Fuzzy logic based convective cloud detection from the Kalpana data

  • Authors

    • B Ravi Kumar
    • B Anuradha
    https://doi.org/10.14419/ijet.v7i3.29.19183
  • AVHRR Convective Clouds, Fuzzy Logic, Spectral Data.

  • Abstract

    The cloud classification and analysis of the distinctive convective clouds from the satellite data have been mobilized using many traditional classification methods in atmospheric models for foreseen the natural hazards. In this paper one promising method makes use of fuzzy logic to perceive the convective clouds with strong triangular and trapezoidal membership functions. Convective clouds extracted from satellite images are compared with INSAT Multispectral Rainfall Algorithm (IMSRA).The development of the fuzzy logic rule based expert system with Kalpana satellite spectral data consist of advanced very high resolution (AVHRR) channels, which include Water vapour, infrared windows. In this proposed method shows that the fuzzy logic method offers greater correct results than conventional algorithms to identify the convective clouds.

     

     

     

  • References

    1. [1] G. S. Pankiewicz, “Pattern Recognition technique for identification of clouds and cloud systems, Meteorol. Appl., vol.2, pp.257-271, Sep.1995.

      [2] Goodman, A. H., and A. Henderson-Sellers, (1988). Cloud detection and analysis: A review of recent progress, Atmos. Res., Vol. 21, pp. 203-228.

      [3] C.Donald Ahrens, “Meteorology today-in introduction to weather, climate and the environment†Ninth Edition, 2009.

      [4] L. Jun, W. Paul, M. Z. Yang, A. F. Richard, Ackerman S. A., "High-Spatial-Resolution Surface and Cloud-Type Classification from MODIS Multispectral Band Measurements,†Journal of Meteorology and Climatology., Vol.42, pp.204-226, 2003.

      [5] R.L Bankert and R.H.wade,“Optimization of an instance based goes cloud classification algorithm,†Journal of Applied Meteorology and Climatology, Vol.46 ,no.1, pp.36-49.Jan,2007.

      [6] Y. Hong, K. L. Hsu, Sorooshian S., X. Gao, “Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system,†Journal of Meteorology and Climatology., Vol.43, 1834-1853, 2004.

      [7] M. Singh, M. Glennen, “Automated ground-based cloud recognition, Pattern Anal. Applic. Vol. 8, 2005. http://dx.doi.org/10.1007/s10044-005-0007-5.

      [8] Pei-yu Chen, Raghavan Srinivasan,Gunar Fedosejevs, “An automated clouds detection method for daily NOAA 16 advanced very high resolution radiometer data over Texas and Mexicoâ€, Journal of Geophysical research, VOL. 108, 2003.

      [9] A. Kandel,G.Langholz,Fuzzy control Systems, CRC Press, Inc., 1994.

      [10] Roca R, Viollier M, Picon L, desbois M, “A Multisatellite analysis of deep convection and its moist environment over the Indian Ocean during the winter monsoonâ€, J.Geophys.Res.107(D19),2002.

  • Downloads

  • How to Cite

    Ravi Kumar, B., & Anuradha, B. (2018). Fuzzy logic based convective cloud detection from the Kalpana data. International Journal of Engineering & Technology, 7(3.29), 316-318. https://doi.org/10.14419/ijet.v7i3.29.19183

    Received date: 2018-09-07

    Accepted date: 2018-09-07