[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

A comparative visualization tool for ocean data analysis based on mode water regions

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

Ocean data have been improved with the enhancement of observed values and the evolution of computational technologies. It has also been verified based on the reproducibility of various ocean phenomena. Mode water is one of the indicators for assessing ocean data because of its special properties. However, its definition differs for each ocean data. Besides, its observation is primarily performed by 2D analysis using the cutting plane of the ocean space. Therefore, reproducibility of the ocean space may have not been fully examined. Here, this paper presents a visual analysis tool for the feature of ocean data based on the 3D shape comparison of the mode water regions among three ocean datasets. Our comparison is based on similarity measure from shape appearances of the mode water regions extracted as isosurfaces. Users can interact with shape similarity data and a pair of isosurfaces. Our visualization tool supports to easily explore the relationship of different variable thresholds that are used for conditions of the mode water region and observe the specified parts of the pair in the 3D space. We demonstrate the availability and potential benefit of this approach through three examples that searched for the best conditions and expert feedback.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Alabi OS, Wu X, Harter JM, Phadke M, Pinto L, Petersen H, Bass S, Keifer M, Zhong S, Healey CG, Taylor RM (2012) Comparative visualization of ensembles using ensemble surface slicing. In: Proceedings of SPIE, 8294

  • Bimbo AD, Pala P (2006) Content-based retrieval of 3D models. ACM Trans Multim Comput Commun Appl 2(1):20–43

    Article  Google Scholar 

  • Biswas A, Dutta S, Shen HW, Woodring J (2013) An information-aware framework for exploring multivariate data sets. IEEE Trans Vis Comput Graph 19(12):2683–2692

    Article  Google Scholar 

  • Bruckner S, Möller T (2010) Isosurface similarity maps. Comput Graph Forum 29(3):773–782

    Article  Google Scholar 

  • Chaouch M, Verroust-Blondet A (2007) A new descriptor for 2D depth image indexing and 3D model retrieval. In: 2007 IEEE international conference on image processing, vol 6, pp 373–376

  • Chen DY, Tian XP, Shen YT, Ouhyoung M (2003) On visual similarity based 3D model retrieval. Comput Graph Forum 22(3):223–232

    Article  Google Scholar 

  • Davis XJ, Rothstein LM, Dewar WK, Menemenlis D (2011) Numerical investigations of seasonal and interannual variability of North Pacific subtropical mode water and its implications for Pacific climate variability. J Clim 24(11):2648–2665

    Article  Google Scholar 

  • Demir I, Kehrer J, Westermann R (2016) Screen-space silhouettes for visualizing ensembles of 3D isosurfaces. In: IEEE Pacific visualization symposium, pp 204–208

  • Douglass EM, Jayne SR, Peacock S, Bryan FO, Maltrud ME (2012) Subtropical mode water variability in a climatologically forced model in the northwestern Pacific Ocean. J Phys Oceanogr 42(1):126–140

    Article  Google Scholar 

  • ElNaghy H, Hamad S, Khalifa ME (2013) Taxonomy for 3D content-based object retrieval methods. Int J Recent Res Appl Stud 14(2):412–446

    Google Scholar 

  • Fujishiro I, Maeda Y, Sato H, Takeshima Y (1996) Volumetric data exploration using interval volume. IEEE Trans Vis Comput Graph 2(2):144–155

    Article  Google Scholar 

  • Gao W, Li P, Xie SP, Xu L, Liu C (2016) Multicore structure of the North Pacific subtropical mode water from enhanced argo observations. Geophys Res Lett 43(3):1249–1255

    Article  Google Scholar 

  • Han X, Li Z, Huang H, Kalogerakis E, Yu Y (2017) High-resolution shape completion using deep neural networks for global structure and local geometry inference. In: Proceedings of the IEEE international conference on computer vision, pp 85–93

  • Hazarika S, Biswas A, Dutta S, Shen HW (2018) Information guided exploration of scalar values and isocontours in ensemble datasets. Entropy 20(7):540–558

    Article  Google Scholar 

  • Hazarika S, Dutta S, Shen HW (2016) Visualizing the variations of ensemble of isosurfaces. In: IEEE Pacific visualization symposium, pp 209–213

  • Huber DF, Hebert M (2003) Fully automatic registration of multiple 3D data sets. Image Vis Comput 21(7):637–650

    Article  Google Scholar 

  • Johansson J, Ljung P, Jern M, Cooper M (2006) Revealing structure in visualizations of dense 2D and 3D parallel coordinates. Inf Vis 5(2):125–136

    Article  Google Scholar 

  • Lian Z, Godil A, Sun X (2010) Visual similarity based 3D shape retrieval using bag-of-features. In: 2010 Shape modeling international conference, pp 25–36

  • Lindstrom P, Turk G (2000) Image-driven simplification. ACM Trans Graph 19(3):204–241

    Article  Google Scholar 

  • Lin J, She MF, Tsai MH, Lin IC, Lau YC, Liu HH (2018) Retrieving 3D objects with articulated limbs by depth image input. In: VISIGRAPP (1: GRAPP), pp 101–111

  • Liu L, Silver D, Bemis K, Kang D, Curchitser E (2017) Illustrative visualization of mesoscale ocean eddies. Comput Graph Forum 36(3):447–458

    Article  Google Scholar 

  • Locarnini RA, Mishonov AV, Antonov JI, Boyer TP, Garcia HE, Baranova OK, Zweng MM, Paver CR, Reagan JR, Johnson DR, Hamilton M, Seidov D (2013) World ocean atlas volume 1: temperature. NOAA Atlas NESDIS 73, Silver Spring. MD 40:2013

  • Masumoto Y, Sasaki H, Kagimoto T, Komori N, Ishida A, Sasai Y, Miyama T, Motoi T, Mitsudera H, Takahashi K, Sakuma H, Yamagata T (2004) A fifty-year eddy-resolving simulation of the world ocean—preliminary outcomes of OFES (OGCM for the earth simulator). J Earth Simul 1:35–56

    Google Scholar 

  • Masuzawa J (1969) Subtropical mode water. In: Deep sea research and oceanographic abstracts, vol 16, no 5. Elsevier, Amsterdam, pp 463–468

  • Nishikawa S, Tsujino H, Sakamoto K, Nakano H (2010) Effects of mesoscale eddies on subduction and distribution of subtropical mode water in an eddy-resolving OGCM of the western North Pacific. J Phys Oceanogr 40(8):1748–1765

    Article  Google Scholar 

  • Ohbuchi R, Osada K, Furuya T, Banno T (2008) Salient local visual features for shape-based 3D model retrieval. In: Shape modeling and applications, pp 93–102

  • Oka E, Qiu B, Takatani Y, Enyo K, Sasano D, Kosugi N, Ishii M, Nakano T, Suga T (2015) Decadal variability of subtropical mode water subduction and its impact on biogeochemistry. J Oceanogr 71(4):389–400

    Article  Google Scholar 

  • Qi CR, Su H, Nießner M, Dai A, Yan M, Guibas LJ (2016) Volumetric and multi-view cnns for object classification on 3d data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5648–5656

  • Shih JL, Chen HY (2006) 3D model retrieval based on grid sphere and dodecahedral silhouette descriptors. In: 9th joint international conference on information sciences (JCIS-06), Atlantis Press

  • Shilane P, Min P, Kazhdan M, Funkhouser T (2004) The princeton shape benchmark. In: Shape modeling applications, pp 167–178

  • Su H, Maji S, Kalogerakis E, Learned-Miller E (2015) Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision, pp 945–953

  • Talley LD (1999) Some aspects of ocean heat transport by the shallow, intermediate and deep overturning circulations. In: Clark U, Webb S, Keigwin D (eds) Mechanisms of global climate change at millennial time scales. Geophysical monograph series, vol 112. AGU, Washington, D.C., pp 1–22

    Chapter  Google Scholar 

  • Tao J, Imre M, Wang C, Chawla NV, Guo H, Sever G, Kim SH (2018) Exploring time-varying multivariate volume data using matrix of isosurface similarity maps. IEEE Trans Vis Comput Graph 25(1):1236–1245

    Article  Google Scholar 

  • Tenth Report of the Joint Panel on Oceanographic Tables and Standards. UNESCO Technical Papers in Marine Science, vol 36 (1981)

  • Toyama K, Suga T (2011) Roles of mode waters in the formation and maintenance of central water in the North Pacific. In: New developments in mode-water research. Springer, Tokyo, pp 75–88

  • Usui N, Wakamatsu T, Tanaka Y, Hirose N, Toyoda T, Nishikawa S, Fujii Y, Takatsuki Y, Igarashi H, Nishikawa H, Ishikawa Y, Kuragano T, Kamachi M (2017) Four-dimensional variational ocean reanalysis: a 30-year high-resolution dataset in the western North Pacific (FORA-WNP30). J Oceanogr 73(2):205–233

    Article  Google Scholar 

  • Vranic DV, Saupe D (2004) 3D model retrieval. Doctoral Dissertation, University of Leipzig, pp 1–227

  • Wei TH, Chen CM, Woodring J, Zhang H, Shen HW (2017) Efficient distribution-based feature search in multi-field datasets. In: IEEE Pacific visualization symposium, pp 121–130

  • Weissenböck J, Fröhler B, Gröller E, Kastner J, Heinzl C (2018) Dynamic volume lines: visual comparison of 3D volumes through space-filling curves. IEEE Trans Vis Comput Graph 25(1):1040–1049

    Article  Google Scholar 

  • Xie Z, Xu K, Shan W, Liu L, Xiong Y, Huang H (2015) Projective feature learning for 3D shapes with multi-view depth images. Comput Graph Forum 34(7):1–11

    Article  Google Scholar 

  • Xu L, Xie SP, McClean JL, Liu Q, Sasaki H (2014) Mesoscale eddy effects on the subduction of North Pacific mode waters. J Geophys Res: Oceans 119(8):4867–4886

    Article  Google Scholar 

  • Xu L, Xie SP, Liu Q, Liu C, Li P, Lin X (2017) Evolution of the North Pacific subtropical mode water in anticyclonic eddies. J Geophys Res: Oceans 122(12):10118–10130

    Article  Google Scholar 

  • Yano M, Itoh T, Tanaka Y, Matsuoka D, Araki F (2018) Comparative 3D visualization tool for observation of mode water. In: IEEE Pacific visualization symposium, pp 230–234

  • Yasuda T, Kitamura Y (2003) Long-term variability of North Pacific subtropical mode water in response to spin-up of the subtropical gyre. J Oceanogr 59(3):279–290

    Article  Google Scholar 

  • Zhou H, Yuan X, Qu H, Cui W, Chen B (2008) Visual clustering in parallel coordinates. Comput Graph Forum 27(3):1047–1054

    Article  Google Scholar 

  • Zweng MM, Reagan JR, Antonov JI, Locarnini RA, Mishonov AV, Boyer TP, Garcia HE, Baranova OK, Johnson DR, Seidov D, Biddle MM (2013) World ocean atlas volume 2: salinity. NOAA Atlas NESDIS 74. Silver Spring. MD 39:2013

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Midori Yano.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yano, M., Itoh, T., Tanaka, Y. et al. A comparative visualization tool for ocean data analysis based on mode water regions. J Vis 23, 313–329 (2020). https://doi.org/10.1007/s12650-020-00629-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-020-00629-y

Keywords

Navigation