Journal of Meteorological Research

, Volume 31, Issue 4, pp 747–766 | Cite as

Improving the extreme rainfall forecast of Typhoon Morakot (2009) by assimilating radar data from Taiwan Island and mainland China

  • Xuwei Bao
  • Dan Wu
  • Xiaotu Lei
  • Leiming Ma
  • Dongliang Wang
  • Kun Zhao
  • Ben Jong-Dao Jou
Article
First Online:
Received:
Accepted:
  • 146 Downloads

Abstract

This study examined the impact of an improved initial field through assimilating ground-based radar data from mainland China and Taiwan Island to simulate the long-lasting and extreme rainfall caused by Morakot (2009). The vortex location and the subsequent track analyzed through the radial velocity data assimilation (VDA) are generally consistent with the best track. The initial humidity within the radar detecting region and Morakot’s northward translation speed can be significantly improved by the radar reflectivity data assimilation (ZDA). As a result, the heavy rainfall on both sides of Taiwan Strait can be reproduced with the joint application of VDA and ZDA. Based on sensitivity experiments, it was found that, without ZDA, the simulated storm underwent an unrealistic inward contraction after 12-h integration, due to underestimation of humidity in the global reanalysis, leading to underestimation of rainfall amount and coverage. Without the vortex relocation via VDA, the moister (drier) initial field with (without) ZDA will produce a more southward (northward) track, so that the rainfall location on both sides of Taiwan Strait will be affected. It was further found that the improvement in the humidity field of Morakot is mainly due to assimilation of high-value reflectivity (strong convection) observed by the radars in Taiwan Island, especially at Kenting station. By analysis of parcel trajectories and calculation of water vapor flux divergence, it was also found that the improved typhoon circulation through assimilating radar data can draw more water vapor from the environment during the subsequent simulation, eventually contributing to the extreme rainfall on both sides of Taiwan Strait.

Key words

Morakot radar assimilation rainfall simulation 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

The authors are grateful to Dr. Fuqing Zhang and the two anonymous reviewers for providing valuable comments and suggestions that improved our original manuscript.

References

  1. Chanson, H., 2010: The impact of Typhoon Morakot on the southern Taiwan coast. Shore Beach, 78, 33–37.Google Scholar
  2. Chien, F. -C., and H. -C. Kuo, 2011: On the extreme rainfall of Typhoon Morakot (2009). J. Geophys. Res., 116, D05104, doi: 10.1029/2010JD015092.CrossRefGoogle Scholar
  3. Crum, T. D., R. L. Albert, and D. W. Burgess, 1993: Recording, archiving, and using WSR-88D data. Bull. Amer. Meteor. Soc., 74, 645–653, doi: 10.1175/1520-0477(1993)074<0645: RAAUWD>2.0.CO;2.CrossRefGoogle Scholar
  4. Dong, J., and M. Xue, 2013: Assimilation of radial velocity and reflectivity data from coastal WSR-88D radars using an ensemble Kalman filter for the analysis and forecast of landfalling Hurricane Ike (2008). Quart. J. Roy. Meteor. Soc., 139, 467–487, doi: 10.1002/qj.1970.CrossRefGoogle Scholar
  5. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using amesoscale twodimensional model. J. Atmos. Sci., 46, 3077–3107, doi: 10. 1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.CrossRefGoogle Scholar
  6. Fang, X., Y. -H. Kuo, and A. Wang, 2011: The impacts of Taiwan topography on the predictability of Typhoon Morakot’s record-breaking rainfall: A high-resolution ensemble simulation. Wea. Forecasting, 26, 613–633, doi: 10.1175/WAF-D- 10-05020.1.CrossRefGoogle Scholar
  7. Fast, J. D., and R. C. Easter, 2006: A lagrangian particle dispersion model compatible with WRF. 7th Annual WRF User’s Workshop. NCAR, June 19–22, Boulder, CO, P6.2.Google Scholar
  8. Ge, X., T. Li, S. Zhang, et al., 2010: What causes the extremely heavy rainfall in Taiwan during Typhoon Morakot (2009). Atmos. Sci. Lett., 11, 46–50.Google Scholar
  9. Hall, J. D., M. Xue, L. Ran, et al., 2013: High-resolution modeling of Typhoon Morakot (2009): Vortex Rossby waves and their role in extreme precipitation over Taiwan. J. Atmos. Sci., 70, 163–186, doi: 10.1175/JAS-D-11-0338.1.CrossRefGoogle Scholar
  10. Hendricks, E. A., J. R. Moskaitis, Y. Jin, et al., 2011: Prediction and diagnosis of Typhoon Morakot (2009) using the Naval Research Laboratory’s mesoscale tropical cyclone model. Terr. Atmos. Oceanic Sci,. 22, 579–594, doi: 10.3319/TAO.2011.05.30.01(TM).CrossRefGoogle Scholar
  11. Hong, C. -C., M. -Y. Lee, H. -H. Hsu, et al., 2010: Role of submonthly disturbance and 40–50-day ISO on the extreme rainfall event associated with Typhoon Morakot (2009) in southern Taiwan. Geophys. Res. Lett., 37, L08805, doi: 10.1029/2010GL042761.Google Scholar
  12. Hong, S. -Y., J. Dudhia, and S. -H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120, doi: 10.1175/1520-0493(2004)132<0103:ARATIM> 2.0.CO;2.CrossRefGoogle Scholar
  13. Howard, K., M. Splitt, S. Lazarus, et al., 2009: The impact of atmospheric model resolution on a coupled wind/wave forecast system. Preprints, 16th Conference on Air–Sea Interaction, Phoenix, Arizona, Amer. Meteor. Soc., CD-ROM P9.2.Google Scholar
  14. Hu, M., M. Xue, and K. Brewster, 2006a: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Weather Rev., 134, 675–698, doi: 10.1175/MWR3092.1.CrossRefGoogle Scholar
  15. Hu, M., M. Xue, J. Gao, et al., 2006b: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev,. 134, 699–721, doi: 10.1175/MWR3093.1.CrossRefGoogle Scholar
  16. Huang, H. -L., M. -J. Yang, and C. -H. Sui, 2014: Water budget and precipitation efficiency of Typhoon Morakot (2009). J. Atmos. Sci., 71, 112–129, doi: 10.1175/JAS-D-13-053.1.CrossRefGoogle Scholar
  17. Jou, B. J. -D., Y.-C. Yu, F. Lei, et al., 2012: Synoptic environment and rainfall characteristics of Typhoon Morakot (0908). J. Atmos. Sci., 38, 21–38.Google Scholar
  18. Lamberton, N., M. Splitt, S. Lazarus, et al., 2009: Assimilation of nearshore winds into a high-resolution atmosphere/wave modeling system. Preprints, 13th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), Phoenix, Arizona, Amer. Meteor. Soc., CD-ROM 5B.2.Google Scholar
  19. Lee, C. -S., C. -C. Wu, T. -C. C. Wang, et al., 2011: Advances in understanding the “perfect monsoon-influenced typhoon”: Summary from International Conference on Typhoon Morakot (2009). Asia–Pacific J. Atmos. Sci., 47, 213–222, doi: 10.1007/s13143-011-0010-2.CrossRefGoogle Scholar
  20. Lin, I. -I., M. -D. Chou, and C. -C. Wu, 2011: The impact of a warm ocean eddy on Typhoon Morakot (2009): A preliminary study from satellite observations and numerical modelling. Terr. Atmos. Oceanic Sc,i. 22, 661–671, doi: 10.3319/TAO.2011.08.19.01(TM).CrossRefGoogle Scholar
  21. Mlawer, E. J., and S. A. Clough, 1997: On the extension of RRTM to the shortwave region. In Proceedings of the Sixth Atmospheric Measurement (ARM) Science Team Meeting, CONF-9603149, U. S. Department of Energy, Washington, D.C., 223–226.Google Scholar
  22. Nguyen, H. V., and Y. -L. Chen, 2011: High-resolution initialization and simulations of Typhoon Morakot (2009). Mon. Wea. Rev., 139, 1463–1491, doi: 10.1175/2011MWR3505.1.CrossRefGoogle Scholar
  23. Oye, R., C. Mueller, and S. Smith, 1995: Software for radar translation, visualization, editing, and interpolation. Preprints, 29th International Radar Meteor. Conf., AMS, Vail, Colorado, 9–13 July 1995, 359–361.Google Scholar
  24. Schwartz, C. S., Z. Liu, Y. Chen, and X.–Y. Huang, 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of Typhoon Morakot. Wea. Forecasting, 27, 424–437, doi: 10.1175/WAF-D-11-00033.1.CrossRefGoogle Scholar
  25. Skamarock, W. C., and Coauthors, 2008: A Description of the Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN-4751STR, 113 pp.Google Scholar
  26. Stohl, A., C. Forster, A. Frank, et al., 2005: Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys., 5, 4739–4799, doi: 10.5194/acpd-5-4739-2005.CrossRefGoogle Scholar
  27. Wang, C. -C., H. -C. Kuo, Y. -H. Chen, et al., 2012: Effects of asymmetric latent heating on typhoon movement crossing Taiwan: The case of Morakot (2009) with extreme rainfall. J. Atmos. Sci., 69, 3172–3196, doi: 10.1175/JAS-D-11-0346.1.CrossRefGoogle Scholar
  28. Wang, S. -T., 1980: Prediction of the behavior and intensity of typhoons in Taiwan and its vicinity. Research Rep. 018, Taipei, Taiwan, 100 pp. (in Chinese)Google Scholar
  29. Wang, T.-C., Y. -S. Tang, C. -H. Wei, et al., 2010: The precipitation characteristics of Typhoon Morakot (2009) from radar analyses. Chinese J. Atmos. Sci., 38, 39–61. (in Chinese)Google Scholar
  30. Wang, Y., 2009: How do outer spiral rainbands affect tropical cyclone structure and intensity? J. Atmos. Sci., 66, 1250–1273, doi: 10.1175/2008JAS2737.1.CrossRefGoogle Scholar
  31. Wei, C. -H., Y. -C. Chuang, T. -H. Hor, et al., 2014: Dual-Doppler radar investigation of a convective rainband during the impact of the southwesterly monsoonal flow on the circulation of Typhoon Morakot (2009). J. Meteor. Soc. Japan, 92, 363–383, doi: 10.2151/jmsj.2014-406.CrossRefGoogle Scholar
  32. Wu, C. -C., 2001: Numerical simulation of Typhoon Gladys (1994) and its interaction with Taiwan terrain using the GFDL hurricane model. Mon. Wea. Rev., 129, 1533–1549, doi: 10.1175/1520-0493(2001)129<1533:NSOTGA>2.0.CO;2.CrossRefGoogle Scholar
  33. Wu, C. -C., 2013: Typhoon Morakot: Key findings from the Journal TAO for improving prediction of extreme rains at landfall. Bull. Amer. Meteor. Soc., 94, 155–160, doi: 10.1175/BAMSD-11-00155.1.CrossRefGoogle Scholar
  34. Wu, L., J. Liang, and C. -C. Wu, 2011: Monsoonal influence on Typhoon Morakot (2009). Part I: Observational analysis. J. Atmos. Sci., 68, 2208–2221, doi: 10.1175/2011JAS3730.1.Google Scholar
  35. Xie, B., and F. Zhang, 2012: Impacts of Typhoon Track and island topography on the heavy rainfalls in Taiwan associated with Morakot (2009). Mon. Wea. Rev., 140, 3379–3394, doi: 10.1175/MWR-D-11-00240.1.CrossRefGoogle Scholar
  36. Xu, J., and Y. Wang, 2010: Sensitivity of the simulated tropical cyclone inner-core size to the initial vortex size. Mon. Wea. Rev., 138, 4135–4157, doi: 10.1175/2010MWR3335.1.CrossRefGoogle Scholar
  37. Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction tool. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161–193, doi: 10.1007/s007030070003.CrossRefGoogle Scholar
  38. Xue, M., D.-H. Wang, J.-D. Gao, et al., 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139–170, doi: 10.1007/s00703-001-0595-6.CrossRefGoogle Scholar
  39. Yu, C.-K., and L.-W. Cheng, 2013: Distribution and mechanisms of orographic precipitation associated with Typhoon Morakot (2009). J. Atmos. Sci., 70, 2894–2915, doi: 10.1175/JAS-D- 12-0340.1.CrossRefGoogle Scholar
  40. Zhang, F. Q., Y. Weng, Y. -H. Kuo, et al., 2010: Predicting Typhoon Morakot’s catastrophic rainfall with a convectionpermitting mesoscale ensemble system. Wea. Forecasting, 25, 1816–1825, doi: 10.1175/2010WAF2222414.1.CrossRefGoogle Scholar
  41. Zhao, K., and M. Xue, 2009: Assimilation of coastal Doppler radar data with the ARPS 3DVAR and cloud analysis for the prediction of Hurricane Ike (2008). Geophys. Res. Lett., 36, L12803, doi: 10.1029/2009GL038658.CrossRefGoogle Scholar
  42. Zhao, K., X. Li, M. Xue, et al., 2012: Short-term forecasting through intermittent assimilation of data from Taiwan and mainland China coastal radars for Typhoon Meranti (2010) at landfall. J. Geophys. Res,. 117, D06108, doi: 10.1029/2011JD017109.Google Scholar
  43. Zhao, Q., and Y. Jin, 2008: High-resolution radar data assimilation for Hurricane Isabel (2003) at landfall. Bull. Amer. Meteor. Soc., 89, 1355–1372, doi: 10.1175/2008BAMS2562.1.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Xuwei Bao
    • 1
  • Dan Wu
    • 1
  • Xiaotu Lei
    • 1
  • Leiming Ma
    • 1
  • Dongliang Wang
    • 1
  • Kun Zhao
    • 2
  • Ben Jong-Dao Jou
    • 3
  1. 1.Shanghai Typhoon InstituteChina Meteorological AdministrationShanghaiChina
  2. 2.Key Laboratory of Mesoscale Severe Weather/Ministry of Education, and School of Atmospheric SciencesNanjing UniversityNanjingChina
  3. 3.Department of Atmospheric SciencesNational Taiwan UniversityTaipeiChina

Personalised recommendations