JACIII Vol.12 No.6 pp. 516-522
doi: 10.20965/jaciii.2008.p0516


A Data Mining Approach to Rainfall Intensity Classification Using TRMM/TMI Data

Shan-Tai Chen*, Shung-Lin Dou*, and Wann-Jin Chen**

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

st St., Tashi, Taoyuan, Taiwan, R.O.C.

**Dept. of Environment Information Engineering, Chung Cheng Institute of Technology, National Defense University

June 7, 2008
August 20, 2008
November 20, 2008
data mining, classification, rainfall intensity, TRMM, microwave

The systematic approach we propose for classifying oceanic rainfall intensity during the typhoon season consists of two major steps – 1) identifying the rain areas and 2) classifying rainfall intensity into normal and heavy for these areas. The heterogeneous hierarchical classifier (HHC), an ensemble model we developed for accurately identifying heavy rainfall events, consists of a set of base classifiers. The base classifiers are independently constructed through heterogeneous data mining approaches such as artificial neural networks, decision trees, and self-organizing maps. The meteorological satellite Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data from 2000 to 2005 are used to create the classification models. TRMM precipitation radar (PR) data and rain gauge data from Automatic Rainfall and Meteorological Telemetry System (ARMTS) measurement are used as ground truth data to evaluate models. Two thirds of the dataset is used for model training and one third for testing. Experimental results show that the proposed model classifies rainfall intensity highly accurately and outperforms previously published methods.

Cite this article as:
Shan-Tai Chen, Shung-Lin Dou, and Wann-Jin Chen, “A Data Mining Approach to Rainfall Intensity Classification Using TRMM/TMI Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.12, No.6, pp. 516-522, 2008.
Data files:

    [1] M. Alexiuk, N. Pizzi, and W. Pedrycz, “Classification of volumetric storm cell patterns,” In Proc. of the 1999 IEEE Canadian Conf. on Electrical and Computer Engineering, pp. 1081-1085, 1999.
    [2] C. Martinez, J. Campins, A. Jansa, and A.Genoves, “Heavy rain events in the Western Mediterranean: an atmospheric pattern classification,” Advances in Sci. and Res., Vol.2, pp. 61-64, 2008.
    [3] K. Nishiyama, S. Endo, K. Jinno, C. B. Uvo, J. Olsson, and R. Berndtsson, “Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a self-organizing map,” Atmospheric research, Vol.83, pp. 185-120, 2007.
    [4] B. G. Lee, R. T. Chin, and D. W. Martin, “Automated Rain-Rate Classification of Satellite Images Using Statistical Pattern Recognition,” IEEE Transactions On Geoscience And Remote Sensing, Vol.GE-23, No.3, pp. 315-324, May, 1985.
    [5] R. Parvathi, B. Manikiam, V. Jayaraman, and M. G. Chandrasekhar, “Rain-rate classification of INSAT-VHRR images through statistical methods,” Advances in Space Research, Vol.13, Issue 5, pp. 143-148, May, 1993.
    [6] 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.
    [7] 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.
    [8] 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.
    [9] N. C. Grody, “Classification of Snow Cover and Precipitation Using the Special Sensor Microwave Imager,” J. Geophys. Res., Vol.96, pp. 7423-7435, 1991.
    [10] 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.
    [11] 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.
    [12] S.-T. Chen, C.-C. Wu, W.-J. Chen, and J.-C. Hu, “Rain-Area Identification Using TRMM/TMI Data by Data Mining Approach,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.3, pp. 243-248, 2008.
    [13] C. Kummerow, Y. Hong, W. S. Olson, S. Yang, R. F. Adler, J. McCollum, 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] 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.
    [15] 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.
    [16] 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.
    [17] C. Kummerow, “Beamfilling Error in Passive Microwave Rainfall Retrievals,“ J. Appl. Meteor., Vol.37, pp. 356-370, 1998.

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