Advertisement

Pure and Applied Geophysics

, Volume 176, Issue 1, pp 421–440 | Cite as

Evaluation of Track and Intensity Prediction of Tropical Cyclones Over North Indian Ocean Using NCUM Global Model

  • Ashish RoutrayEmail author
  • Devajyoti Dutta
  • John P. George
Article
  • 62 Downloads

Abstract

The performance of the National Centre for Medium Range Weather Forecasting-UK Met office (NCUM) global model in prediction of tropical cyclones (TCs) over the North Indian Ocean (NIO) at 25-km resolution is evaluated on the basis of 43 forecasts for 11 TCs. For this purpose, the analyses are carried out based on (1) basins of formation, (2) straight-moving and recurving/looping TCs, and (3) TC intensity at model initialization. The overall performance of NCUM global model has been found reasonably well in predicting TCs over NIO basin as it demonstrates a good skill irrespective of the region of formation, nature of movement, and intensity. The model has reasonably well predicted the tracks of the TCs in maximum number of the IC runs at different stages of the storms. The mean Direct Position Errors (DPEs) (skill with reference to CLIPER model) over the NIO vary from 97 to 248 km (5–57%) for 12–72-h forecast lengths. The NCUM model is found to be more skillful for track prediction of TCs when initialized at the Severe Cyclone Stage rather than at the Cyclonic Stage or lower. Therefore, the DPEs are lesser with higher model ICs run in each TC case. The model is more capable to predict the landfall location than the landfall time of the storms. The results also show that, on average, forecast tracks as predicted by NCUM lie to the right (i.e., model shows eastward bias of the best-track position) in all simulations for all the basins. The analysis of Along-Track errors reveals that the model forecast positions are biased to the south of (behind) the observed positions. It is evident that the NCUM forecasts are slower relative to the actual translation speed of the system for all forecast lengths, and the NCUM model predicts a delayed landfall. It is observed that the NCUM model has less predictability of intensity prediction of intense storms.

Keywords

Tropical cyclones NCUM model along track CLIPER model direct position errors 

Notes

Acknowledgements

The authors acknowledge the IMD for providing the best-tracks and CLIPER model data of the TCs which is used in the present study to validate the model simulations. The authors gratefully acknowledge Dr. M. Mohapatra, Scientist-G, IMD, New Delhi for his immense help in clarifying the doubts throughout the research period. The authors also thank the scientists from UK Met Office. We express our sincere thanks to anonymous reviewers for their valuable comments and suggestions for improvement of the manuscript.

References

  1. Aberson, S. D., & Franklin, J. L. (1999). Impact on hurricane track and intensity forecasts of GPS dropwindsonde observations from the first-season flights of the NOAA Gulfstream-IV jet aircraft. Bulletin of the American Meteorological Society, 80(3), 421–427.CrossRefGoogle Scholar
  2. Aberson, S. D., Majumdar, S. J., Reynolds, C. A., & Etherton, B. J. (2011). An observing system experiment for tropical cyclone targeting techniques using the Global Forecast System. Monthly Weather Review, 139, 895–907.CrossRefGoogle Scholar
  3. Aksoy, A., Aberson, S. D., Vukicevic, T., Sellwood, K. J., Lorsolo, S., & Zhang, X. (2013). Assimilation of high-resolution tropical cyclone observations with an ensemble Kalman filter using NOAA/AOML/HRD’s HEDAS: evaluation of the 2008–11 vortex-scale analyses. Monthly Weather Review, 141(6), 1842–1865.CrossRefGoogle Scholar
  4. Bandyopadhyay, B. K., & Singh, Charan. (2006). Cyclone track forecast by combining persistence, climatology and synoptic method. Mausam, 57, 619–628.Google Scholar
  5. Bender, M. A., Ginis, I., Tuleya, R. E., Thomas, B., & Marchok, T. (2007). The operational GFDL coupled hurricane-ocean Prediction System and a summary of its performance. Monthly Weather Review, 135, 3965–3989.CrossRefGoogle Scholar
  6. Bhaskar Rao, D. V., & Ashok, K. (2001). Simulation of tropical cyclone circulation over the Bay of Bengal using the Arakawa-Schubert cumulus parameterization. Part II: some sensitivity experiments. Pure and Applied Geophysics, 158(5–6), 1017–1046.CrossRefGoogle Scholar
  7. Bhaskar Rao, D. V., HariPrasad, D., & Srinivas, D. (2009). Impact of horizontal resolution and the advantages of the nested domains approach in the prediction of tropical cyclone intensification and movement. Journal of Geophysical Research, 114, D11106.  https://doi.org/10.1029/2008JD011623.CrossRefGoogle Scholar
  8. Buckingham, C., Marchok, T., Ginis, I., Rothstein, L., & Rowe, D. (2010). Short-and medium-range prediction of tropical and transitioning cyclone tracks within the NCEP global ensemble forecasting system. Weather and Forecasting, 25(6), 1736–1754.CrossRefGoogle Scholar
  9. Carr, L. E., & Elsberry, R. L. (2000). Dynamical tropical cyclone track forecast errors. Part II: midlatitude circulation influences. Weather Forecasting, 15, 662–681.CrossRefGoogle Scholar
  10. Chen, T. C., Wang, S. Y., Yen, M. C., & Clark, A. J. (2009). Impact of the intraseasonal variability of the western North Pacific large-scale circulation on tropical cyclone tracks. Weather Forecasting, 24, 646–666.CrossRefGoogle Scholar
  11. Cullen, M. J. P., Davies, T., Mawson, M. H., James, J. A., Coulter, S. C., & Malcolm, A. (1997). An overview of numerical methods for the next generation UK NWP and climate model. Atmosphere-Ocean, 35(sup1), 425–444.CrossRefGoogle Scholar
  12. Davidson, N. E., Xiao, Y., Ma, Y., Weber, H. C., Sun, X., Rikus, L. J., et al. (2014). ACCESS–TC: vortex specification, 4DVAR initialization, verification, and structure diagnostics. Monthly Weather Review, 142(3), 1265–1289.CrossRefGoogle Scholar
  13. Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A., White, A. A., et al. (2005). A new dynamical core for the Met Office’s global and regional modelling of the atmosphere. Quarterly Journal of the Royal Meteorological Society, 131(608), 1759–1782.CrossRefGoogle Scholar
  14. Davis, C., Wang, W., Dudhia, J., & Torn, R. (2010). Does increased horizontal resolution improve hurricane wind forecasts? Weather and Forecasting, 25, 1826–1841.CrossRefGoogle Scholar
  15. Elsberry, R. L., Clune, W. M., Elliott, G., & Harr, P. A. (2009). Evaluation of global model early track and formation predictions during the combined TCS08 and T-PARC field experiment. Asia-Pacific Journal of Atmospheric Sciences, 45, 357–374.Google Scholar
  16. Elsberry, R. L., Jordan, M. S., & Vitart, F. (2010). Predictability of tropical cyclone events on intraseasonal timescales with the ECMWF monthly forecast model. Asia-Pacific Journal of Atmospheric Sciences, 46, 135–153.CrossRefGoogle Scholar
  17. Elsberry, R. L., Lambert, T. D., & Boothe, M. A. (2007). Accuracy of Atlantic and eastern North Pacific tropical cyclone intensity forecast guidance. Weather and Forecasting, 22(4), 747–762.CrossRefGoogle Scholar
  18. Emanuel, K., & Zhang, F. (2016). On the predictability and error sources of tropical cyclone intensity forecasts. Journal of Atmospheric Science, 73, 3739–3747.CrossRefGoogle Scholar
  19. Emanuel, K., & Zhang, F. (2017). The role of inner-core moisture in tropical cyclone predictability and practical forecast skill. Journal of Atmospheric Science, 74, 2315–2324.CrossRefGoogle Scholar
  20. Essery, R., Best, M. and Cox, P. (2001). MOSES 2.2 technical documentation (Vol. 30). Hadley Centre Technical Note. http://jules.jchmr.org/sites/default/files/HCTN_30.pdf
  21. Froude, L. S., Bengtsson, L., & Hodges, K. I. (2007). The prediction of extratropical storm tracks by the ECMWF and NCEP ensemble prediction systems. Monthly Weather Review, 135, 2545–2567.CrossRefGoogle Scholar
  22. Gall, J. S., Ginis, I., Lin, S.-J., Marchok, T., & Chen, J.-H. (2011). Experimental tropical cyclone prediction using the GFDL 25 km resolution global atmospheric model. Weather and Forecasting, 26, 1008–1019.CrossRefGoogle Scholar
  23. Gentry, S. Megan, & Lackmann, Gray M. (2010). Sensitivity of simulated tropical cyclone structure and intensity to horizontal resolution. Monthly Weather Review, 138, 688–704.CrossRefGoogle Scholar
  24. Goerss, J. S., & Jeffries, R. A. (1994). Assimilation of synthetic tropical cyclone observations into the navy operational global atmospheric prediction system. Weather and Forecasting, 9, 557–576.CrossRefGoogle Scholar
  25. Goerss, J. S., Sampson, C. R., & Gross, J. (2004). A history of western North Pacific tropical cyclone track forecast skill. Weather and Forecasting, 19, 633–638.CrossRefGoogle Scholar
  26. Gopalakrishnan, S.G., Liu Q., Marchok T., Sheinin D., Surgi N., Tuleya R., Yablonsky R., & Zhang X. (2012). Hurricane Weather and Research and Forecasting (HWRF) model scientific documentation. L. Bernardet (Ed.), NOAA/ESRL Rep., pp. 96. http://www.dtcenter.org/HurrWRF/users/docs/scientific_documents/HWRFScientificDocumentation_v3.4a.pdf. Accessed 17 June 2018.
  27. Gopalakrishnan, S. G., Goldenberg, S., Quirino, T., Zhang, X., Marks, F., Jr., Yeh, K. S., et al. (2012). Toward improving high-resolution numerical hurricane forecasting: influence of model horizontal grid resolution, initialization, and physics. Weather and Forecasting, 27, 647–666.CrossRefGoogle Scholar
  28. Gopalakrishnan, S. G., Marks, F., Jr., Zhang, J. A., Zhang, X., Bao, J.-W., & Tallapragada, V. (2013). A study of the impacts of vertical diffusion on the structure and intensity of the tropical cyclones using the high-resolution HWRF system. Journal of the Atmospheric Sciences, 70, 524–541.CrossRefGoogle Scholar
  29. Gopalakrishnan, S. G., Zhang, X., Bao, J.-W., Yeh, K.-S., & Atlas, R. (2011). The experimental HWRF system: a study on the influence of horizontal resolution on the structure and intensity changes in tropical cyclones using an idealized framework. Monthly Weather Review, 139, 1762–1784.CrossRefGoogle Scholar
  30. Goswami, P., Himesh, S., & Goud, B. S. (2010). Impact of urbanization on tropical mesoscale events: investigation of three heavy rainfall events. Meteorologische Zeitschrift, 19(4), 385–397.CrossRefGoogle Scholar
  31. Goswami, P., Mandal, A., Upadhyaya, H. C., & Hourdin, F. (2006). Advance forecasting of cyclone track over North Indian Ocean using a global circulation model. Mausam, 57, 111–118.Google Scholar
  32. Goswami, P., & Mohapatra, G. N. (2014). A comparative evaluation of impact of domain size and parameterization scheme on simulation of tropical cyclones in the Bay of Bengal. Journal of Geophysical Research: Atmospheres, 119, 10–22.Google Scholar
  33. Grant, A. L. M., & Brown, A. R. (1999). A similarity hypothesis for shallow-cumulus transports. Quarterly Journal of the Royal Meteorological Society, 125(558), 1913–1936.CrossRefGoogle Scholar
  34. Gregory, D., & Rowntree, P. R. (1990). A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Monthly Weather Review, 118, 1483–1506.CrossRefGoogle Scholar
  35. Hanley, K. E., Plant, R. S., Stein, T. H., Hogan, R. J., Nicol, J. C., Lean, H. W., et al. (2015). Mixing-length controls on high-resolution simulations of convective storms. Quarterly Journal of the Royal Meteorological Society, 141, 272–284.CrossRefGoogle Scholar
  36. Harr, P. A., & Elsberry, R. L. (1991). Tropical cyclone track characteristics as a function of large-scale circulation anomalies. Monthly Weather Review, 119, 1448–1468.CrossRefGoogle Scholar
  37. Harr, P. A., & Elsberry, R. L. (1995). Large scale circulation variability over the tropical western north pacific. Part I: special pattern and tropical cyclone characteristics. Monthly Weather Review, 123, 1225–11246.CrossRefGoogle Scholar
  38. Heming, J. T., & Goerss J. (2010). Track and structure forecasts of tropical cyclones. Global Perspectives on Tropical Cyclones.  https://doi.org/10.1142/9789814293488_0010.Google Scholar
  39. Heming, J. T., 2010. The impact of resolution on Met Office model predictions of tropical cyclone track and intensity. In: AMS 29th Conference on Hurricanes and Tropical Meteorology, Tucson, Arizona. https://ams.confex.com/ams/29Hurricanes/techprogram/programexpanded_608.htm.
  40. Heming, J. T., Chan, J. C. L., & Radford, A. M. (1995). A new scheme for the initialisation of tropical cyclones in the UK Meteorological Office global model. Meteorological Applications, 2, 171–184.CrossRefGoogle Scholar
  41. Heming, J. T., & Greed, G. (2002). The Met Office 2002 global model upgrade and the expected impact on tropical cyclone forecasts. American meteorological society 25th conference on hurricanes and tropical meteorology, San Diego (pp. 180–181). Boston: CA. American Meteorological Society.Google Scholar
  42. India Meteorological Department (2011). Tracks of cyclones and depressions over North Indian Ocean. Technical Note, p. 48. http://www.rmcchennaieatlas.tn.nic.in/Help/TechNote2011.pdf.
  43. Knapp, K. R., & Kruk, M. C. (2010). Quantifying interagency differences in tropical cyclone best-track wind speed estimates. Monthly Weather Review, 138, 1459–1473.CrossRefGoogle Scholar
  44. Kumkar, Yogesh V., Sen, P. N., Chaudhari, Hemankumar S., & Jai-Ho, Oh. (2018). Tropical cyclones over the North Indian Ocean: experiments with the high-resolution global icosahedral grid point model GME. Meteorology and Atmospheric Physics, 130, 23–37.CrossRefGoogle Scholar
  45. Kurihara, Y., Tuleya, R. E., & Bender, M. A. (1998). The GFDL hurricane prediction system and its performance in the 1995 hurricane season. Monthly Weather Review, 126, 1306–1322.CrossRefGoogle Scholar
  46. Levinson, D. H., Diamond, H. J., Knapp, K. R., Kruk, M. C., & Gibney, E. J. (2010). Toward a homogenous global tropical cyclone best-track dataset. Bulletin of the American Meteorological Society, 91, 377–380.CrossRefGoogle Scholar
  47. Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., & Smith, R. N. B. (2000). A new boundary layer mixing scheme. Part I: scheme description and single-column model tests. Monthly Weather Review, 128, 3187–3199.CrossRefGoogle Scholar
  48. Marchok, T.P., 2002, April. How the NCEP tropical cyclone tracker works. In: Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc. P (Vol. 1). https://ams.confex.com/ams/25HURR/techprogram/paper_37628.htm.
  49. Martin, G. M., Bush, M. R., Brown, A. R., Lock, A. P., & Smith, R. N. B. (2000). A new boundary layer mixing scheme. Part II: tests in climate and mesoscale models. Monthly Weather Review, 128(9), 3200–3217.CrossRefGoogle Scholar
  50. Mohanty, U. C., Osuri, Krishna K., Routray, A., Mohapatra, M., & Pattanayak, Sujata. (2010). Simulation of Bay of Bengal tropical cyclones with WRF model: impact of initial and boundary conditions. Marine Geodesy, 33, 294–314.CrossRefGoogle Scholar
  51. Mohapatra, M., Bandyopadhyay, B. K., & Ajit, Tyagi. (2012). Best track parameters of tropical cyclones over the North Indian ocean: a review. Natural Hazards, 63, 1285–1317.CrossRefGoogle Scholar
  52. Mohapatra, M., Bandyopadhyay, B. K., & Nayak, D. P. (2013a). Evaluation of operational tropical cyclone intensity forecasts over north Indian Ocean issued by India meteorological department. Natural Hazards, 68, 433–451.CrossRefGoogle Scholar
  53. Mohapatra, M., Nayak, D. P., Sharma, R. P., & Bandyopadhyay, B. K. (2013b). Evaluation of official tropical cyclone track forecast over north Indian ocean issued by India meteorological department. Journal of Earth System Science, 122, 589–601.CrossRefGoogle Scholar
  54. Neumann, C. J., & Mandal, G. S. (1978). Statistical prediction of tropical storm motion over the Bay of Bengal and Arabian Sea. Indian J. Meteorol. Hydrol. Geophys, 29, 487–500.Google Scholar
  55. Osuri, K. K., Mohanty, U. C., Routray, A., Mohapatra, M., & Niyogi, D. (2013). Real-time track prediction of tropical cyclones over the North Indian Ocean using the ARW model. Journal of Applied Meteorology and Climatology, 52(11), 2476–2492.CrossRefGoogle Scholar
  56. Osuri, K. K., Mohanty, U. C., Routray, A., & Niyogi, D. (2015). Improved prediction of Bay of Bengal tropical cyclones through assimilation of doppler weather radar observations. Monthly Weather Review, 143(11), 4533–4560.CrossRefGoogle Scholar
  57. Osuri, K. K., Nadimpalli, R., Mohanty, U. C., & Niyogi, D. (2017). Prediction of rapid intensification of tropical cyclone phailin over the Bay of Bengal using the HWRF modeling system. Q. J. Roy. Meteorol. Soc., 143, 678–690.CrossRefGoogle Scholar
  58. Pielke, R. A., Sr. (2002). Mesoscale meteorological modeling (p. 676). San Diego: Academic Press.Google Scholar
  59. Pike, A. C., & Neumann, C. J. (1987). The variation of track forecast difficulty among tropical cyclone basins. Weather and Forecasting, 2(3), 237–241.CrossRefGoogle Scholar
  60. Raghavan, S., & Sen Sarma, A. K. (2000). Tropical cyclone impacts in India and neighbourhood. Storms, 1, 339–356.Google Scholar
  61. Rajagopal, E. N., Iyengar, G. R., George, J. P., Gupta, M. D., Mohandas, S., Siddharth, R., Gupta, A., Chourasia, M., Prasad, V. S., Aditi, K. S. and Ashish, A., (2012). Implementation of unified model based analysis-forecast system at NCMRWF. NCMRWF Technical Report No. NMRF/TR/2/2012, pp. 1–46. http://www.ncmrwf.gov.in/UM_OPS_VAR_Report.pdf.
  62. Ramarao, Y. V., Hatwar, H. R., & Agnihotri, G. (2006). Tropical cyclone prediction by numerical models in India meteorological department. Mausam, 57, 47–60.Google Scholar
  63. Regional Specialized Meteorological Centre (RSMC), Cyclone Warning Division, India Meteorological department, India. (2014). Report on cyclonic disturbances over North Indian Ocean during 2013. Retrieved from http://www.rsmcnewdelhi.imd.gov.in/images/pdf/publications/annual-rsmc-report/rsmc-2013.pdf. Accessed 17 June 2018.
  64. Routray, A., Kar, S. C., Mali, P., & Sowjanya, K. (2014). Simulation of monsoon depressions using WRF-VAR: impact of different background error statistics and lateral boundary conditions. Monthly Weather Review, 142(10), 3586–3613.CrossRefGoogle Scholar
  65. Routray, A., Mohanty, U. C., Osuri, K. K., Kar, S. C., & Niyogi, D. (2016). Impact of satellite radiance data on simulations of Bay of Bengal tropical cyclones using the WRF-3DVAR modeling system. IEEE Transactions on Geoscience and Remote Sensing, 54(4), 2285–2303.CrossRefGoogle Scholar
  66. Routray, A., Singh, V., George J. P., Mohandas, S., and Rajagopal, E. N. (2017). Simulation of tropical cyclones over Bay of Bengal with NCMRWF regional unified model. Pure and Applied Geophysics, 174, 1101–1119.CrossRefGoogle Scholar
  67. Ryerson, W. R. (2006). Evaluation of the AFWA WRF 4-km moving nest model predictions for Western North Pacific tropical cyclones (doctoral dissertation. Naval Postgraduate School): Monterey California.Google Scholar
  68. Tallapragada, V., Kieu, C., Kwon, Y., Trahan, S., Liu, Q.-F., Zhang, Z., et al. (2014). Evaluation of storm structure from the operational HWRF model during 2012 implementation. Monthly Weather Review, 142, 4308–4325.CrossRefGoogle Scholar
  69. Vitart, F., Leroy, A., & Wheeler, M. C. (2010). A comparison of dynamical and statistical predictions of weekly tropical cyclone activity in the Southern Hemisphere. Monthly Weather Review, 138, 3671–3682.CrossRefGoogle Scholar
  70. Waldron, K. M., Peagle, J., & Horel, J. D. (1996). Sensitivity of a spectrally filtered and nudged limited-area model to outer model options. Monthly Weather Review, 124, 529–547.CrossRefGoogle Scholar
  71. Wilson, D. R., & Ballard, S. P. (1999). A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Quarterly Journal of the Royal Meteorological Society, 125, 1607–1636.CrossRefGoogle Scholar
  72. World Meteorological Organization (WMO) 2009. Standard format for verification of TC forecast. TCM-6, pp 83. https://www.wmo.int/pages/prog/www/tcp/documents/TCM6-FinalReport.pdf. Accessed 17 June 2018.
  73. Wu, L., Wang, B., & Geng, S. (2005). Growing typhoon influence on East Asia. Geophysical Research Letters, 32, L18703.  https://doi.org/10.1029/2005GL022937.Google Scholar
  74. Xiao, Q., Zou, X., & Wang, B. (2000). Initialization and simulation of a landfalling hurricane using a variational bogus data assimilation scheme. Monthly Weather Review, 128, 2252–2269.CrossRefGoogle Scholar
  75. Zhang, W., Leung, Yee, & Chan, Johnny C. L. (2013). The analysis of tropical cyclone tracks in the Western North Pacific through data mining. Part I: tropical cyclone recurvature. Journal of Applied Meteorology and Climatology, 52, 1394–1416.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ashish Routray
    • 1
    Email author
  • Devajyoti Dutta
    • 1
  • John P. George
    • 1
  1. 1.National Centre for Medium Range Weather Forecasting (NCMRWF)Ministry of Earth Sciences (MoES)NoidaIndia

Personalised recommendations