Top > JDR > An Experimental Data Handling System for Ensemble Numerical ...

single-dr.php

JDR Vol.8 No.1 pp. 48-56
(2013)
doi: 10.20965/jdr.2013.p0048

Paper:

Views over last 60 days: 475

An Experimental Data Handling System for Ensemble Numerical Weather Predictions Using a Web-Based Data Server and Analysis Tool “Gfdnavi”

Shigenori Otsuka*, Seiya Nishizawa**, Takeshi Horinouchi***,
and Shigeo Yoden*

*Division of Earth and Planetary Sciences, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo, Kyoto 606-8502, Japan

**RIKEN Advanced Institute for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan

***Division of Earth System Science, Graduate School of Environmental Earth Science, Hokkaido University, Kita-10, Nishi-5, Kita-ku, Sapporo 060-0810, Japan

Received:
October 10, 2012
Accepted:
November 22, 2012
Published:
February 1, 2013
Creative Commons License
Keywords:
analysis, visualization, database, web application, ensemble numerical weather predictions
Abstract
Gfdnavi is a web-based data and knowledge server program for geophysical fluid data that constructs databases, provides analysis and visualization tools, and shares knowledge documents. A new Gfdnavi user interface for analyzing and visualizing data on web browsers is developed to improve the user experience by providing seamless analysis and visualization operations, multiple diagram editing, a layer function, and so on. An experimental data handling system for ensemble numerical weather prediction data is constructed using Gfdnavi to address such issues as data processing and transfer between weather centers and decision makers in various sectors, including that of disaster management. Special tools to analyze and visualize ensemble numerical weather prediction data are implemented as user-defined Gfdnavi plug-ins. An interactive document that provides basic ideas of how to utilize probabilistic ensemble data information is written with the Gfdnavi knowledge documentation system, in which hyperlinks enable users to edit diagrams in the document.
Cite this article as:
S. Otsuka, S. Nishizawa, T. Horinouchi, and S. Yoden, “An Experimental Data Handling System for Ensemble Numerical Weather Predictions Using a Web-Based Data Server and Analysis Tool “Gfdnavi”,” J. Disaster Res., Vol.8 No.1, pp. 48-56, 2013.
Data files:
References
  1. [1] R. Mureau, F. Molteni, and T. N. Palmer, “Ensemble prediction using dynamically conditioned perturbations,” Quar. J. Roy. Meteor. Soc., Vol.119, pp. 299-323, 1993.
  2. [2] WMO, “Accelerating improvements in the accuracy of one-day to two-week high-impact weather forecasts for the benefit of society, the economy and the environment: THORPEX,” 2005,
    http://www.wmo.int/pages/prog/arep/wwrp/new/documents/brochure e.pdf
  3. [3] [accessed Jan. 17, 2013]
  4. [4] T. Horinouchi, S. Nishizawa, C. Watanabe, A. Tomobayashi, S. Otsuka, T. Koshiro, Y.-Y. Hayashi, and GFD Dennou Club, “Gfdnavi, web-based data and knowledge server software for geophysical fluid sciences, Part I: Rationales, stand-alone features, and supporting knowledge documentation linked to data,” Lecture Notes in Computer Science, Vol.6913, pp. 93-104, 2010.
  5. [5] S. Nishizawa, T. Horinouchi, C. Watanabe, Y. Isamoto, A. Tomobayashi, S. Otsuka, and GFD Dennou Club, “Gfdnavi, webbased data and knowledge server software for geophysical fluid sciences, Part II: RESTful web services and object-oriented programming interface,” Lecture Notes in Computer Science, Vol.6193, pp. 105-116, 2010.
  6. [6] T. N. Palmer, “The economic value of ensemble forecasts as a tool for risk assessment: From days to decades,” Quar. J. Roy. Meteor. Soc., Vol.128, pp. 747-774, 2002.
  7. [7] S. Yoden, K. Saito, T. Takemi, and S. Nishizawa, “New stages of international collaborations which serves for mitigation of meteorological disasters in Southeast Asia,” Tenki, Vol.55, pp. 705-708, 2008 (in Japanese).
  8. [8] P. J. Webster, “Myanmar’s deadly daffodil,” Nature Geoscience, Vol.1, pp. 488-490, 2008.
  9. [9] H. M. Fritz, C. D. Blount, S. Thwin, M. K. Thu, and N. Chan, “Cyclone Nargis storm surge in Myanmar,” Nature Geoscience, Vol.2, pp. 448-449, 2009.
  10. [10] K. Saito, J. Ishida, K. Aranami, T. Hara, T. Segawa, M. Narita, and Y. Honda, “Nonhydrostatic atmospheric models and operational development at JMA,” J. Meteor. Soc. Japan, Vol.85B, pp. 271-304, 2007.
  11. [11] A. F. Blumberg and G. L. Mellor, “A description of a three-dimensional coastal ocean circulation model,” In Threedimensional coastal ocean models, pp. 1-16, American Geophysical Union, 1987.
  12. [12] K. Saito, T. Kuroda, M. Kunii, and N. Kohno, “Numerical simulation of Myanmar cyclone Nargis and the associated storm surge Part II: Ensemble prediction,” J. Meteor. Soc. Japan, Vol.88, pp. 547-570, 2010.
  13. [13] T. Kuroda, K. Saito, M. Kunii, and N. Kohno, “Numerical simulations of Myanmar cyclone Nargis and the associated storm surge Part I: Forecast experiment with a nonhydrostatic model and simulation of storm surge,” J. Meteor. Soc. Japan, Vol.88, pp. 521-545, 2010.
  14. [14] J. W. Tukey, “Exploratory data analysis,” Addison-Wesley Publishing Co., 1977.
  15. [15] R.McGill, J.W. Tukey, andW. A. Larsen, “Variations of box plots,” The American Statistician, Vol.32, pp. 12-16, 1978.
  16. [16] E. Kalnay, “Atmospheric modeling, data assimilation, and predictability,” Cambridge University Press, 2003.
  17. [17] T. Shibayama, H. Takagi, N. Hnu, and Y. Aoki, “Field survey of storm surge disaster due to cyclone Nargis in Myanmar,” J. Japan Soc. Civil Engineers B2 (Coastal Engineering), Vol.65, pp. 1376-1380, 2009 (in Japanese).

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 09, 2024