Skill Evaluation of Extended-Range Forecasts of Rainfall and Temperature over the Meteorological Subdivisions of India

Susmitha Joseph Indian Institute of Tropical Meteorology, Pune, India

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A. K. Sahai Indian Institute of Tropical Meteorology, and India Meteorological Department, Pune, India

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R. Phani Indian Institute of Tropical Meteorology, Pune, India

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R. Mandal Indian Institute of Tropical Meteorology, Pune, India

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A. Dey Indian Institute of Tropical Meteorology, Pune, India

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R. Chattopadhyay Indian Institute of Tropical Meteorology, Pune, India

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S. Abhilash Cochin University of Science and Technology, Kerala, India

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Abstract

Under the National Monsoon Mission Project initiated by the government of India’s Ministry of Earth Sciences, an indigenous dynamical ensemble prediction system (EPS) has been developed at the Indian Institute of Tropical Meteorology based on the state-of-the-art Climate Forecast System Model version 2 (CFSv2) coupled model, for extended-range (~15–20 days in advance) prediction. The forecasts are generated for the entire year covering the southwest monsoon, the northeast monsoon, and the summer and winter seasons. As the forecast of rainfall is important during the southwest and northeast monsoon seasons, along with that of the temperature during the summer and winter seasons, the present study documents the deterministic as well as probabilistic skill of the EPS in predicting the results in the respective seasons, over various meteorological subdivisions throughout India, on a pentad-lead time scale. The EPS is found to be skillful in predicting rainfall during the southwest and northeast monsoon seasons, as well as temperature during the summer and winter seasons, across different subdivisions of India. In addition, the EPS is noted to be skillful in predicting selected extremes in rainfall and temperature. This affirms the reliability and usefulness of the present EPS from an operational perspective.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-18-0055.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. A. K. Sahai, sahai@tropmet.res.in

Abstract

Under the National Monsoon Mission Project initiated by the government of India’s Ministry of Earth Sciences, an indigenous dynamical ensemble prediction system (EPS) has been developed at the Indian Institute of Tropical Meteorology based on the state-of-the-art Climate Forecast System Model version 2 (CFSv2) coupled model, for extended-range (~15–20 days in advance) prediction. The forecasts are generated for the entire year covering the southwest monsoon, the northeast monsoon, and the summer and winter seasons. As the forecast of rainfall is important during the southwest and northeast monsoon seasons, along with that of the temperature during the summer and winter seasons, the present study documents the deterministic as well as probabilistic skill of the EPS in predicting the results in the respective seasons, over various meteorological subdivisions throughout India, on a pentad-lead time scale. The EPS is found to be skillful in predicting rainfall during the southwest and northeast monsoon seasons, as well as temperature during the summer and winter seasons, across different subdivisions of India. In addition, the EPS is noted to be skillful in predicting selected extremes in rainfall and temperature. This affirms the reliability and usefulness of the present EPS from an operational perspective.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-18-0055.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. A. K. Sahai, sahai@tropmet.res.in

Supplementary Materials

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  • Abhilash, S., A. K. Sahai, N. Borah, R. Chattopadhyay, S. Joseph, S. Sharmila, S. De, and B. N. Goswami, 2014a: Does bias correction in the forecasted SST improve the extended range prediction skill of active-break spells of Indian summer monsoon rainfall? Atmos. Sci. Lett., 15, 114119, https://doi.org/10.1002/asl2.477.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abhilash, S., A. K. Sahai, N. Borah, R. Chattopadhyay, S. Joseph, S. Sharmila, S. De, B. N. Goswami, and A. Kumar, 2014b: Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2. Climate Dyn., 42, 28012815, https://doi.org/10.1007/s00382-013-2045-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abhilash, S., A. K. Sahai, S. Pattnaik, B. N. Goswami, and A. Kumar, 2014c: Extended range prediction of active-break spells of Indian summer monsoon rainfall using an ensemble prediction system in NCEP Climate Forecast System. Int. J. Climatol., 34, 98113, https://doi.org/10.1002/joc.3668.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abhilash, S., and Coauthors, 2015a: Better spread–error relationship in a multimodel ensemble prediction system. Bull. Amer. Meteor. Soc., 96, 12281229, https://doi.org/10.1175/1520-0477-96.8.1221.

    • Search Google Scholar
    • Export Citation
  • Abhilash, S., and Coauthors, 2015b: Improved spread–error relationship and probabilistic prediction from the CFS-based Grand Ensemble Prediction System. J. Appl. Meteor. Climatol., 54, 15691578, https://doi.org/10.1175/JAMC-D-14-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., and J. Juras, 2006: Measuring forecast skill: Is it real skill or is it the varying climatology? Quart. J. Roy. Meteor. Soc., 132, 29052923, https://doi.org/10.1256/qj.06.25.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, J., and H.-L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520533, https://doi.org/10.1175/WAF-D-10-05038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joseph, S., and Coauthors, 2015: North Indian heavy rainfall event during June 2013: Diagnostics and extended range prediction. Climate Dyn., 44, 20492065, https://doi.org/10.1007/s00382-014-2291-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., and F. W. Zwiers, 2003: On the ROC score of probability forecasts. J. Climate, 16, 41454150, https://doi.org/10.1175/1520-0442(2003)016<4145:OTRSOP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kripalani, R. H., and P. Kumar, 2004: Northeast monsoon rainfall variability over south peninsular Indian vis-a-vis the Indian Ocean dipole mode. Int. J. Climatol., 24, 12671282, https://doi.org/10.1002/joc.1071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mohanty, U. C., and Coauthors, 2013: Real-time experimental extended range forecast system for Indian summer monsoon rainfall: A case study for monsoon 2011. Curr. Sci., 104, 856870.

    • Search Google Scholar
    • Export Citation
  • Pan, H. L., and W. S. Wu, 1995: Implementing a mass flux convective parameterization package for the NMC medium-range forecast model. NMC Office Note 409, 43 pp., http://www2.mmm.ucar.edu/wrf/users/phys_refs/CU_PHYS/Old_SAS.pdf.

  • Pattanaik, D. R., 2014: Meteorological subdivisional-level extended range forecast over India during southwest monsoon 2012. Meteor. Atmos. Phys., 124, 167182, https://doi.org/10.1007/s00703-014-0308-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajeevan, M., J. Bhate, J. D. Kale, and B. Lal, 2006: High resolution daily gridded rainfall data for the Indian region: Analysis of break and active monsoon spells. Curr. Sci., 91, 296306.

    • Search Google Scholar
    • Export Citation
  • Rajeevan, M., S. Gadgil, and J. Bhate, 2010: Active and break spells of the Indian summer monsoon. J. Earth Syst. Sci., 119, 229247, https://doi.org/10.1007/s12040-010-0019-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahai, A. K., and Coauthors, 2013: Simulation and extended range prediction of monsoon intraseasonal oscillations in NCEP CFS/GFS version 2 framework. Curr. Sci., 104, 13941408.

    • Search Google Scholar
    • Export Citation
  • Sahai, A. K., S. Abhilash, R. Chattopadhyay, N. Borah, S. Joseph, S. Sharmila, and M. Rajeevan, 2015a: High-resolution operational monsoon forecasts: An objective assessment. Climate Dyn., 44, 31293140, https://doi.org/10.1007/s00382-014-2210-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahai, A. K., R. Chattopadhyay, S. Joseph, R. Mandal, A. Dey, S. Abhilash, R. P. M. Krishna, and N. Borah, 2015b: Real-time performance of a multi-model ensemble based extended range forecast system in predicting the 2014 monsoon season based on NCEP-CFSv2. Curr. Sci., 109, 18021813.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saseendran, S. A., S. V. Singh, L. S. Rathore, and S. Das, 2002: Characterization of weekly cumulative rainfall forecasts over meteorological subdivisions of India using a GCM. Wea. Forecasting, 17, 832844, https://doi.org/10.1175/1520-0434(2002)017<0832:COWCRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, J., and D. S. Gutzler, 1983: Interannual variability and predictability of 500 mb geopotential heights over the Northern Hemisphere. Mon. Wea. Rev., 111, 12731279, https://doi.org/10.1175/1520-0493(1983)111<1273:IVAPOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Srivastava, A. K., M. Rajeevan, and S. R. Kshirsagar, 2008: Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. NCC Research Rep. 8, India Meteorological Department, 15 pp., http://imdpune.gov.in/Clim_Pred_LRF_New/Reports/NCCResearchReports/research_report_8.pdf.

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676.

    • Crossref
    • Export Citation
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