A Study of South-West Monsoon Over Indian Sub-Continent using Satellite Derived Precipitation Estimates

  • Authors

    • P. Giri Prasad
    • S. Varadarajan
    2018-11-26
    https://doi.org/10.14419/ijet.v7i4.29.21643
  • .
  • Abstract

    In the present study, an investigation has been made over Indian Sub-Continent during South-West Monsoon for the years 2015-2016. The results show that no precipitation products are close to the gridded actual rainfall. But good correlation coefficients (CC) exist between the satellites derived precipitation product and actual rainfall. In this paper, multisatellite high-resolution precipitation products,namely Climate Prediction Center Morphing (CMORPH) version 1.0, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V7 product are compared with India Meteorological Department (IMD) gridded rainguage data.

    From the results, it is observed that south west monsoon during 2016 produces more rainfall compared to monsoon season of 2015. Five different regions with different climate zones are selected shows the variability of climate over Indian Sub-Continent. For the selected regions, monthly average rainfall(in mm) ,Correlation Coefficient(CC) and Root Mean Square Error (RMSE)  are evaluated for satellite derived precipitation products and IMD gridded rain guage data.

  • References

    1. [1] SatyaPrakash,V. Sathiyamoorthy, C. Mahesh, and R. M. Gairola, 2014: An evaluation of high-resolution multisatellite rainfall products over the Indian monsoon region. Int. J. Remote Sens., 35, 3018–3035, doi:10.1080/01431161.2014.894661.

      [2] Joyce, R., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH:A method that produces global precipitation estimates frompassive microwave and infrared data at high spatial and temporalresolution. J. Hydrometeor., 5, 487–503, doi:10.1175/1525-7541(2004)005,0487:CAMTPG.2.0.CO;2.

      [3] Prakash, S., C. Mahesh, R. M. Gairola, and P. K. Pal, 2010: Estimationof Indian summer monsoon rainfall using Kalpana-1VHRR data and its validation using rain gauge andGPCP data. Meteor. Atmos. Phys., 110, 45–57, doi:10.1007/s00703-010-0106-8.

      [4] Mishra, A., R. M. Gairola, A. K. Varma, and V. K. Agarwal, 2009:Study of intense rainfall events over India using Kalpana-IRand TRMM precipitation radar observations. Int. J. Curr. Sci.,97, 689–695.

      [5] Gairola, R.M., Krishnamurti, T.N., 1992. Rain rates based on SSM/I, OLR and rain gauge data sets. Meteorol. Atmos. Phys. 50, 165–174.

      [6] Todd, M.C., Barrett, E.C., Beaumont, M.J., Green, J., 1995. Satellite identification of rain days over the upper Nile river basin using an optimum infrared rain/no rain threshold temperature model. J. Appl. Meteorol. 34, 2600–2611.

      [7] Arkin, P. A., Krishna Rao, A. V. R. and Kelkar, R. R.,1989,Large scale precipitation and outgoing longwave radiation from INSAT-1B during the 1986 South West Monsoon season, J. Climate, 2, 619–128.

      [8] Aires, F., C. Prigent, W. B. Rossow, and M. Rothstein, 2001: A newneural network approach including first guess for retrieval ofatmospheric water vapour, cloud liquid water path, surface temperaturesandemissivities over land from satellite microwaveobservations. J. Geophys. Res., 106 (D14), 14 887–14 907.

      [9] Hsu, K., X. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neuralnetworks. J. Appl. Meteor., 36, 1176–1190.

      [10] Tsintikidis, D., J. L. Haferman, N. Anagnostou, W. F. Krajewski, andT. F. Smith, 1997: A neural network approach to estimating rainfallfromspaceborne microwave data. IEEE Trans. Geosci. RemoteSens., 35, 1079–1092.

      [11] Bellerby, T., M. Todd, D. Kniveton, and C. Kidd, 2000: Rainfall estimation from a combination of TRMM precipitation radar andGOES multispectral satellite imagery through the use of an artificialneural network. J. Appl. Meteor., 39, 2115–2128.

      [12] Pai, D. S., et al. (2014). Development of a very high spatial resolution(0.250 9 0.250) Long period (1901–2010) daily gridded rainfalldata set over the Indian region. Mausam, 65(1), 1–18.

      [13] Huffman, G. J., D. T. Bolvin, E. J. Nelkin, D. B. Wolff, R. F. Adler, G. Gu, Y. Hong, K. P. Bowman, and E. F. Stocker. 2007. “The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales.†Journal of Hydrometeorology 8 (1): 38–55. doi:10.1175/JHM560.1.

      [14] Huffman, G. J., R. F. Adler, D. T. Bolvin, and E. J. Nelkin. 2010. “The TRMM Multi-Satellite Precipitation Analysis (TMPA).†In Satellite Rainfall Applications for Surface Hydrology, edited by F. Hossain and M. Gebremichael, 3–22. Dordrecht: Springer Verlag. ISBN: 978-90-481-2914-0.

      [15] Prakash, S., C. Mahesh, and R. M. Gairola. 2013. “Comparison of TRMM Multi-Satellite Precipitation Analysis (TMPA)-3B43 Version 6 and 7 Products with Rain Gauge Data from Ocean Buoys.†Remote Sensing Letters 4 (7): 677–685. doi:10.1080/2150704X.2013.783248.

  • Downloads

  • How to Cite

    Giri Prasad, P., & Varadarajan, S. (2018). A Study of South-West Monsoon Over Indian Sub-Continent using Satellite Derived Precipitation Estimates. International Journal of Engineering & Technology, 7(3.29), 702-708. https://doi.org/10.14419/ijet.v7i4.29.21643

    Received date: 2018-11-26

    Accepted date: 2018-11-26

    Published date: 2018-11-26