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Meteorology and Atmospheric Physics

, Volume 131, Issue 1, pp 11–28 | Cite as

Impact of radiance data assimilation on the prediction performance of cyclonic storm SIDR using WRF-3DVAR modelling system

  • K. S. Singh
  • M. Mandal
  • Prasad K. BhaskaranEmail author
Original Paper

Abstract

This study attempts to investigate the impact of assimilation of satellite radiances and its role to improve the model initial condition and forecast of Bay of Bengal cyclone ‘Sidr’ by using the weather research and forecasting model and its three-dimensional variational data assimilation system. Results signify that the assimilation of high-resolution satellite radiances of advanced microwave sounding unit B (AMSU-B) data at peak channels has led to significant improvement in the initial fields of the storm structure. It improved the initial condition of moisture profile more significantly than the temperature profile, when radiances are assimilated into the model. In addition, the assimilation of AMSU-B showed a more positive impact on the prediction of the track and intensity of the storm than the assimilation of radiances of advanced microwave sounding unit A (AMSU-A) and high-resolution infra-red sounder (HIRS). The assimilation exercise with all observations (NCEP PREBUFR, AMSU-A, AMSU-B, HIRS, microwave humidity sounder, and atmospheric infra-red sounder) indicate that the track errors are reduced by about 46, 62, 90, and 86%, respectively, at 24, 48, 72, and 96 h forecasts compared to the experiment considering without data assimilation. The landfall, intensity, and structure of storm are well captured when all observations are assimilated into the model. Overall, it is concluded that assimilation of radiances is beneficial for the analysis and forecast of the storm. The results suggest that assimilating of both NCEP PREBUFR and radiance observations into the mesoscale model improves the initial condition and forecast of the storm.

Notes

Acknowledgements

The authors sincerely acknowledge the IMD for providing the best-fit track data to validate model results, NCEP for providing PREBUFR, GFS analysis and forecasted data sets and NCAR for the WRF and its 3DVAR software. NASA is acknowledged for providing TRMM precipitation data sets. The CSIR is acknowledged for the funding the research activity. First author would like to express his special thanks to Prof. Ramesh Ramchandran, director of NCSCM and Dr. Purvaja Ramchandran, division chair, NCSCM, Ministry of Environment, Forest and Climate Change. We would like to appreciatively acknowledge the IIT Kharagpur for providing necessary facilities to conduct research work.

References

  1. Akter N, Tsuboki K (2012) Numerical simulation of Cyclone Sidr using a cloud-resolving model: characteristics and formation process of an outer rainband. Mon Weather Rev 140:789–810CrossRefGoogle Scholar
  2. Auligné T, McNally AP, Dee DP (2007) Adaptive bias correction for satellite data in a numerical weather prediction system. Q J R Meteorol Soc 133:631–642CrossRefGoogle Scholar
  3. Barker DM, Huang W, Guo YR, Bourgeois AJ, Xiao QN (2004) A three-dimensional variational data assimilation system for MM5: implementation and Initial Results. Mon Weather Rev 132:897–914CrossRefGoogle Scholar
  4. Barker NL, Hogan TF, Campbell WF, Pauley RL, Swadley SD (2005) The Impact of AMSU-A radiance assimilation in the U.S. Navy’s operational global atmospheric prediction system (NOGAPS). A technical report NRL/MR/7530-05-8836, p 18Google Scholar
  5. Barker D, Huang XY, Liu Z, Auligné T, Zhang X, Rugg S, Ajjaji R, Bourgeois A, Bray J, Chen Y, Demirtas M, Guo YR, Henderson T, Huang W, Lin HC, Michalakes J, Rizvi S, Zhang X (2012) The weather research and forecasting (WRF) model’s community variational/ensemble data assimilation system:WRFDA. Bull Am Meteorol Soc 93:831–843CrossRefGoogle Scholar
  6. Bhashkar Rao DV, Prasad DH, Srinivas D, Anjaneyulu Y (2010) Role of vertical resolution in numerical models towards the intensification, structure and track of tropical cyclones. Mar Geodesy 33:338–355CrossRefGoogle Scholar
  7. Bouttier F, Kelly G (2001) Observing-system experiments in the ECMWF 4D-Var data assimilation system. Q J R Meteorol Soc 127:1469–1488CrossRefGoogle Scholar
  8. Chen SH (2007) The impact of assimilating SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulation. Mon Weather Rev 135:549–566CrossRefGoogle Scholar
  9. Chen SH, Vandenberghe F, Petty GW, Bresch JF (2006) Application of SSM/I satellite data to a hurricane simulation. Q J R Meteorol Soc 130:801–825CrossRefGoogle Scholar
  10. Chen S, Li W, Lub Y, Wena Z (2014) Variations of latent heat flux during tropical cyclones over the South China Sea. Meteorol Appl 21:717–723. doi: 10.1002/met.1398 CrossRefGoogle Scholar
  11. Chou CB, Huang HP (2011) The impact of assimilating atmospheric infrared sounder observation on the forecast of typhoon tracks. Adv Meteorol. doi: 10.1155/2011/803593 CrossRefGoogle Scholar
  12. Dee DP (2005) Bias and data assimilation. Q J R Meteorol Soc 131:3323–3343CrossRefGoogle Scholar
  13. Derber JC, Wu WS (1998) The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon Weather Rev 126:2287–2299CrossRefGoogle Scholar
  14. Dube SK, Indu J, Rao AD, Murty TS (2009) Storm surge modelling for the Bay of Bengal and Arabian Sea. Nat Hazard 51:3–27. doi: 10.1007/s11069-009-9397-9 CrossRefGoogle Scholar
  15. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  16. English SJ, Renshaw RJ, Dibben PC, Smith AJ, Rayer PJ, Poulsen C, Saunders FW, Eyre JR (2000) A comparison of the impact of TOVS and ATOVS satellite sounding data on the accuracy of numerical weather forecasts. Q J R Meteorol Soc 126:2911–2931Google Scholar
  17. Eyre JR, Kelly GA, Mcnally AP, Andersson E, Persson A (1993) Assimilation of TOVS radiance information through one dimensional variational analysis. Q J R Meteorol Soc 119:1427–1463CrossRefGoogle Scholar
  18. Greeshma MM, Srinivas CV, Yesubabu V, Naidu CV, Baskaran R, Venkatraman B (2015) Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model. Ann Geophys 33:805–828CrossRefGoogle Scholar
  19. Han Y, Weng F, Liu Q, Delst PV (2007) A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J Geophys Res Atmos 112:D11121. doi: 10.1029/2006JD008208 CrossRefGoogle Scholar
  20. 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
  21. Harris BA, Kelly G (2006) A satellite radiance-bias correction scheme for data assimilation. Q J R Meteorol Soc. doi: 10.1002/qj.49712757418 CrossRefGoogle Scholar
  22. 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
  23. Huang XY, Xiao Q, Barker DM, Zhang X, Michalakes J, Huang W, Henderson T, Bray J, Chen J, Ma Z, Dudhia J, Guo J, Zhang X, Won DJ, Lin HC, Kuo YH (2009) Four-dimensional variational data assimilation for WRF: formulation and preliminary results. Mon Weather Rev 137:299–317CrossRefGoogle Scholar
  24. Kotal SD, Roy Bhowmik SK, Kundu PK (2008) Application of statistical–dynamical scheme for real time forecasting of the Bay of Bengal very severe cyclonic storm ‘‘Sidr’’ of November 2007. Geofizika 25:139–158Google Scholar
  25. Kumar A, Done J, Dudhia J, Niyogi D (2011) Simulations of cyclone Sidr in the Bay of Bengal with a high-resolution model: sensitivity to large-scale boundary forcing. Meteorol Atmos Phys 114:123–137. doi: 10.1007/s00703-011-0161-9 CrossRefGoogle Scholar
  26. Leslie LM, Marshall JF, Morison RP, Spinoso C, Purser RJ, Pescod N, Seecamp R (1998) Improved hurricane track from the continuous assimilation of high quality satellite wind data. Mon Weather Rev 126:1248–1258CrossRefGoogle Scholar
  27. Lewis LM, Bates P, Horsburgh K, Nealb J, Schumann G (2013) A storm surge inundation model of the northern Bay of Bengal using publicly available data. Q J R Meteorol Soc 139:358–369CrossRefGoogle Scholar
  28. Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol 22:1065–1092CrossRefGoogle Scholar
  29. Lorenc AC, Ballard SP, Bell RS, Ingleby NB, Andrews PLF, Barker DM, Bray JR, Clayton AM, Dalby T, Payne TJ, Saunders FW (2000) The met office global three-dimensional variational data assimilation scheme. Q J R Meteorol Soc 126:2991–3012CrossRefGoogle Scholar
  30. Mandal M, Mohanty UC (2006) Impact of satellite derived wind in mesoscale simulation of Orissa super cyclone. Indian J Marine Sci 35(2):161–173Google Scholar
  31. Mandal M, Singh KS, Balaji M, Mahapatra M (2016) Performance of WRF-ARW model in real-time prediction of Bay of Bengal cyclone ‘Phailin’. Pure Appl Geophys 173:1783–1801. doi: 10.1007/s00024-015-1206-7 CrossRefGoogle Scholar
  32. Marshal 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 25:1077–1080CrossRefGoogle Scholar
  33. McMillin LM, Dean C (1982) Evaluation of a new operational technique for producing clear radiances. J Appl Meteorol 21(7):1005–1014CrossRefGoogle Scholar
  34. Mei W, Claudia P, Francois P (2012) The effect of translation speed upon the intensity of tropical cyclones over the tropical ocean. Geophys Res Lett 39:L07801. doi: 10.1029/2011GL050765 CrossRefGoogle Scholar
  35. 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 Atmos 102:16663–16682CrossRefGoogle Scholar
  36. Osuri KK, Mohanty UC, Routray A, Mohapatra M (2012) The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean. Int J Remote Sens 33(5):1627–1652CrossRefGoogle Scholar
  37. Osuri KK, Mohanty UC, Routray A, Mohapatra M, Niyogi D (2013) Real-time track prediction of tropical cyclones over the North Indian Ocean using the ARW model. J Appl Meteor Climatol 52:2476–2492Google Scholar
  38. Osuri KK, Mohanty UC, Routray A, Niyogi D (2015) Improved prediction of Bay of Bengal tropical cyclones through assimilation of Doppler weather radar observations. Mon Weather Rev. doi: 10.1175/MWR-D-13-00381.1 CrossRefGoogle Scholar
  39. Pan HL, Wu WS (1995) Implementing a mass flux convection parameterization package for the NMC medium-range forecast model. NMC Office Note, No. 409, pp 40Google Scholar
  40. Parrish DF, Derber JC (1992) The National Meteorological Center’s spectral statistical interpolation analysis system. Mon Weather Rev 120:1747–1763CrossRefGoogle Scholar
  41. Pattnayak S, Mohanty UC (2008) A comparative study on performance of MM5 and WRF models in simulation of tropical cyclones over Indian seas. Curr Sci 95:923–936Google Scholar
  42. Poterjoy J, Zhang F (2014) Intercomparison and coupling of ensemble and four-dimensional variational data assimilation methods for the analysis and forecasting of Hurricane Karl (2010). Mon Weather Rev 142:3347–3364CrossRefGoogle Scholar
  43. Pu ZX, Braun S (2001) Evaulation of bogus vortex techniques with four dimensional variational data assimilation. Mon Weather Rev 129:2023–2039Google Scholar
  44. Pu Z, Zhang L (2010) Validation of Atmospheric Infrared Sounder temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones. J Geophys Res Atmos 27:115(D24)Google Scholar
  45. Pu Z, Li X, Zipser EJ (2009) Diagnosis of the initial and forecast errors in the numerical simulation of the rapid intensification of Hurricane Emily (2005). Weather Forecast 24:1236–1251Google Scholar
  46. Rakesh V, Singh R, Pal PK, Joshi PC (2009) Impacts of satellite-observed winds and total precipitable water on WRF short-range forecasts over the Indian region during the 2006 summer monsoon. Weather Forecast 24:1706–1731Google Scholar
  47. Rakesh V, Singh R, Pal PK, Joshi PC (2011) Impact of satellite soundings on the simulation of heavy rainfall associated with tropical depressions. Nat Hazards 58:945–980Google Scholar
  48. Routray A, Mohanty UC, Rizvi SRH, Niyogi D, Osuri KK, 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 CrossRefGoogle Scholar
  49. Routray A, Mohanty UC, Osuri KK, Kar SC, Niyogi D (2016) Impact of satellite radiance data on simulations of Bay of Bengal tropical cyclones using the WRF-3DVAR modeling system. IEEE Trans Geosci Remote Sens 54(4):2285–2303CrossRefGoogle Scholar
  50. Sandeep S, Chandrasekar A, Singh D (2006) The impact of assimilation of AMSU data for the prediction of a tropical cyclone over India using a mesoscale model. Int J Remote Sens 27:4621–4653CrossRefGoogle Scholar
  51. Singh KS, Mandal M (2014) Impact of conventional and non-conventional observations on mesoscale prediction of Bay of Bengal cyclone Mahasen. In: Book Proceedings of Meteorolo Agro-meteorol Extreme Events. 3:144–148. ISBN:978-960-524-430-9Google Scholar
  52. Singh R, Pal PK, Kishtawal CM, Joshi PC (2008) The impact of variational assimilation of SSM/I and QSCAT satellite observations on the numerical simulation of Indian Ocean Tropical Cyclones. Weather Forecast 23:460–476CrossRefGoogle Scholar
  53. Singh R, Kishtwal 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 111:103–119. doi: 10.1007/s00703-011-0127-y CrossRefGoogle Scholar
  54. Singh R, Kishtawal CM, Ojha SP, Pal PK (2012a) Impact of assimilation of atmospheric infrared sounder (AIRS) radiances and retrievals in the WRF 3D-Var assimilation system. J Geophys Res Atmos 117:D11107. doi: 10.1029/2011JD017367
  55. Singh R, Kishtwal CM, Pal PK (2012b) Impact of ATOVS radiance on the analysis and forecasts of a mesoscale model over the indian region during the 2008 summer monsoon. Pure Appl Geophys 169:425–445CrossRefGoogle Scholar
  56. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF version 2, NCAR TECHNICAL NOTEGoogle Scholar
  57. Srinivas CV, Yesubabu V, Venkatesan R, Ramakrishna SS (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 110:19–44CrossRefGoogle Scholar
  58. Srinivas CV, Yesubabu V, Hariprasad KB, Ramakrishna SS, Venkatraman B (2013) Real-time prediction of a severe cyclone ‘Jal’over Bay of Bengal using a high-resolution mesoscale model WRF (ARW). Nat Hazard 65(1):331–357CrossRefGoogle Scholar
  59. Vinodkumar, Chandrasekhar A, Alapaty K, Niyogi D (2008) The impacts of indirect soil moisture assimilation and direct surface temperature and humidity assimilation on a mesoscale model simulation of an Indian monsoon depression. J Appl Meteorol Climatol 47:1393–1412CrossRefGoogle Scholar
  60. Weng F (2007) Advances in radiative transfer modeling in support of satellite data assimilation. J Atmos Sci 64:3799–3807CrossRefGoogle Scholar
  61. Weng F, Zou X, Wang X, Yang S, Goldberg MD (2012) Introduction to Suomi national polar-orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications. J Geophys Res 117:D19112Google Scholar
  62. Wu L, Wang B, Geng S (2005) Growing influence of Typhoon on East Asia. Geophys Res Lett 32:L18703Google Scholar
  63. Xie Y, Xing J, Shi J, Dou Y, Lei Y (2016) Impacts of radiance data assimilation on the Beijing 7.21 heavy rainfall. Atmos Res 169:318–330CrossRefGoogle Scholar
  64. Xu D, Liu Z, Huang X-Y, Min J, Wang H (2013) Impact of assimilating IASI radiance observations on forecasts of two tropical cyclones. Meteorol Atmos Phys 122:1–18CrossRefGoogle Scholar
  65. Xu D, Min J, Shen F, Ban J, Chen P (2016) Assimilation of MWHS radiance data from the FY-3B satellite with the WRF Hybrid-3DVAR system for the forecasting of binary typhoons. J Adv Model Earth Syst 8(2):1014–1028CrossRefGoogle Scholar
  66. Zhang X, Xiao Q, Patrick F (2007) The impact of multi-satellite data on the initialization and simulation of Hurricane Lili’s (2002). Mon Weather Rev 135:526–548CrossRefGoogle Scholar
  67. 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
  68. Zou X, Wang X, Weng F, Li G (2011) Assessments of Chinese Fengyun microwave temperature sounder (MWTS) measurements for weather and climate applications. J Atmos Ocean Technol 28:1206–1227CrossRefGoogle Scholar
  69. Zou X, Weng F, Zhang B, Lin L, Qin Z, Tallapragada V (2013) Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes. J Geophys Res Atmos 118:558–576. doi: 10.1002/2013JD020405 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  • K. S. Singh
    • 1
    • 2
  • M. Mandal
    • 2
  • Prasad K. Bhaskaran
    • 3
    Email author
  1. 1.National Centre for Sustainable Coastal ManagementMinistry of Environment, Forest and Climate ChangeChennaiIndia
  2. 2.Centre for Oceans, Rivers, Atmosphere and Land ScienceIndian Institute of Technology, KharagpurKharagpurIndia
  3. 3.Ocean Engineering and Naval ArchitectureIndian Institute of Technology, KharagpurKharagpurIndia

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