Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Aberson SD, Etherton BJ (2006) Targeting and data assimilation studies during Hurricane Humberto (2001). J Atmos Sci 63:175–186

    Article  Google Scholar 

  • Aberson SD, Sampson CR (2003) On the predictability of tropical cyclone tracks in the northwest Pacific basin. Mon Weather Rev 131(7):1491–1497

    Article  Google Scholar 

  • Andersson E et al (1998) The ECMWF implementation of three dimensional variational assimilation (3D-Var). Part III: experimental results. Q J R Meterol Soc 124:1831–1860

    Article  Google Scholar 

  • Anthes RA (1982) Tropical cyclones: their evolution, structure and effects. Meteorological monographs. Am Meterol Soc Boston 593, 19(41):208

  • Barnes SG (1964) A technique for maximizing details in numerical weather map analysis. J Appl Meteorol 3:396–409

    Article  Google Scholar 

  • Bergthorsson P, Döös BR (1955) Numerical weather map analysis. Tellus 7:329–340

    Google Scholar 

  • 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):61–78

  • Bhaskar Rao DV, Hari Prasad D (2007) Sensitivity of tropical cyclone intensification to boundary layer and convective processes. Nat Hazards 41(3):429–445

  • 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

  • Chen SH (2007) The impact of assimilating SSM/I and QSCAT satellite winds on Hurricane Isidore simulation. Mon Weather Rev 135:549–566

  • 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 129:569–585

    Article  Google Scholar 

  • Courtier P et al (1998) The ECMWF implementation of three dimensional variational (3DVAR) data assimilation. Part I: formulation. Q J R Meterol Soc 123:1–26

    Google Scholar 

  • Daley R (1991) Atmospheric data analysis. Cambridge University Press, New York, p 457

    Google Scholar 

  • De Vera A, Terra R (2012) Combining CMORPH and rain gauges observations over the Rio Negro Basin. J Hydrometeorol 13:1799–1809

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Dudhia J (1989) Numerical study of convection observed during winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107

    Article  Google Scholar 

  • Franklin JL, DeMaria M (1992) The impact of Omega dropwindsonde observations on barotropic hurricane track forecasts. Mon Weather Rev 120:381–391

    Article  Google Scholar 

  • 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 121:2433–2451

    Article  Google Scholar 

  • 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, 286

  • 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 496

    Google Scholar 

  • Gilchrist B, Cressman GP (1954) An experiment in objective analysis. Tellus 6(4):309–318

    Article  Google Scholar 

  • 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: 145-163

  • 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):219–239

    Article  Google Scholar 

  • Hoke JE, Anthes RA (1976) The initialization of numerical models by a dynamic initialization technique. Mon Weather Rev 104:1551–1556

    Article  Google Scholar 

  • Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341

    Article  Google Scholar 

  • 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 pp

  • 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 pp

  • 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 pp

  • 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 pp

  • 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 5:487–503

  • Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181

    Article  Google Scholar 

  • Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge, p 364

    Google Scholar 

  • Kistler RE (1974) A study of data assimilation techniques in an autobarotropic primitive equation channel model. M.S. thesis, The Penn. State University, pp 84

  • 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 50:2149–2166

    Article  Google Scholar 

  • 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 115–124

    Google Scholar 

  • 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 137:1615–1622

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Climate Appl Meteorol 22:1065–1092

    Article  Google Scholar 

  • 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):1077–1080

    Article  Google Scholar 

  • 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):16663–16682

    Article  Google Scholar 

  • Mohanty UC, Mandal M, Raman S (2004) Simulation of Orissa Super Cyclone (1999) using PSU/NCAR mesoscale model. Nat Hazards 31:373–390

    Article  Google Scholar 

  • 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

  • Parrish DF, Derber JC (1992) The National Meteorological Center’s Spectral Statistical Interpolation analysis system. Mon Weather Rev 120:1747–1763

    Article  Google Scholar 

  • Prasad K, Rao YVR (2003) Cyclone track prediction by a quasi-Lagrangian model. Meteorol Atmos Phys 83:173–185

    Google Scholar 

  • 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 163–176

    Chapter  Google Scholar 

  • 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 130:2248–2258

    Article  Google Scholar 

  • 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 339–356

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 23:460–476

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 pp

  • Soden BJ, Velden CS, Tuleya RE (2001) The impact of satellite winds on experimental GFDL Hurricane model forecasts. Mon Weather Rev 129:835–852

    Article  Google Scholar 

  • 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 164:1–23

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):867–889

    Article  Google Scholar 

  • 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

  • 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

  • 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 118:1252–1277

    Article  Google Scholar 

  • Stauffer DR, Seaman N (1994) Multiscale four-dimensional data assimilation. J App Meteorol 33:416–434

    Article  Google Scholar 

  • Talagrand O (1997) Assimilation of observations, an introduction. J Meteorol Soc Jpn 75:191–209

    Google Scholar 

  • 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 135:526–548

  • 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 57:836–860

    Article  Google Scholar 

  • 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 48–54

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia

    V. Yesubabu

  2. Radiological Safety and Environment Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, 603102, India

    C. V. Srinivas & K. B. R. R. Hari Prasad

  3. Department of Meteorology and Oceanography, Andhra University, Visakhapatnam, 530003, India

    S. S. V. S. Ramakrishna

Authors
  1. V. Yesubabu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. V. Srinivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. S. V. S. Ramakrishna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. B. R. R. Hari Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. V. Srinivas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yesubabu, V., Srinivas, C.V., Ramakrishna, S.S.V.S. et al. Impact of period and timescale of FDDA analysis nudging on the numerical simulation of tropical cyclones in the Bay of Bengal. Nat Hazards 74, 2109–2128 (2014). https://doi.org/10.1007/s11069-014-1293-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-014-1293-2

Keywords

Search

Navigation