationships between the climate change andthe movement of the areas with high ﬁshing hauls can bediscovered, countermeasures can be taken to deal with theeﬀect of the global warming.
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