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Impact of data assimilation on a calibrated WRF model for the prediction of tropical cyclones over the Bay of Bengal


Affiliations
1 Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
2 Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India; Center of Excellence in Atmospheric and Climate Sciences, Indian Institute of Technology Madras, Chennai 600 036, India; Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India
 

The main objective of the present study is to examine the impact of three-dimensional variational data assimilation utilizing the multivariate background error covariance (BEC) estimates, in combination with the model calibration, for the simulations of seven tropical cyclones over the Bay of Bengal region. The study indicates that the utilization of multivariate BEC in assimilation influences the model forecasts in terms of wind speed at 10 m height, precipitation, cyclone tracks and cyclone intensity. The assimilation experiments conducted with a previously calibrated model combined with the control variable option 6 (cv6) of BEC have reduced the overall ischolar_main mean square error (RMSE) of 10 m wind speed by 17.02%, precipitation by 11.14%, cyclone track by 41.93% and the intensity by 25.5% when compared to the default model simulations without assimilation. The best experimental set-up is then used for the operational forecast of a recent cyclone Gulab. The results show an RMSE reduction of 18.61% in the cyclone track and 28.99% in intensity forecasts. These results also confirm that the utilization of cv6 BEC in the assimilation of conventional and radiance observations on a calibrated model improves the forecast of tropical cyclones over the Bay of Bengal region.

Keywords

Data assimilation, model calibration, multivariate background error statistics, operational forecast, tropical cyclones.
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  • Gray, W. M., Global view of the origin of tropical disturbances and storms. Mon. Weather Rev., 1968, 96(10), 669–700.

  • Singh, O. P., Ali Khan, T. M. and Rahman, Md. S., Changes in the frequency of tropical cyclones over the north Indian Ocean. Meteorol. Atmos. Phys., 2000, 75(1–2), 11–20.

  • Deshpande, M., Singh, V. K., Ganadhi, M. K., Roxy, M. K., Emmanuel, R. and Kumar, U., Changing status of tropical cyclones over the north Indian Ocean. Climate Dyn., 2021, 57(11), 1–23.

  • Rao, D. V. B., Srinivas, D. and Satyanarayana, G. C., Trends in the genesis and landfall locations of tropical cyclones over the Bay of Bengal in the current global warming era. J. Earth Syst. Sci., 2019, 128(7), 1–10.

  • Jyoteeshkumar Reddy, P., Sriram, D., Gunthe, S. S. and Balaji, C., Impact of climate change on intense Bay of Bengal tropical cyclones of the post-monsoon season: a pseudo global warming approach. Climate Dyn., 2021, 56(9), 1–25.

  • Ooyama, K., Numerical simulation of the life cycle of tropical cyclones. J. Atmos. Sci., 1969, 26(1), 3–40.

  • Nadimpalli, R., Osuri, K. K., Pattanayak, S., Mohanty, U. C., Nageswararao, M. M. and Kiran Prasad, S., Real-time prediction of movement, intensity and storm surge of very severe cyclonic storm Hudhud over Bay of Bengal using high-resolution dynamical model. Nat. Hazards, 2016, 81(3), 1771–1795.

  • Sandeep, C. P. R., Krishnamoorthy, C., and Balaji, C., Impact of cloud parameterization schemes on the simulation of cyclone Vardah using the WRF model. Curr. Sci., 2018, 115(6), 1143– 1153.

  • Chandramouli, K. and Chakravarthy, B., Ingesting microwave sounder radiances for improvement in track forecast of cyclone Vardah. J. Appl. Remote Sensing, 2018, 12(2), 026015.

  • Chandrasekar, R. and Balaji, C., Sensitivity of tropical cyclone Jal simulations to physics parameterizations. J. Earth Syst. Sci., 2012, 121(4), 923–946.

  • Chandrasekar, R. and Balaji, C., Impact of physics parameterization and 3DVAR data assimilation on prediction of tropical cyclones in the Bay of Bengal region. Nat. Hazards, 2016, 80(1), 223–247.

  • Baki, H., Chinta, S., Balaji, C. and Srinivasan, B., A sensitivity study of WRF model microphysics and cumulus parameterization schemes for the simulation of tropical cyclones using GPM radar data. J. Earth Syst. Sci., 2021, 130(4), 1–30.

  • Di, Z. et al., Assessing WRF model parameter sensitivity: a case study with 5 day summer precipitation forecasting in the Greater Beijing area. Geophys. Res. Lett., 2015, 42(2), 579–587.

  • Kalnay, E., Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, Cambridge, 2003.

  • Yang, B., Qian, Y., Lin, G., Leung, R. and Zhang, Y., Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model. Atmosp. Chem. Phys., 2012, 12(5), 2409–2427.

  • Baki, H., Chinta, S., Balaji, C. and Srinivasan, B., WRF model parameter calibration to improve the prediction of tropical cyclones over the Bay of Bengal using machine learning-based multiobjective optimization. arXiv preprint arXiv:2110.05817, 2021.

  • Singh, R., Kishtawal, C. M., Pal, P. K. and Joshi, P. C., Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones. Meteorol. Atmos. Phys., 2011, 111(3–4), 103–119.

  • Ha, J. H. and Lee, D. K., Effect of length scale tuning of background error in WRF-3DVAR system on assimilation of highresolution surface data for heavy rainfall simulation. Adv. Atmos. Sci., 2012, 29(6), 1142–1158.

  • Osuri, K. K., Mohanty, U. C., Routray, A. and Mohapatra, M., The impact of satellite-derived wind data assimilation on track, intensity, and structure of tropical cyclones over the North Indian Ocean. Int. J. Remote Sensing, 2012, 33(5), 1627–1652.

  • Yesubabu, V., Srinivas, C. V., Hariprasad, K. B. R. R. and Baskaran, R., A study on the impact of observation assimilation on the numerical simulation of tropical cyclones JAL and THANE using 3DVAR. Pure Appl. Geophys., 2014, 171(8), 2023–2042.

  • Dhanya, M., Gopalakrishnan, D., Chandrasekar, A., Singh, S. K. and Prasad, V. S., The impact of assimilating Megha Tropiques SAPHIR radiances in the simulation of tropical cyclones over the Bay of Bengal using the WRF model. Int. J. Remote Sensing, 2016, 37(13), 3086–3103.

  • Gopalakrishnan, D. and Chandrasekar, A., On the improved predictive skill of WRF model with regional 4DVar Initialization: a study with north Indian Ocean tropical cyclones. IEEE Trans. Geosci. Remote Sensing, 2018, 56(6), 3350–3357.

  • Gopalakrishnan, D. and Chandrasekar, A., Improved 4DVar simulation of Indian Ocean tropical cyclones using a regional model. IEEE Trans. Geosci. Remote Sensing, 2018, 56(9), 5107–5114.

  • Kutty, G., Gogoi, R., Rakesh, V. and Pateria, M., Comparison of the performance of hybrid etkf-3dvar and 3dvar data assimilation scheme on the forecast of tropical cyclones formed over the Bay of Bengal. J. Earth Syst. Sci., 2020, 129(1), 1–14.

  • Bannister, R. N., A review of forecast error covariance statistics in atmospheric variational data assimilation. i: Characteristics and measurements of forecast error covariances. Q. J. R. Meteorol. Soc. J. Atmosph. Sci., Appl. Meteorol. Phys. Oceanogr., 2008, 134(637), 1951–1970.

  • Rakesh, V. and Goswami, P., Impact of background error statistics on forecasting of tropical cyclones over the north Indian Ocean. J. Geophys. Res. Atmosp., 2011, 116(20), 1–21.

  • Dhanya, M. and Chandrasekar, A., Multivariate background error covariances in the assimilation of SAPHIR radiances in the simulation of three tropical cyclones over the Bay of Bengal using the WRF model. Int. J. Remote Sensing, 2018, 39(1), 191–209.

  • Thiruvengadam, P., Indu, J. and Ghosh, S., Improving convective precipitation forecasts using ensemble-based background error covariance in 3DVAR radar assimilation system. Earth Space Sci., 2020, 7(4), 1–11.

  • Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G. and Powers, J. G., A description of the Advanced Research WRF version 3 (No. NCAR/TN-475+STR). University Corporation for Atmospheric Research; doi:10.5065/D68S4MVH.

  • Kain, J. S., The Kain–Fritsch convective parameterization: an update. J. Appl. Meteorol., 2004, 43(1), 170–181.

  • Beljaars, A. C. M., The parametrization of surface fluxes in largescale models under free convection. Q. J. R. Meteorol. Soc., 1995, 121(522), 255–270.

  • Hong, S.-Y. and Lim, J.-O. J., The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pac. J. Atmos. Sci., 2006, 42(2), 129–151.

  • Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and Clough, S. A., Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res.: Atmos., 1997, 102(D14), 16663–16682.

  • Tewari, M. et al., Implementation and verification of the unified NOAH land surface model in the WRF model (formerly paper number 17.5). In Proceedings of the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, WA, USA, 2004, pp. 11–15.

  • Dudhia, J., Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 1989, 46(20), 3077–3107.

  • Hong, S.-Y., Noh, Y. and Dudhia, J., A new vertical diffusion package with an explicit treatment of entrainment processes. Month. Weather Rev., 2006, 134(9), 2318–2341.

  • Thiruvengadam, P., Indu, J. and Ghosh, S., Improving convective precipitation forecasts using ensemble-based background error covariance in 3DVar radar assimilation system. Earth Space Sci.,

  • , 7(4), e2019EA000667.

  • Parrish, D. F. and Derber, J. C., The National Meteorological Center’s spectral statistical interpolation analysis system. Month. Weather Rev., 1992, 120(8), 1747–1763.

  • National Centers for Environmental Prediction, National Weather Service, NOAA, US Department of Commerce. NCEP ADP global upper air and surface weather observations (prepbufr format), NCEP, USA, 2008.

  • National Centers for Environmental Prediction, National Weather Service, NOAA, US Department of Commerce, NCEP GDAS satellite data 2004-continuing. NCEP, USA, 2009.

  • Huffman, G. and Savtchenko, A. K., GPM IMERG final precipitation L3 half hourly 0.1 degree  0.1 degree V06, 2019 (accessed on 23 September 2020).

  • Indira Rani, S. et al., Imdaa: high-resolution satellite-era reanalysis for the Indian monsoon region. J. Climate, 2021, 34(12), 5109–5133.

  • Yesubabu, V., Srinivas, C. V., Hariprasad, K. B. R. R. and Baskaran, R., A study on the impact of observation assimilation on the numerical simulation of tropical cyclones Jal and Thane using 3dVar. Pure Appl. Geophys., 2014, 171(8), 2023–2042.

  • Dhanya, M. and Chandrasekar, A., Multivariate background error covariances in the assimilation of Saphir radiances in the simulation of three tropical cyclones over the Bay of Bengal using the WRF model. Int. J. Remote Sensing, 2018, 39(1), 191–209.

  • Zhao, M. and Held, I. M., TC-permitting GCM simulations of hurricane frequency response to sea surface temperature anomalies projected for the late-twenty-first century. J. Climate, 2012, 25(8), 2995–3009.

  • Bell, R., Strachan, J., Vidale, P.-L., Hodges, K. and Roberts, M., Response of tropical cyclones to idealized climate change experiments in a global high-resolution coupled general circulation model. J. Climate, 2013, 26(20), 7966–7980.

  • Jackson, L. S. et al., The effect of explicit convection on couplings between rainfall, humidity, and ascent over Africa under climate change. J. Climate, 2020, 33(19), 8315–8337.

  • Ma, L.-M. and Tan, Z.-M., Improving the behavior of the cumulus parameterization for tropical cyclone prediction: convection trigger. Atmos. Res., 2009, 92(2), 190–211.

  • Emanuel, K., DesAutels, C., Holloway, C. and Korty, R., Environmental control of tropical cyclone intensity. J. Atmos. Sci., 2004, 61(7), 843–858.

  • Hendricks, E. A., Peng, M. S., Fu, B. and Li, T., Quantifying environmental control on tropical cyclone intensity change. Month. Weather Rev., 2010, 138(8), 3243–3271.

  • Kaplan, J., DeMaria, M. and Knaff, J. A., A revised tropical cyclone rapid intensification index for the Atlantic and eastern north Pacific basins. Weather Forecast., 2010, 25(1), 220–241.


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  • Impact of data assimilation on a calibrated WRF model for the prediction of tropical cyclones over the Bay of Bengal

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Authors

Harish Baki
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
C. Balaji
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India; Center of Excellence in Atmospheric and Climate Sciences, Indian Institute of Technology Madras, Chennai 600 036, India; Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India
Balaji Srinivasan
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India

Abstract


The main objective of the present study is to examine the impact of three-dimensional variational data assimilation utilizing the multivariate background error covariance (BEC) estimates, in combination with the model calibration, for the simulations of seven tropical cyclones over the Bay of Bengal region. The study indicates that the utilization of multivariate BEC in assimilation influences the model forecasts in terms of wind speed at 10 m height, precipitation, cyclone tracks and cyclone intensity. The assimilation experiments conducted with a previously calibrated model combined with the control variable option 6 (cv6) of BEC have reduced the overall ischolar_main mean square error (RMSE) of 10 m wind speed by 17.02%, precipitation by 11.14%, cyclone track by 41.93% and the intensity by 25.5% when compared to the default model simulations without assimilation. The best experimental set-up is then used for the operational forecast of a recent cyclone Gulab. The results show an RMSE reduction of 18.61% in the cyclone track and 28.99% in intensity forecasts. These results also confirm that the utilization of cv6 BEC in the assimilation of conventional and radiance observations on a calibrated model improves the forecast of tropical cyclones over the Bay of Bengal region.

Keywords


Data assimilation, model calibration, multivariate background error statistics, operational forecast, tropical cyclones.

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DOI: https://doi.org/10.18520/cs%2Fv122%2Fi5%2F569-583