Advances in Atmospheric Sciences

, Volume 32, Issue 7, pp 967–978 | Cite as

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

  • Tuanjie Hou
  • Fanyou Kong
  • Xunlai Chen
  • Hengchi Lei
  • Zhaoxia Hu
Article

Abstract

To improve the accuracy of short-term (0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System (HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) three-dimensional variational data assimilation (3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station (AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting (QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score (FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.

Key words

data assimilation radar data heavy rainfall quantitative precipitation forecasting 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585.CrossRefGoogle Scholar
  2. Clark, A. J., and Coauthors, 2012: An overview of the 2010 Hazardous Weather Testbed experimental forecast program Spring Experiment. Bull. Amer. Meteor. Soc., 93, 55–74.CrossRefGoogle Scholar
  3. Cucurull, L., F. Vandenberghe, D. Barker, E. Vilaclara, and A. Rius, 2004: Three-dimensional variational data assimilation of ground-based GPS ZTD and meteorological observations during the 14 December 2001 storm event over the Western Mediterranean Sea. Mon. Wea. Rev., 132, 749–763.CrossRefGoogle Scholar
  4. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107.CrossRefGoogle Scholar
  5. Gallus, W. A., and M. Segal., 2001: Impact of improved initialization of mesoscale features on convective system rainfall in 10-km Eta simulations. Wea. Forecasting, 16, 680–696.CrossRefGoogle Scholar
  6. Gao, J. D., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457–469.CrossRefGoogle Scholar
  7. Gu, J. F., Q. N. Xiao, Y. H. Kuo, D. M. Barker, J. S. Xue, and X. X. Ma, 2005: Assimilation and simulation of typhoon Rusa (2002) using theWRF system. Adv. Atmos. Sci., 22, 415–427, doi:10.1007/BF02918755.CrossRefGoogle Scholar
  8. Ha, J. K., H. W. Kim, and D. K. Lee, 2011: Observation and numerical simulations with radar and surface data assimilation for heavy rainfall over Central Korea. Adv. Atmos. Sci., 28, 573–590, doi: 10.1007/s00376-010-0035-y.CrossRefGoogle Scholar
  9. Hou, T. J., F. Y. Kong, X. L. Chen, and H. C. Lei, 2013: Impact of 3DVAR data assimilation on the prediction of heavy rainfall over Southern China. Advances in Meteorology, doi: 10.1155/2013/129642.Google Scholar
  10. Hu, M., and M. Xue, 2007: Impact of configurations of rapid intermittent assimilation of WSR-88D radar data for the 8 May 2003 Oklahoma City tornadic thunderstorm case. Mon. Wea. Rev., 135, 507–525.CrossRefGoogle Scholar
  11. Hu, M., M. Xue, and K. Brewster, 2006a: 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675–698.CrossRefGoogle Scholar
  12. Hu, M., M. Xue, J. Gao, and K. Brewster, 2006b: 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699–721.CrossRefGoogle Scholar
  13. Janjić, Z. I., 1990: The step-mountain coordinate: Physical package. Mon. Wea. Rev., 118, 1429–1443.CrossRefGoogle Scholar
  14. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181.CrossRefGoogle Scholar
  15. Kain, J. S., and Coauthors, 2010: Assessing advances in the assimilation of radar data and other mesoscale observations within a collaborative forecasting-research environment. Wea. Forecasting, 25, 1510–1521.CrossRefGoogle Scholar
  16. Liu, H., J. Anderson, and Y. H. Kuo, 2012a: Improved analyses and forecasts of Hurricane Ernesto’s genesis using radio occultation data in an ensemble filter assimilation system. Mon. Wea. Rev., 140, 151–166.CrossRefGoogle Scholar
  17. Liu, H. Y., J. S. Xue, J. F. Gu, and H. M. Xu, 2012b: Radar data assimilation of the GRAPES model and experimental results in a typhoon case. Adv. Atmos. Sci., 29, 344–358, doi: 10.1007/s00376-011-1063-y.CrossRefGoogle Scholar
  18. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102, 16 663–16 682.CrossRefGoogle Scholar
  19. Powers, J. G., and K. Gao, 2000: Assimilation of DMSP and TOVS satellite soundings in a mesoscale model. J. Appl. Meteor., 39, 1727–1741.CrossRefGoogle Scholar
  20. Roberts, N. M., and H. W. Lean 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78–97.CrossRefGoogle Scholar
  21. Ruggiero, F. H., K. D. Sashegyi, R. V. Madala, and S. Raman, 1996: The use of surface observations in four-dimensional data assimilation using a mesoscale model. Mon. Wea. Rev., 124, 1018–1033.CrossRefGoogle Scholar
  22. Schenkman, A. D., M. Xue, A. Shapiro, K. Brewster, and J. D. Gao, 2011a: The analysis and prediction of the 8–9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR. Mon. Wea. Rev., 139, 224–246.CrossRefGoogle Scholar
  23. Schenkman, A. D., M. Xue, A. Shapiro, K. Brewster, and J. D. Gao, 2011b: Impact of CASA radar and Oklahoma Mesonet data assimilation on the analysis and prediction of tornadic mesovortices in an MCS. Mon. Wea. Rev., 139, 3422–3445.CrossRefGoogle Scholar
  24. Shen, Y., P. Zhao, Y. Pan, and J. J. Yu, 2014: A high spatiotemporal gauge-satellite merged precipitation analysis over China. J. Geophys. Res., 119, 3063–3075.Google Scholar
  25. Sheng, C., S. Gao, and M. Xue, 2006: Short-range prediction of a heavy precipitation event by assimilating Chinese CINRADSA radar reflectivity data using complex cloud analysis. Meteor. Atmos. Phys., 94, 167–183.CrossRefGoogle Scholar
  26. Stauffer, D. R., N. L. Seaman, and F. S. Binkowski, 1991: Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation within the planetary boundary layer. Mon. Wea. Rev., 119, 734–754.CrossRefGoogle Scholar
  27. Stratman, D. R., M. C. Coniglio, S. E. Koch, and M. Xue, 2013: Use of multiple verification methods to evaluate forecasts of convection from hot-and cold-start convection-allowing models. Wea. Forecasting, 28, 119–138.CrossRefGoogle Scholar
  28. Sun, J. Z., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642–1661.CrossRefGoogle Scholar
  29. Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–5115.CrossRefGoogle Scholar
  30. Xiao, Q. N., Y. H. Kuo, J. Z. Sun, W. C. Lee, D. M. Barker, and E. Lim, 2007: An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall. J. Appl. Meteor. Climatol., 46, 14–22.CrossRefGoogle Scholar
  31. Xue, M., D. H. Wang, J. D. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139–170.CrossRefGoogle Scholar
  32. Xue, M., M. Hu, and A. D. Schenkman, 2014: Numerical prediction of the 8 May 2003 Oklahoma City tornadic supercell and embedded tornado using ARPS with the assimilation of WSR-88D data. Wea. Forecasting, 29, 39–62.CrossRefGoogle Scholar
  33. 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
  34. Zhao, Q. Y., and Y. Jin, 2008: High-resolution radar data assimilation for hurricane Isabel (2003) at landfall. Bull. Amer. Meteor. Soc., 89, 1355–1372.CrossRefGoogle Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Tuanjie Hou
    • 1
    • 2
  • Fanyou Kong
    • 2
  • Xunlai Chen
    • 2
    • 3
    • 4
  • Hengchi Lei
    • 1
    • 5
  • Zhaoxia Hu
    • 1
  1. 1.Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Center for Analysis and Prediction of StormsUniversity of OklahomaNormanUSA
  3. 3.Shenzhen Key Laboratory of Severe Weather in South ChinaShenzhenChina
  4. 4.Shenzhen Meteorological BureauShenzhenChina
  5. 5.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina

Personalised recommendations