期刊文献+

Assimilating AMSU-A Radiance Data with the WRF Hybrid En3DVAR System for Track Predictions of Typhoon Megi(2010) 被引量:5

Assimilating AMSU-A Radiance Data with the WRF Hybrid En3DVAR System for Track Predictions of Typhoon Megi(2010)
下载PDF
导出
摘要 The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A(AMSU-A) on the track prediction of Typhoon Megi(2010) was studied using the Weather Research and Forecasting(WRF) model and a hybrid ensemble threedimensional variational(En3DVAR) data assimilation(DA) system.The influences of tuning the length scale and variance scale factors related to the static background error covariance(BEC) on the track forecast of the typhoon were studied.The results show that,in typhoon radiance data assimilation,a moderate length scale factor improves the prediction of the typhoon track.The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts,even when the static BEC was carefully tuned to optimize its performance.When the hybrid DA was employed,the track forecast was significantly improved,especially for the sharp northward turn after crossing the Philippines,with the flow-dependent ensemble covariance.The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically.The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR.Additionally,for 24 h forecasts,the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels. The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A(AMSU-A) on the track prediction of Typhoon Megi(2010) was studied using the Weather Research and Forecasting(WRF) model and a hybrid ensemble threedimensional variational(En3DVAR) data assimilation(DA) system.The influences of tuning the length scale and variance scale factors related to the static background error covariance(BEC) on the track forecast of the typhoon were studied.The results show that,in typhoon radiance data assimilation,a moderate length scale factor improves the prediction of the typhoon track.The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts,even when the static BEC was carefully tuned to optimize its performance.When the hybrid DA was employed,the track forecast was significantly improved,especially for the sharp northward turn after crossing the Philippines,with the flow-dependent ensemble covariance.The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically.The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR.Additionally,for 24 h forecasts,the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels.
出处 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2015年第9期1231-1243,共13页 大气科学进展(英文版)
基金 supported by the National Fundamental 973 Research Program of China(Grant No.OPPAC-2013CB430102) Natural Science Foundation of China(41375025) the Priority Academic Program Development(PAPD) of Jiangsu Higher Education Institutions
关键词 data assimilation RADIANCE observation operator data assimilation radiance observation operator
  • 相关文献

参考文献1

二级参考文献33

  • 1Meng, Z., and F. Zhang, 2007: Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments. Mon. Wea. Rev., 135, 1403- 1423.
  • 2Meng, Z., and F. Zhang, 2008a: Test of an ensemble-Kahnan filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3Dvar in a real-data case study. Mort. Wea. Rev., 136, 522-540.
  • 3Meng, Z., and F. Zhang, 2008b: Test of an ensemble-Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Performance over a warmseason month of June 2003. Mort. Wea. Rev., 136, 3671 -3682.
  • 4Navon, I. M., D. N. Daescu, and Z. Liu, 2005: The impact of background error on incomplete observations for 4D-Var data assimilation with the FSU GSM. Computational Science-ICCS 2005, PT 2, 3515, 837 844.
  • 5R.abier, F., J. N. Thepaut, and P. Courtier, 1998: Extended assimilation and forecast experiments with a four-dimensional variational assimilation system. Quart. J. Roy. Meteor. Soc., 124, 1861- 1887.
  • 6Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mort. Wea. Rev., 131, 1663-1677.
  • 7Sun, J., 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.
  • 8Talagrand, O., 1997: Assimilation of observations, an introduction. J. Meteor. Soc. Japan, 75, 191-209.
  • 9Wang, X., C. Snyder, and T. M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble- 3DVAtt hybrid analysis schemes. Mon. Wea. Rev., 135, 222-227.
  • 10Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mort. Wea. Rev., 130, 1923.

共引文献25

同被引文献24

引证文献5

二级引证文献20

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部