Top > JACIII > Rain-Area Identification Using TRMM/TMI Data by Data Mining Ap ...

single-jc.php

JACIII Vol.12 No.3 pp. 243-248
doi: 10.20965/jaciii.2008.p0243
(2008)

Paper:

Views over last 60 days: 370

Rain-Area Identification Using TRMM/TMI Data by Data Mining Approach

Shan-Tai Chen*, Chien-Chen Wu*, Wann-Jin Chen**,
and Jen-Chi Hu**

*Dept. of Computer Science, Chung Cheng Institute of Technology, National Defense University, No.190, Sanyuan 1

st St., Tashi, Taoyuan, Taiwan

**School of Defense Science, Chung Cheng Institute of Technology, National Defense University

Received:
April 22, 2007
Accepted:
September 20, 2007
Published:
May 20, 2008
Creative Commons License
Keywords:
data mining, classification, rain-area identification, TRMM, microwave
Abstract
Rain-area identification distinguishes between rainy and non-rainy areas, which is the first step in some critical real-world problems, such as rain intensity identification and rain-rate estimation. We develop a data mining approach for oceanic rain-area identification during typhoon season, using microwave data from the Tropical Rainfall Measuring Mission (TRMM) satellite. Three schemes tailored for the problem are developed, namely (1) association rule analysis for uncovering the set of potential attributes relevant to the problem, (2) three-phase outlier removal for cleaning data and (3) the neural committee classifier (NCC) for achieving more accurate results. We created classification models from 1998-2004 TRMM Microwave Imager (TRMM-TMI) satellite data and used Automatic Rainfall and Meteorological Telemetry System (ARMTS) rain gauge data measurements to evaluate the model. Experimental results show that our approach achieves high accuracy for the rain-area identification problem. The classification accuracy of our approach, 96%, outperforms the 78.6%, 77.3%, 83.3% obtained by the scattering index, threshold check, and rain flag methods, respectively.
Cite this article as:
S. Chen, C. Wu, W. Chen, and J. Hu, “Rain-Area Identification Using TRMM/TMI Data by Data Mining Approach,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.3, pp. 243-248, 2008.
Data files:
References
  1. [1] K. S. Cheng and S. F. Shih, “Rainfall Area Identification Using GOES Satellite Data,” Journal of Irrigation and Drainage Engineering, Vol.118, No.1, pp. 179-190, January/February, 1992.
  2. [2] M. H. Gonzalez and I. Velasco, “Rainfall area identification using satellite data,” Climate Research, Vol.5, pp. 259-267, 1995.
  3. [3] C. Kummerow and L. Giglio, “A method combining passive microwave and infrared rainfall observations,” Journal of Atmospheric and Oceanic Technology, 12, pp. 33-45, 1995.
  4. [4] G. J. Huffman, R. F. Adler, D. T. Bolvin, G. Gu, E. J. Nelkin, K. P. Bowman, Y. Hong, E. F. Stocker, and D. B. Wolff, “The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales,” Journal of Hydrometeorology, Vol.8, Issue 1, pp. 38-55, February, 2007.
  5. [5] V. L. Sanderson, C. Kidd, and G. R. McGregor, “A Comparison of TRMM Microwave Techniques for Detecting the Diurnal Rainfall Cycle,” J. Hydrometeor., 7, pp. 687-704, 2006.
  6. [6] T. T. Wilheit, A. T. C. Chang, and L. S. Chiu, “Retrieval of monthly rainfall indices from microwave radiometeric measurements using probability distribution functions,” Journal of Atmospheric and Oceanic Technology, 8, pp. 118-136, 1991.
  7. [7] F. S. Marzano, “Recent developments on precipitation retrieval by spaceborne microwave radiometry,” Toward new Sensors and New Retrievals in Radiometry, Report following the COST-712 Project 2+3 Workshop in Amsterdam, version 1, pp. 33-53, 18-19, January, 2000.
  8. [8] J. C. Hu, W. J. Chen, G.-R. Liu, M. H. Chang, and H. P. Gang, “Quantitative rain rate over ocean using microwave observations during the typhoon season,” Weather Forecasting and Analyzing, Vol.185, No.380, pp. 21-30, 2005.
  9. [9] W. J. Chen, J. C. Hu, G. R. Liu, and M. H. Chang, “Quantitative precipitation over ocean using TMI microwave observations during the typhoon season,” Journal of Atmospheric Science, Vol.34, No.1, pp. 67-88, 2006.
  10. [10] N. C. Grody, “Classification of Snow Cover and Precipitation Using the Special Sensor Microwave Imager,” J. Geophys. Res., Vol.96, pp. 7423-7435, 1991.
  11. [11] W. J. Chen and C. C. Li, “Oceanic Rain Rate Retrievals Using TRMM Microwave Imager Multi-Channel Brightness Temperatures During the 1998 SCSMEX,” Terrestrial, Atmospheric and Oceanic Sciences, Vol.11, pp. 765-788, 2000.
  12. [12] M. A. Goodberlet, C. T. Swift, and J. C. Wilkerson, “Remote Sensing of Ocean Surface Winds with the Special Sensor Microwave/Imager,” J. Geophys. Res., Vol.94, C10, pp. 14547-14555, 1989.
  13. [13] C. Kummerow, Y. Hong, W. S. Olson, S. Yang, R. F. Adler, J. Mc-Collum, R. Ferraro, G. Petty, D. B. Shin, and T. T. Wilheit, “The evaluation of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors,” J. Appl. Meteor., Vol.40, pp. 1801-1820, 2001.
  14. [14] C. C. Li, “Retrievals and application of rainfall rate over ocean using TMI microwave observation,” The thesis of Ph. D. of National Defense University, 2002.
  15. [15] J. C. Principe, N. R. Euliano, and W. C. Lefebvre, “Neural and Adaptive System : Fundamentals through Simulations,” John Wiley & Sons, New York, 2000.
  16. [16] T. H. Martin, B. D. Howard, and B. Mark, “Neural Network Design,” Thomson Learning, Boston, 1996.
  17. [17] C. Kummerow, W. Barnes, T. Kozu, J. Shiue, and J. Simpson, “The Tropical Rainfall Measuring Mission (TRMM) Sensor Package,” Journal of Atmospheric and Oceanic Technology, Vol.15, No.3, pp. 809-817, 1998.
  18. [18] C. Kummerow, “Beamfilling Error in Passive Microwave Rainfall Retrievals,” J. Appl. Meteor., Vol.37, pp. 356-370, 1998.
  19. [19] W. J. Chen, J. C. Hu, and M. D. Tsai, “Over-ocean rainfall retrieval from TRMM/TMI data during the Typhoon season,” 14th Conf. on Satellite Meteorology and Oceanography, Atlanta, U.S.A, Jan.28-Feb.02, 2006.

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 Apr. 05, 2024