Advertisement

Natural Hazards

, Volume 74, Issue 3, pp 2109–2128 | Cite as

Impact of period and timescale of FDDA analysis nudging on the numerical simulation of tropical cyclones in the Bay of Bengal

  • V. Yesubabu
  • C. V. Srinivas
  • S. S. V. S. Ramakrishna
  • K. B. R. R. Hari Prasad
Original Paper

Abstract

In this study, the impact of four-dimensional data assimilation (FDDA) analysis nudging is examined on the prediction of tropical cyclones (TC) in the Bay of Bengal to determine the optimum period and timescale of nudging. Six TCs (SIDR: November 13–16, 2007; NARGIS: April 29–May 02, 2008; NISHA: November 25–28, 2008; AILA: May 23–26, 2009; LAILA: May 18–21, 2010; JAL: November 04–07, 2010) were simulated with a doubly nested Weather Research and Forecasting (WRF) model with a horizontal resolution of 9 km in the inner domain. In the control run for each cyclone, the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis and forecasts at 0.5° resolution are used for initial and boundary conditions. In the FDDA experiments available surface, upper air observations obtained from NCEP Atmospheric Data Project (ADP) data sets were used for assimilation after merging with the first guess through objective analysis procedure. Analysis nudging experiments with different nudging periods (6, 12, 18, and 24 h) indicated a period of 18 or 24 h of nudging during the pre-forecast stage provides maximum impact on simulations in terms of minimum track and intensity forecasts. To determine the optimum timescale of nudging, two cyclone cases (NARGIS: April 28–May 02, 2008; NISHA: November 25–28, 2008) were simulated varying the inverse timescales as 1.0e−4 to 5.0e−4 s−1 in steps of 1.0e−4 s−1. A positive impact of assimilation is found on the simulated characteristics with a nudging coefficient of either 3.0e−4 or 4.0e−4 s−1 which corresponds to a timescale of about 1 h for nudging dynamic (u,v) and thermodynamical (t,q) fields.

Keywords

Tropical cyclones WRF-ARW FDDA nudging Period Timescale 

Notes

Acknowledgments

Authors sincerely thank Dr. Satyamurty Director, EIRSG, Dr. B. Venkatrman, AD, RSEG and Dr. R. Baskaran Head, RIAS for their encouragement and support in carrying out the study. The first author acknowledges the Space Applications Centre, Ahmadabad for the award of the Junior Research Fellowship under MeghaTropiques-Utilization Project under which this research was carried out. The WRF-ARW model was obtained from NCAR. The NCEP GFS analysis and forecasts were downloaded from NCEP. The QSCAT data were obtained from NASA. The prepbufr surface upper air observations are taken from the online archives of NCAR. Authors thank the anonymous reviewers for critical reviews and valuable comments, which helped to improve the manuscript.

References

  1. Aberson SD, Etherton BJ (2006) Targeting and data assimilation studies during Hurricane Humberto (2001). J Atmos Sci 63:175–186CrossRefGoogle Scholar
  2. Aberson SD, Sampson CR (2003) On the predictability of tropical cyclone tracks in the northwest Pacific basin. Mon Weather Rev 131(7):1491–1497CrossRefGoogle Scholar
  3. Andersson E et al (1998) The ECMWF implementation of three dimensional variational assimilation (3D-Var). Part III: experimental results. Q J R Meterol Soc 124:1831–1860CrossRefGoogle Scholar
  4. Anthes RA (1982) Tropical cyclones: their evolution, structure and effects. Meteorological monographs. Am Meterol Soc Boston 593, 19(41):208Google Scholar
  5. Barnes SG (1964) A technique for maximizing details in numerical weather map analysis. J Appl Meteorol 3:396–409CrossRefGoogle Scholar
  6. Bergthorsson P, Döös BR (1955) Numerical weather map analysis. Tellus 7:329–340Google Scholar
  7. Bhaskar Rao DV, Hari Prasad, D (2006) Numerical prediction of the Orissa super-cyclone: sensitivity to the parameterization of convection, boundary layer and explicit moisture processes. Mausam 57(1):61–78Google Scholar
  8. Bhaskar Rao DV, Hari Prasad D (2007) Sensitivity of tropical cyclone intensification to boundary layer and convective processes. Nat Hazards 41(3):429–445Google Scholar
  9. Bhaskar Rao DV, 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. J Geophys Res 114:D11106, 24 pp. doi: 10.1029/2008JD011623
  10. Chen SH (2007) The impact of assimilating SSM/I and QSCAT satellite winds on Hurricane Isidore simulation. Mon Weather Rev 135:549–566Google Scholar
  11. Chen F, Dudhia J (2001) Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model description and implementation. Mon Weather Rev 129:569–585CrossRefGoogle Scholar
  12. Courtier P et al (1998) The ECMWF implementation of three dimensional variational (3DVAR) data assimilation. Part I: formulation. Q J R Meterol Soc 123:1–26Google Scholar
  13. Daley R (1991) Atmospheric data analysis. Cambridge University Press, New York, p 457Google Scholar
  14. De Vera A, Terra R (2012) Combining CMORPH and rain gauges observations over the Rio Negro Basin. J Hydrometeorol 13:1799–1809CrossRefGoogle Scholar
  15. Deshpande M, Pattnaik S, Salvekar PS (2010) Impact of physical parameterization schemes of numerical simulation of super cyclone Gonu. Nat Hazards 55:211–231. doi: 10.1007/s11069-010-9521-x CrossRefGoogle Scholar
  16. Dudhia J (1989) Numerical study of convection observed during winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  17. Franklin JL, DeMaria M (1992) The impact of Omega dropwindsonde observations on barotropic hurricane track forecasts. Mon Weather Rev 120:381–391CrossRefGoogle Scholar
  18. Franklin JL, Feuer SE, Marks FD Jr (1993) The kinematic structure of Hurricane Gloria (1985), determined from nested analyses of dropwindsonde and Doppler radar data. Mon Weather Rev 121:2433–2451CrossRefGoogle Scholar
  19. Gandin LS (1963) Objective analysis of the meteorological field, Gidrometeorologicheskoe Izdate’stvo, Leningrad, translated from Russian in 1965 by Israel Program for Scientific Translations, Jerusalem, 286Google Scholar
  20. Ghil M, Ide K, Bennett AF, Courtier P, Kimoto M, Sato N (eds) (1997) Data assimilation in meteorology and oceanography: theory and practice. Universal Academy Press, Tokyo, p 496Google Scholar
  21. Gilchrist B, Cressman GP (1954) An experiment in objective analysis. Tellus 6(4):309–318CrossRefGoogle Scholar
  22. Gray WM (2000) General characteristics of tropical cyclones. In: Pielke R Jr, Pielke R Sr (eds) Storms, vol 1, Routledge, 11 New Fetter Lane, London EC4P4EE: 145-163Google Scholar
  23. Harasti PR, McAdie CJ, Dodge PP, Lee WC, Tuttle J, Murillo ST, Marks FD (2004) Real-time implementation of single-doppler radar analysis methods for tropical cyclones: algorithm improvements and use with WSR-88D display data. Weather Forecast 19(2):219–239CrossRefGoogle Scholar
  24. Hoke JE, Anthes RA (1976) The initialization of numerical models by a dynamic initialization technique. Mon Weather Rev 104:1551–1556CrossRefGoogle Scholar
  25. Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341CrossRefGoogle Scholar
  26. India Meteorological Department (2008) Report on cyclonic disturbances over North Indian Ocean during 2007, RSMC-Tropical Cyclone Report No. 1/2008, IMD, New Delhi, India, 98 ppGoogle Scholar
  27. India Meteorological Department (2009) Report on cyclonic disturbances over North Indian Ocean during 2008, RSMC-Tropical Cyclone Report No. 1/2009, IMD, New Delhi, India, 108 ppGoogle Scholar
  28. India Meteorological Department (2010) Report on cyclonic disturbances over North Indian Ocean during 2009, RSMC- Tropical Cyclone Report No. 1/2010, IMD, New Delhi, India, 122 ppGoogle Scholar
  29. India Meteorological Department (2011) Report on cyclonic disturbances over North Indian Ocean during 2010, RSMC- Tropical Cyclone Report No. 1/2011, IMD, New Delhi, India, 162 ppGoogle Scholar
  30. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503Google Scholar
  31. Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181CrossRefGoogle Scholar
  32. Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge, p 364Google Scholar
  33. Kistler RE (1974) A study of data assimilation techniques in an autobarotropic primitive equation channel model. M.S. thesis, The Penn. State University, pp 84Google Scholar
  34. Knaff JA, DeMaria M, Molenar DA, Sampson CR, Seybold MG (2011) An automated objective, multiple-satellite-platform tropical cyclone surface wind analysis. J Appl Meteorol Climatol 50:2149–2166CrossRefGoogle Scholar
  35. Krishna KO, Routray A, Mohanty UC, Kulkarni MA (2010) Simulation of tropical cyclones over Indian Seas: data impact study using WRF-Var assimilation system. In: Charabi Y (ed) Indian Ocean tropical cyclones and climate change. Springer, Berlin, pp 115–124Google Scholar
  36. Langland RH, Velden C, Panley PM, Berger H (2009) Impact of satellite-derived rapid-scan wind observations on numerical model forecasts of hurricane Katrina. Mon Weather Rev 137:1615–1622CrossRefGoogle Scholar
  37. Lei L, Stauffer DR, Deng A (2012a) A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part II: application in a shallow-water model. Tellus A 64:18485. doi: 10.3402/tellusa.v64i0.18485
  38. Lei L, Stauffer DR, Deng A (2012b) A hybrid nudging-ensemble Kalman filter approach to data assimilation in WRF/DART. Q J R Meteorol Soc 138:2066–2078. doi: 10.1002/qj.1939 CrossRefGoogle Scholar
  39. Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Climate Appl Meteorol 22:1065–1092CrossRefGoogle Scholar
  40. Marshall JL, Leslie L, Morrison R, Pescod N, Seecamp R, Spinoso C (2000) Recent developments in the continuous assimilation of satellite wind data for tropical cyclone forecasting. Adv Space Res 25(25):1077–1080CrossRefGoogle Scholar
  41. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14):16663–16682CrossRefGoogle Scholar
  42. Mohanty UC, Mandal M, Raman S (2004) Simulation of Orissa Super Cyclone (1999) using PSU/NCAR mesoscale model. Nat Hazards 31:373–390CrossRefGoogle Scholar
  43. Mukhopadhyay P, Taraphdar S, Goswami BN (2011) Influence of moist processes on track and intensity forecast of cyclones over the north Indian Ocean. J Geophys Res 116:D05116, 21 pp. doi: 10.1029/2010JD014700
  44. Parrish DF, Derber JC (1992) The National Meteorological Center’s Spectral Statistical Interpolation analysis system. Mon Weather Rev 120:1747–1763CrossRefGoogle Scholar
  45. Prasad K, Rao YVR (2003) Cyclone track prediction by a quasi-Lagrangian model. Meteorol Atmos Phys 83:173–185Google Scholar
  46. Pu Z (2009) Assimilation of satellite data in improving numerical simulations of tropical cyclones: progress, challenge and development. In: Park SK, Xu L (eds) Data assimilation for atmospheric, oceanic, and hydrologic applications. Springer, Berlin, pp 163–176CrossRefGoogle Scholar
  47. Pu Z, Tao WK, Braun S, Simpson J, Jia Y, Halverson J, Hou A, Olson W (2002) The impact of TRMM data on mesoscale numerical simulation of super typhoon Paka. Mon Weather Rev 130:2248–2258CrossRefGoogle Scholar
  48. Raghavan S, SenSarma AK (2000) Tropical cyclone impacts in India and neighbourhood. In: Pielke R Jr, Pielke R Sr (eds) Storms, vol 1. Routledge, London, pp 339–356Google Scholar
  49. Raju PVS, Jayaraman P, Mohanty UC (2011) Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model. Meteorol Atmos Phys 113(3–4):125–137. doi: 10.1007/s00703-011-0151-y CrossRefGoogle Scholar
  50. Singh R, Pal PK, Kishtawal CM, Joshi PC (2008) The impact of variational assimilation of SSM/I and QuikSCAT satellite observations on the numerical simulation of Indian Ocean tropical cyclones (2008). Weather Forecast 23:460–476CrossRefGoogle Scholar
  51. Singh R, Kishtawal CM, Pal PK, 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(3–4):103–119. doi: 10.1007/s00703-011-0127-y CrossRefGoogle Scholar
  52. Singh R, Kishtawal CM, Pal PK, Joshi PC (2012) Improved tropical cyclone forecasts over north Indian Ocean with direct assimilation of AMSU-A radiances. Meteorol Atmos Phys 115(1–2):15–34. doi: 10.1007/s00703-011-0165-5 CrossRefGoogle Scholar
  53. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Dudha MG, Huang X, Wang W, Powers Y (2008) A description of the advanced research WRF Ver. 30. NCAR technical note. NCAR/TN-475 + STR. Mesocale and Microscale Meteorology Division, National Centre for Atmospheric Research, Boulder Colorado, USA, 113 ppGoogle Scholar
  54. Soden BJ, Velden CS, Tuleya RE (2001) The impact of satellite winds on experimental GFDL Hurricane model forecasts. Mon Weather Rev 129:835–852CrossRefGoogle Scholar
  55. Srinivas CV, Venkatesan R, Rao DVB, Hariprasad D (2007) Numerical simulation of Andhra severe cyclone (2003): model sensitivity to boundary layer and convection parameterization. Pure appl Geophys 164:1–23CrossRefGoogle Scholar
  56. Srinivas CV, Yesubabu V, Venkatesan R, Ramakrishna 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. Meteor Atmos Phys 110:19–44. doi: 10.1007/s00703-010-0102-z CrossRefGoogle Scholar
  57. Srinivas CV, Yesubabu V, Hari Prasad KBRR, Venkatraman B, Ramakrishna SSVS (2012a) Numerical simulation of cyclonic storms FANOOS, NARGIS with assimilation of conventional and satellite observations using 3-DVAR. Nat Hazards 63(2):867–889CrossRefGoogle Scholar
  58. Srinivas CV, Yesubabu V, Hariprasad KBRR, Ramakrishna SSVS, Venkatraman B (2012b) Real-time prediction of a severe cyclone ‘Jal’ over Bay of Bengal using a high-resolution mesoscale model WRF (ARW). Nat Hazards. doi: 10.1007/s11069-012-0364-5
  59. Srinivas CV, Bhaskar Rao DV, Yesubabu V, Baskaran R, Venkatraman B (2012c) Tropical cyclone predictions over the Bay of Bengal using the high-resolution advanced research weather research and forecasting model. Q J R Meteorol Soc 138. doi: 10.1002/qj.2064
  60. Stauffer DR, Seaman N (1990) Use of four-dimensional data assimilation in a limited-area mesoscale model. 794 Part I: experiments with synoptic-scale data. Mon Weather Rev 118:1252–1277CrossRefGoogle Scholar
  61. Stauffer DR, Seaman N (1994) Multiscale four-dimensional data assimilation. J App Meteorol 33:416–434CrossRefGoogle Scholar
  62. Talagrand O (1997) Assimilation of observations, an introduction. J Meteorol Soc Jpn 75:191–209Google Scholar
  63. Zhang X, Xiao Q, Patrick F (2007) The impact of multi-satellite data on the initialization and simulation of Hurricane Lilli's (2002) rapid weakening phase. Mon Weather Rev 135:526–548Google Scholar
  64. Zou X, Xiao Q (2000) Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme. J Atmos Sci 57:836–860CrossRefGoogle Scholar
  65. Zupanski M, Kalnay E (1999) Principles of data assimilation. In: Browning KA, Gurney RJ (eds) Global energy and water cycles. Cambridge University Press, Cambridge, pp 48–54Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • V. Yesubabu
    • 1
  • C. V. Srinivas
    • 2
  • S. S. V. S. Ramakrishna
    • 3
  • K. B. R. R. Hari Prasad
    • 2
  1. 1.Department of Earth Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia
  2. 2.Radiological Safety and Environment GroupIndira Gandhi Centre for Atomic ResearchKalpakkamIndia
  3. 3.Department of Meteorology and OceanographyAndhra UniversityVisakhapatnamIndia

Personalised recommendations