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Simulation of Tropical Cyclones over Bay of Bengal with NCMRWF Regional Unified Model

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This study delineates the relative performance of the 12-km resolution NCMRWF regional Unified Model (NCUM-R) over the operational global NCUM (NCUM-G) model. Forecasts of four Bay of Bengal (BoB) landfalling tropical cyclones (TCs) using several different initial conditions (ICs) are used to compare the performance of two models. The position and intensity errors of the TCs are estimated with respect to the India Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) best-track datasets and an inter-comparison study is also carried out between IMD and JTWC. The overall results suggest that the NCUM-R simulates the position and intensity of TCs more accurately compared to the NCUM-G. A majority of the TC tracks in the NCUM-G diverge more from the IMD track when compared to NCUM-R simulated tracks. It is also clearly noticed that both the models are more skillful in track prediction when initialized at intensity stages greater than “cyclone” category. However, the mean position errors at different forecast hours and landfall errors of TCs are reduced by approximately 31 and 47% in the NCUM-R simulations compared to NCUM-G simulations, respectively. The mean gain in skill of the NCUM-R in cross track (CT) and along track (AT) error is around 29 and 24% over NCUM-G, respectively. The intensity errors are less in the NCUM-R simulations. The mean rainfall skill scores are considerably improved in the NCUM-R simulations in day-1 and day-2 as compared to the NCUM-G simulations. It is noticed that the mean position errors of the TCs are approximately 8% lower when compared against the JTWC tracks than the IMD tracks. However, the intensity errors are higher against the JTWC than that of IMD most likely due to the averaging period of the wind speed.

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References

  • Aberson, S. D. (2011). The impact of dropwindsonde data from the THRORPEX-pacific Area Regional Campaign and the NOAA Hurricane field program on tropical cyclone forecasts in the Global Forecasting System. Monthly Weather Review, 139, 2689–2703.

    Article  Google Scholar 

  • Ashrit, R., et al. (2013). Performance of global ensemble forecast system (GEFS) during monsoon 2013, NCMRWF Technical Report No. NMRF/RR/1/2013, p. 22.

  • Bhaskar Rao, D. V., Hari Prasad, D., Srinivas, D., & Anjaneyulu, Y. (2010). Role of vertical resolution in numerical models towards the intensification, Structure and track of tropical cyclones. Marine Geodesy, 33, 338–355.

    Article  Google Scholar 

  • Chou, K. H., Wu, C. C., Lin, P. H., Aberson, S. D., Weissmann, M., Harnisch, F., et al. (2011). The impact of dropwindsonde observations on typhoon track forecasts in DOTSTAR and T-PARC. Monthly Weather Review, 139, 1728–1743.

    Article  Google Scholar 

  • Chu, J.H., Sampson, C.R., Levin, A.S., Fukada, E. (2002). The Joint typhoon warning center tropical cyclone best-tracks 1945–2000, US Naval Research Laboratory Rep. NRL/MR/7540-02-16, p. 22.

  • Cullen, M. J. P., Davies, T., Mawson, M. H. J., James, A., Coulter, S. C., Malcolm, A. (1997). An overview of numerical methods for the next generation U.K. NWP and climate model. Atmosphere-Ocean, 35, 425–444. doi:10.1080/07055900.1997.9687359.

    Article  Google Scholar 

  • 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, 1265–1289.

    Article  Google Scholar 

  • 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, 1759–1782.

    Article  Google Scholar 

  • Davis, C. A., et al. (2008). Prediction of landfalling hurricanes with the advanced hurricane WRF model. Monthly Weather Review, 136, 1990–2005.

    Article  Google Scholar 

  • Essery, R., Best, M., Cox, P. (2001). MOSES 2.2 Tech. Doc. Hadley Centre Tech. Rep. 30, Met Office Hadley Centre, p. 30.

  • Fiorino, M., Goerss, J. M., Jensen, J. J., & Harrison, E. J., Jr. (1993). An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific. Weather and Forecasting, 8, 3–24.

    Article  Google Scholar 

  • Froude, L. S. R., Bengtsson, L., & Hodges, K. (2007). The prediction of extratropical storm tracks by the ECMWF and NCEP ensemble prediction systems. Monthly Weather Review, 135, 2545–2567.

    Article  Google Scholar 

  • Gopalakrishnan, S. G., Goldenberg, S., Quirino, T., Zhang, X., Marks, F., 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.

    Article  Google Scholar 

  • Gopalakrishnan, S.G., Surgi, N., Tuleya, R., Janjic, Z. (2006). NCEP’s two-way-interactive-moving-nest NMM-WRF modeling system for hurricane forecasting. Preprints, 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, Am. Meteor. Soc., 7A.3. https://ams.confex.com/ams/pdfpapers/107899.pdf.

  • Grant, A. L. M. (2001). Cloud base mass fluxes in the cumulus capped boundary layer. Quarterly Journal of the Royal Meteorological Society, 127, 407–421.

    Article  Google Scholar 

  • Grant, A. L. M. & Brown, A. R. (1999). A similarity hypothesis for shallow-cumulus transports. Quarterly Journal of the Royal Meteorological Society, 125, 1913–1936.

    Article  Google Scholar 

  • Gray, W. M. (1968). Global view of the origin of the tropical disturbances and storm. Monthly Weather Review, 96, 669–700. doi:10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Rao, D. V. B., Hari Prasad, 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 Geophysical Research, 114, D11106. doi:10.1029/2008JD011623.

    Article  Google Scholar 

  • India Meteorological Department. (2008). Tracks of cyclones and depressions in the Bay of Bengal and Arabian Sea 1891–2007, Electronic version, June 2008.

  • Jankov, I., Gallus, W. A., Jr., Segal, M., Shaw, B., & Koch, S. E. (2005). The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Weather and Forecasting, 20, 1048–1060.

    Article  Google Scholar 

  • Knaff, J. A., & Zehr, R. M. (2007). Reexamination of tropical cyclone wind–pressure relationships. Weather and Forecasting, 22, 71–88.

    Article  Google Scholar 

  • Knapp, K. R., & Kruk, M. C. (2010). Quantifying interagency differences in tropical cyclone best-track wind speed estimates. Monthly Weather Review, 138, 1459–1473.

    Article  Google Scholar 

  • Lean, H. W., Clerk, Peter A., Dixon, Mark, Roberts, Nigel M., Fitch, Anna, Forbes, Richard, et al. (2008). Characteristics of high resolution versions of the Met office Unified Model for forecasting convection over the United Kingdom. Monthly Weather Review, 136, 3408–3424.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., & Smith, R. N. B. (2000). A new boundary-layer mixing scheme—1. Scheme description and single-column model tests. Monthly Weather Review, 128, 3187–3199.

    Article  Google Scholar 

  • Marchok, T.P. (2002). How the NCEP tropical cyclone tracker works. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Am. Meteor. Soc., P1.12. https://ams.confex.com/ams/pdfpapers/37628.pdf.

  • 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, 3200–3217.

    Article  Google Scholar 

  • Mitra, A. K., Bohra, A. K., Rajeevan, M. N., & Krishnamurti, T. N. (2009). Daily Indian precipitation analyses formed from a merge of rain-gauge with TRMM TMPA satellite derived rainfall estimates. Journal of the Meteorological Society of Japan, 87A, 265–279.

    Article  Google Scholar 

  • Mohanty, U. C., Osuri, K. K., & Pattanayak, S. (2013). A study on high resolution mesoscale modeling systems for simulation of tropical cyclones over the Bay of Bengal. Mausam, 64, 117–134.

    Google Scholar 

  • Mohapatra, M., Bandyopadhyay, B. K., & Ajit, T. (2012). Best-track parameters of tropical cyclones over the North Indian Ocean: A review. Natural Hazards, 63, 1285–1317.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Nakazawa, T., & Hoshino, S. (2009). Intercomparison of Dvorak parameters in the tropical cyclone datasets over the western North Pacific. Scientific Online Letters on the Atmosphere, 5, 33–36.

    Google Scholar 

  • Neumann, C. J., & Mandal, G. S. (1978). Statistical prediction of tropical storm motion over the Bay of Bengal and Arabian Sea. Indian Journal of Meteorology, Hydrology and Geophysics, 29, 487–500.

    Google Scholar 

  • O’Hara, J.P., Webster, S. (2012). Assessing the sensitivity to horizontal resolution of Unified Model simulations of Hurricane Katrina, American Geophysical Union, Fall Meeting, BC:2012AGUFM.A41J01130.

  • Orr, A., Phillips, Tony, Webster, Stuart, Elvidge, Andy, Weeks, Mark, Hosking, Scott, et al. (2014). Met Office Unified Model high-resolution simulations of a strong wind event in Antarctica. Quarterly Journal of the Royal Meteorological Society, 140, 2287–2297.

    Article  Google Scholar 

  • Osuri, K. K., Mohanty, U. C., Routray, A., Makarand, A. K., & Mohapatra, M. (2012). Sensitivity of physical parameterization schemes of WRF model for the simulation of Indian seas tropical cyclones. Natural Hazards, 63, 1337–1359.

    Article  Google Scholar 

  • Osuri, K. K., Mohanty, U. C., Routray, A., Mohapatra, M., & Niyogi, Dev. (2013). Real-Time track prediction of tropical cyclones over the north indian ocean using the ARW model. Journal of Applied Meteorology and Climatology, 52, 2476–2492.

    Article  Google Scholar 

  • 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, 4533–4560.

    Article  Google Scholar 

  • Pattanayak, S., Mohanty, U. C., & Gopalakrishnan, S. G. (2012). Simulation of very severe cyclone Mala over Bay of Bengal with HWRF modeling system. Natural Hazards, 63, 1413–1437.

    Article  Google Scholar 

  • Pike, A. C., & Neumann, C. J. (1987). The variation of track forecast difficulty among tropical cyclone basins. Weather and Forecasting, 2, 237–241.

    Article  Google Scholar 

  • Prasad, V.S., Mohandas, S., Das Gupta, M., Rajagopal, E.N., Dutta, S.K. (2011). Implementation of upgraded global forecasting systems (T382L64 and T574L64) at NCMRWF, NCMRWF Technical Report No. NMRF/TR/5/2011, p. 72.

  • Rajagopal, E.N. (2012). Implementation of unified model based analysis-forecast system at NCMRWF, NCMRWF Technical Report No. NMRF/TR/2/2012, p. 45.

  • Routray, A., Kar, S. C., & Mali, P. (2014). Simulation of monsoon depressions over Indian region using WRF-VAR analysis system: Impact of background error statistics. Monthly Weather Review, 142, 3586–3613.

    Article  Google Scholar 

  • Routray, A., Mohanty, U. C., Osuri, K. K., Kar, S. C., & Niyogi, D. (2016). Impact of Radiance on analysis and simulation of tropical cyclones using WRF-3DVAR modeling system. IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/TGRS.2015.2498971.

    Google Scholar 

  • Routray, A., Mohanty, U. C., Rizvi, S. R. H., Niyogi, D., Osuri, K. K., & Pradhan, D. (2011). Impact of doppler weather radar data on numerical forecast of Indian monsoon depressions. Quarterly Journal of the Royal Meteorological Society, 136, 1836–1850.

    Article  Google Scholar 

  • Sowjanya, K., Sarat, C. K., Routray, A., & Mali, P. (2013). Impact of SSM/I retrieval data on the systematic bias of analyses and forecasts of the Indian summer monsoon using WRF assimilation system. International Journal of Remote Sensing, 34, 631–654.

    Article  Google Scholar 

  • Srinivas, D., & Rao, Dodla Venkata Bhaskar. (2014). Implications of vortex initialization and model spin-up in tropical cyclone prediction using advanced research weather research and forecasting model. Natural Hazards, 73, 1043–1062.

    Article  Google Scholar 

  • Tallapragada, V., Kieu, C., Kwon, Y., Trahan, S., Liu, Q., Zhang, Z., et al. (2014). Evaluation of storm structure from the operational HWRF during 2012 implementation. Monthly Weather Review, 142, 4308–4325.

    Article  Google Scholar 

  • Tallapragada, V., Surgi, N., Liu, Q., Kwon, Y., Tuleya, R., O’Connor, W. (2008). Performance of the advanced operational HWRF modeling system during pre-implementation testing and in real-time 2007 hurricane season. Recorded Presentation, 28th Conf. on Hurricanes and Tropical Meteorology, Orlando, FL, Amer. Meteor. Soc., 4A.5. https://ams.confex.com/ams/28Hurricanes/webprogram/Paper138066.html.

  • Tien, D. D., Ngo-Duc, T., Mai, H. T., & Kieu, C. (2013). A study of the connection between tropical cyclone track and intensity errors in the WRF model. Meteorol: Meteorology and Atmospheric Physics. doi:10.1007/s00703-013-0278-0.

    Google Scholar 

  • Willoughby, H. E. (1979). forced secondary circulations in hurricanes. Journal Geophysical Research, 84, 3173–3183.

    Article  Google Scholar 

  • Willoughby, H. E. (2009). Diabatically induced secondary flows in tropical cyclones. Part II: Periodic forcing. Monthly Weather Review, 137, 822–835.

    Article  Google Scholar 

  • Wilson, R. W., & Ballard, S. P. (1999). A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Quarterly Journal Royal Meteorological Society, 125, 1607–1636.

    Article  Google Scholar 

  • WMO. (2009). Standard format for verification of TC forecast. World Meteorological Organization TCM-VI/Doc. 2.4, p. 6. www.wmo.int/pages/prog/www/tcp/documents/Doc2.4_Verification.doc.

  • World Meteorological Organization technical document. (2008). Tropical cyclone operational plan for the Bay of Bengal and the Arabian Sea, Document No. WMO/TDNo. 84:1–1.

  • 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.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the scientists from Met Office, UK for their immense assistance to successfully run the regional modeling system which is used in this study. Thanks are also due to the IMD and JTWC for providing the best-tracks of the TCs that are used to validate the model simulations. The authors gratefully acknowledge Dr. M. Mohapatra, Scientist, IMD, New Delhi for his help in clarifying the doubts during this study.

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  1. National Centre for Medium Range Weather Forecasting, A-50, Sector-62, Noida, Uttar Pradesh, India

    A. Routray, Vivek Singh, John P. George, Saji Mohandas & E. N. Rajagopal

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  1. A. Routray
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Routray, A., Singh, V., George, J.P. et al. Simulation of Tropical Cyclones over Bay of Bengal with NCMRWF Regional Unified Model. Pure Appl. Geophys. 174, 1101–1119 (2017). https://doi.org/10.1007/s00024-016-1447-0

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