• Ahn, M.-H., Y.-K. Seo, H.-S. Park, and A.-S. Suh, 2002: Determination of tropical cyclone center using TRMM Microwave Imager data. Geophys. Res. Lett., 29, doi:10.1029/2001GL013715.

    • Search Google Scholar
    • Export Citation
  • Brousseau, P., and Coauthors, 2008: A prototype convective-scale data assimilation system for operation: The Arome-RUC. HIRLAM Tech. Rep. 68, 23–30.

  • Davis, C. A., and Coauthors, 2008: Prediction of landfalling hurricanes with the Advanced Hurricane WRF model. Mon. Wea. Rev., 136, 19902005.

    • Search Google Scholar
    • Export Citation
  • Dodla, V. B. R., and S. B. Ratna, 2010: Mesoscale characteristics and prediction of an unusual extreme heavy precipitation event over India using a high resolution mesoscale model. Atmos. Res., 95, 255269.

    • Search Google Scholar
    • Export Citation
  • Falguni, P., C. M. Kishtawal, P. K. Pal, and P. C. Joshi, 2004: Geolocation of Indian Ocean tropical cyclones using 85 GHz observations from TRMM Microwave Imager. Curr. Sci., 87, 504509.

    • Search Google Scholar
    • Export Citation
  • Fiorino, M., J. M. Goerss, J. J. Jensen, and E. J. Harrison 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. Wea. Forecasting, 8, 324.

    • Search Google Scholar
    • Export Citation
  • Gopalakrishnan, S. G., N. Surgi, R. Tuleya, and Z. Janjic, 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, Amer. Meteor. Soc., 7A.3. [Available online at https://ams.confex.com/ams/pdfpapers/107899.pdf.]

  • Gopalakrishnan, S. G., S. Goldenberg, T. Quirino, X. Zhang, F. Marks, K.-S. Yeh, R. Atlas, and V. Tallapragada, 2012: Toward improving high-resolution numerical hurricane forecasting: Influence of model horizontal grid resolution, initialization, and physics. Wea. Forecasting, 27, 647666.

    • Search Google Scholar
    • Export Citation
  • Gupta, A., 2006: Current status of tropical cyclone track prediction techniques and forecast errors. Mausam, 57, 151158.

  • Han, J., and H.-L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520533.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and J.-W. Lee, 2009: Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Korea. Atmos. Res., 93, 818831.

    • Search Google Scholar
    • Export Citation
  • Hsiao, L.-F., C.-S. Liou, T.-C. Yeh, Y.-R. Guo, D.-S. Chen, K.-N. Huang, C.-T. Terng, and J.-H. Chen, 2010: A vortex relocation scheme for tropical cyclone initialization in Advanced Research WRF. Mon. Wea. Rev., 138, 32983315.

    • Search Google Scholar
    • Export Citation
  • IMD, 2011: Tracks of cyclones and depressions over North Indian Ocean (from 1891 onwards). India Meteorological Department Tech. Note Cyclone eAtlas—IMD, version 2.0. [Available online at http://www.rmcchennaieatlas.tn.nic.in/Help/TechNote2011.pdf.]

  • Kotal, S. D., and S. K. Roy Bhowmik, 2011: A multimodel ensemble (MME) technique for cyclone track prediction over the North Indian Sea. Geofizika, 28, 275291.

    • Search Google Scholar
    • Export Citation
  • Landman, A. W., A. Seth, and S. J. Camargo, 2005: The effect of regional climate model domain choice on the simulation of tropical cyclone–like vortices in the southwestern Indian Ocean. J. Climate, 18, 12631274.

    • Search Google Scholar
    • Export Citation
  • Lynch, P., D. Giard, and V. Ivanovici, 1997: Improving the efficiency of a digital filtering scheme for diabatic initialization. Mon. Wea. Rev., 125, 19761982.

    • Search Google Scholar
    • Export Citation
  • Marchok, T. P., 2002: How the NCEP tropical cyclone tracker works. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., P1.12. [Available online at https://ams.confex.com/ams/pdfpapers/37628.pdf.]

  • Mohanty, U. C., and A. Gupta, 1997: Deterministic methods for prediction of tropical cyclone tracks. Mausam, 48, 257272.

  • Mohanty, U. C., K. K. Osuri, and S. Pattanayak, 2013: A study on high resolution mesoscale modeling systems for simulation of tropical cyclones over the Bay of Bengal. Mausam, 64, 117134.

    • Search Google Scholar
    • Export Citation
  • Mohapatra, M., B. K. Bandyopadhyay, and D. P. Nayak, 2013a: Evaluation of operational tropical cyclone intensity forecasts over north Indian Ocean issued by India Meteorological Department. Nat. Hazards, 68, 433451.

    • Search Google Scholar
    • Export Citation
  • Mohapatra, M., D. P. Nayak, R. P. Sharma, and B. K. Bandyopadhyay, 2013b: Evaluation of official tropical cyclone track forecast over north Indian Ocean issued by India Meteorological Department. J. Earth Syst. Sci., 122, 589601.

    • Search Google Scholar
    • Export Citation
  • Niyogi, D., T. Holt, S. Zhong, P. C. Pyle, and J. Basara, 2006: Urban and land surface effects on the 30 July 2003 mesoscale convective system event observed in the southern Great Plains. J. Geophys. Res., 111, D19107, doi:10.1029/2005JD006746.

    • Search Google Scholar
    • Export Citation
  • Osuri, K. K., U. C. Mohanty, A. Routray, A. K. Makarand, and M. Mohapatra, 2012a: Sensitivity of physical parameterization schemes of WRF model for the simulation of Indian seas tropical cyclones. Nat. Hazards, 63, 13371359.

    • Search Google Scholar
    • Export Citation
  • Osuri, K. K., U. C. Mohanty, A. Routray, and M. Mohapatra, 2012b: Impact of satellite derived wind data assimilation on track, intensity and structure of tropical cyclones over North Indian Ocean. Int. J. Remote Sens., 33, 16271652.

    • Search Google Scholar
    • Export Citation
  • Pattanaik, D. R., and Y. V. Rama Rao, 2009: Track prediction of very severe cyclone ‘Nargis’ using high resolution Weather Research Forecasting (WRF) model. J. Earth Syst. Sci., 118, 309329.

    • Search Google Scholar
    • Export Citation
  • Pattanayak, S., U. C. Mohanty, and S. G. Gopalakrishnan, 2012: Simulation of very severe Cyclone Mala over Bay of Bengal with HWRF modeling system. Nat. Hazards, 63, 14131437.

    • Search Google Scholar
    • Export Citation
  • Pike, C. A., and C. J. Neumann, 1987: The variation of track forecast difficulty among tropical cyclone basins. Wea. Forecasting, 2, 237241.

    • Search Google Scholar
    • Export Citation
  • Ramarao, Y. V., H. R. Hatwar, and G. Agnihotri, 2006: Tropical cyclone prediction by numerical models in India Meteorological Department. Mausam, 57, 4760.

    • Search Google Scholar
    • Export Citation
  • Routray, A., U. C. Mohanty, S. R. H. Rizvi, D. Niyogi, K. K. Osuri, and D. Pradhan, 2010: Impact of Doppler weather radar data on simulation of Indian monsoon depressions. Quart. J. Roy. Meteor. Soc., 136, 18361850.

    • Search Google Scholar
    • Export Citation
  • Roy Bhowmik, S. K., and S. D. Kotal, 2010: A dynamical statistical model for prediction of a tropical cyclone. Mar. Geod., 33, 412423.

    • Search Google Scholar
    • Export Citation
  • Ryerson, W. R., S. R. Russel, L. Elsberry, and J. Wegiel, 2007: Evaluations of the AFWA Weather Research Forecast Model tropical cyclone predictions. Preprints, 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, Amer. Meteor. Soc., 7A.5. [Available online at https://ams.confex.com/ams/pdfpapers/108856.pdf.]

  • Skamarock, W. C., J. B. Klemp, J. Dudhia, O. G. David, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR/TN–468+STR, 88 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf.]

  • Tallapragada, V., N. Surgi, Q. Liu, Y. Kwon, R. Tuleya, and W. O’Connor, 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. [Available online at https://ams.confex.com/ams/28Hurricanes/webprogram/Paper138066.html.]

  • Torn, R. D., and C. A. Davis, 2012: The influence of shallow convection on tropical cyclone track forecasts. Mon. Wea. Rev., 140, 21882197.

    • Search Google Scholar
    • Export Citation
  • WMO, 2009: Standard format for verification of TC forecast. World Meteorological Organization TCM-VI/Doc. 2.4, 6 pp. [Available online at www.wmo.int/pages/prog/www/tcp/documents/Doc2.4_Verification.doc.]

  • Xiao, Q., 2011: Improvements of hurricane forecast with vortex initialization using WRF Variational (WRF-Var) data assimilation. Recent Hurricane Research—Climate, Dynamics, and Societal Impacts, A. Lupo, Ed., InTech, 297–318. [Available online at http://www.intechopen.com/books/recent-hurricane-research-climate-dynamics-and-societal-impacts.]

  • Yang, L., W.-W. Li, D. Wang, and Y. Li, 2011: Analysis of tropical cyclones in the South China Sea and Bay of Bengal during monsoon season. Recent Hurricane Research—Climate, Dynamics, and Societal Impacts, A. Lupo, Ed., InTech, 227–246. [Available online at http://www.intechopen.com/books/recent-hurricane-research-climate-dynamics-and-societal-impacts.]

  • Zhang, W., Y. Leung, and J. C. L. Chan, 2013: The analysis of tropical cyclone tracks in the western North Pacific through data mining. Part I: Tropical cyclone recurvature. J. Appl. Meteor. Climatol., 52, 13941416.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 612 336 20
PDF Downloads 511 273 15

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

View More View Less
  • 1 School of Earth, Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, Odisha, India
  • | 2 National Centre for Medium Range Weather Forecasting, Noida, India
  • | 3 India Meteorological Department, New Delhi, India
  • | 4 Purdue University, West Lafayette, Indiana
Restricted access

Abstract

The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007–11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%–51%) for a 12–72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%–10% and 8%–24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ~13%–28% and 5%–15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%–40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

Corresponding author address: Prof. U. C. Mohanty, School of Earth, Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, Satya Nagar, Bhubaneswar–751 007, India. E-mail: ucmohanty@gmail.com

Abstract

The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007–11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%–51%) for a 12–72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%–10% and 8%–24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ~13%–28% and 5%–15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%–40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

Corresponding author address: Prof. U. C. Mohanty, School of Earth, Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, Satya Nagar, Bhubaneswar–751 007, India. E-mail: ucmohanty@gmail.com
Save