We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Impact of Initial Condition on Prediction of Bay of Bengal Cyclone ‘Viyaru’ – A Case Study

by K. S. Singh, M. Mandal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 10
Year of Publication: 2014
Authors: K. S. Singh, M. Mandal
10.5120/16378-5868

K. S. Singh, M. Mandal . Impact of Initial Condition on Prediction of Bay of Bengal Cyclone ‘Viyaru’ – A Case Study. International Journal of Computer Applications. 94, 10 ( May 2014), 18-24. DOI=10.5120/16378-5868

@article{ 10.5120/16378-5868,
author = { K. S. Singh, M. Mandal },
title = { Impact of Initial Condition on Prediction of Bay of Bengal Cyclone ‘Viyaru’ – A Case Study },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 10 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number10/16378-5868/ },
doi = { 10.5120/16378-5868 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:16.583644+05:30
%A K. S. Singh
%A M. Mandal
%T Impact of Initial Condition on Prediction of Bay of Bengal Cyclone ‘Viyaru’ – A Case Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 10
%P 18-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accurate prediction of track and intensity of land-falling tropical cyclones is of the great importance in weather prediction in making an effective tropical cyclone warning. This study examines the impact of initial condition on real time prediction of Bay of Bengal cyclone Viyaru. For this purpose, the customized version of Advanced Research core of Weather Research and Forecasting (ARW-WRF) model with two-way interactive double nested model at 27 km and 9 km resolutions is used to predict the storm. The model initial conditions are derived from the FNL analysis and Global Forecasting System (GFS) analysis and the lateral boundary condition is provided every 3 hourly from GFS forecast. The model predicted track and intensity of the storm are compared with the India Meteorological Department (IMD) best-fit track. Results indicate that the track of the storm is reasonably well predicted by the model with both FNL and GFS initial condition. The track of the storm is better predicted by the model with FNL initial condition. It is found that in reference to the track predicted errors with GFS initial condition, the use of FNL initial analysis as condition resulted in 41%, 8%, 5% and 19% improvement respectively in 24h, 48h, 72h, and 96h forecast. This is due to less initial positional error in FNL analysis. The landfall time and location of the storm is also better predicted by the model with FNL initial condition. The trend of intensification and dissipation of the storm is also better predicted with FNL as the initial condition. The intensity of the storm in term of central sea level pressure (CSLP) and maximum surface wind (MSW) is over-predicted by the model with both initial conditions. The 24 hours accumulated precipitation around the landfall time is also better predicted by the model with FNL initial condition.

References
  1. Emanuel, K. 2005. Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 636-638 doi: 10. 1038/nature 03906.
  2. Bhaskar Rao, D. V. , Naidu, C. V. , Srinivasa Rao, B. 2001. Trends and fluctuations of the cyclonic systems over North IndianOcean. Mausam, 52(1. 1-8.
  3. Rogers, R. , Aberson, S. , Black, M. , Black, P. , Cione, J. , Dodge, J. , Gamache, J. , Kaplan,J. , Powell, M. , Dunion, J. , Uhlhorn, E. , Shay, N. , and Surgi, N. 2006. The intensity forecasting experiment: A NOAA multiyear field program for improving tropical cyclone intensity forecasts. Bull. Ame. Mete. Soc. , 87, 1523-1537, doi: 10. 1175/BAMS-87-11-1523.
  4. Rappaport, E. N. , Franklin, J. L. , Avila, L. A. , Baig, S. R. , Beven, J. L. , Blake, E. S. , Burr, C. A. , Jiing, J. G. , Juckins, C. A. , RKnabb, R. D. , Landsea, C. W. , Mainelli, M. , Mayfield, M. , McAdie, C. J. , Pasch, R. J. , C. Sisko, R. J. C. , Stewart, S. R. , and A. N. Tribble, A. N. 2009. Advances and challenges at the National Hurricane Center. Wea. Forecast. , 24, 395-419,
  5. Zhang, F. , Weng, Y. , Gamache, J. F. , and Marks, F. D. 2011. Performance of convection-permitting hurricane initialization and prediction during 2008-2010 with ensemble data assimilation of inner core airborne Doppler radar observations. Geophys. Res. Lett. , 38, doi: 10. 1029/2011GL048469.
  6. De Maria, M. and Kaplan, J. 1999. An updated statistical hurricane intensity prediction scheme for the Atlantic and eastern North Pacific basins", Weather Forecasting, 14, 326-337.
  7. Braun, S. A. , Montgomery, M. T. and Pu, Xu. 2006. High-resolution simulation of hurricane Bonnie (1998). Part I: The organization of eyewall vertical motion. J. Atmos. Sci. , 63, 19-42.
  8. Chen, S. S. 2006. Overview of RAINEX modeling of 2005 hurri- canes. Preprints, 27th Conf. on Hurricanes and Tropical Meteorology. Amer. Meteor. Soc. , 12A. 2.
  9. Krishnamurti, T. N. 2005. The hurricane intensity issue. Mon. Wea. Rev. , 133, 1886-1912.
  10. Zhu, T. , Zhang D. L. , and Weng, F. 2004. Numerical simulation of hurricane Bonnie (1998). Part I: Eyewall evolution and intensity changes. Mon. Wea. Rev. , 132, 225-241.
  11. Franklin, J. L. , Lord, S. J. , and Marks Jr, F. D. 1988. Dropwind-sonde and radar observations of the eye of Hurricane Gloria (1985). Mon. Wea. Rev. , 116, 1237-1244.
  12. Kossin, J. P. , and Eastin, M. D. 2001. Two distinct regimes in the kinematics and thermodynamic structure of the hurricane eye and eyewall. J. Atmos. Sci. , 58, 1079-1090.
  13. Houze, R. A. , and Coauthors 2006. The Hurricane Rainband and Intensity Change Experiment: Observations and modeling of Hurricanes Katrina, Ophelia, and Rita. Bull. Amer. Meteor. Soc. , 87, 1503-1521.
  14. Pielke, R. A. , Matsui, T. , Leoncini, G. , Nobis, T. , Nair, U. , Lu, E. , Eastman, J. , Kumar, S. , Peters, C. L. , Tian, Y. , and Walko, R. 2006. A new paradigm for parameterizations in numerical weather prediction and other atmospheric models. National Wea. Digest, 30, 93-99.
  15. Mandal, M. , Mohanty, U. C. 2006. Impact of satellite derived wind in mesoscale simulation of Orissa super cyclone. Indian Jou. Marine Sci. , 35(2) 161-173.
  16. Abhilash, S. , Das, S. , Kalsi, S. R. , Gupta, M. D. , Mohankumar, K. , George, J. P. , Banergee, S. K,, Thampi, S. B. and Pradhan D. 2007. Impact of doppler radar wind in simulating the intensity and propagation of rain bands associated with mesoscale convective complexes using MM5-3DVAR system. Pure appl. geophys. , 164, 1491-1509. DOI 10. 1007/s00024-007-0235-2.
  17. Singh, R. , Pal, P. K. , Kishtawal, C. M. , Joshi, P. C. 2008. The impact of variational assimilation of SSM/I and QSCAT satellite observations on the numerical simulation of Indian Ocean Tropical Cyclones. Wea. Forecasting, 23, 460-476.
  18. Routray, A. , Mohanty, U. C. , Rizvi, S. R. H, Niyogi, D. , Osuri, K. K. , Pradhan, D. 2010. Impact of doppler weather radar data on numerical forecast of Indian monsoon depressions. Q. J. R. Meteorol. Soc. , DOI:10. 1002/qj. 678.
  19. Srinivas CV, Yesubabu V, Venkatesan R, and Ramarkrishna SSVS. 2010. Impact of assimilation of conventional and satellite meteorological observations on the numerical simulation of a Bay of Bengal Tropical Cyclone of November 2008 near Tamilnadu using WRF model, Meteorol Atmos Phys, 110, 19-44. DOI 10. 1007/s00703-010-0102-z
  20. Singh R, Kishtwal CM, Pal PK, and Joshi PC. 2011. Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones, Meteorol. Atmos. Phys. , 111, 103-119 DOI 10. 1007/s00703-011-0127-y.
  21. Osuri, KK. , Mohanty UC, Routray A, and Mahapatra M. 2012) The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean, Inter. Jour. Rem. Sensing, 33(5) 1627-1652. http://dx. doi. org/10. 1080/01431161. 2011. 596849.
  22. Govindankutty, M. , Chandrasekar, A. , and Pradhan D. 2010. Impact of 3DVAR assimilation of Doppler Weather Radar wind data and IMD observation for the prediction of a tropical cyclone, Intern. Jour. Rem. Sensing. 31(24) 6327-6345.
  23. Srinivas C. V. , Yesubabu, V. , Hariprasad R. B. R. R. , and Ramarkrishna S. S. V. , Venkatraman, B. 2013. Real time prediction of a severe cyclone Jal over Bay of Bengal using a high-resolution mesoscale model WRF-ARW. Nat. Hazards, 65, 331-357. DOI 10. 1007/s11069-012-0364-5.
  24. Pan, H. L. and Wu, W. S. 1995. Implementing a mass flux convective parameterization package for the NMC medium-range forecast model. NMC Office Note 409, 40 pp. [Available from NCEP/EMC, 5200 Auth Rd. , Camp Springs, MD 20746].
  25. Hong, S. Y. , Noh, Y. , and Dudhia, J. 2006. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev. , 134, 2318-2341.
  26. Lin, Y. L. , Farley, R. D. , Orville, H. D. 1983. Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteorol. , 22, 1065-1092.
  27. Mlawer, E. J. , Taubman, S. J. , Brown, P. D. , Iacono, M. J. , Clough, S. A. 1997. Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys Res. , 102(D14. 16663-16682.
  28. Dudhia, J. 1989. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci. , 46, 3077-3107.
  29. Ryerson W. R. , Rugg, S. , Elsberry, R. L. , Wegiel, J. 2007. Evaluations of the AFWA weather research forecast model Western North Pacific tropical cyclone predictions. http://ams. confex. com/ams/pdfpapers/108856. pdf.
  30. Osuri K. K. , Mohanty, U. C. , Routray, A. , Makarand, A. K. , Mohapatra, M. 2011. Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean. Natural Hazards, doi 10. 1007/s11069-011-9862-0.
  31. Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender
Index Terms

Computer Science
Information Sciences

Keywords

Viyaru Bay of Bengal pressure wind precipitation.