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基于集合的观测资料影响性评价——简单AGCM的理想试验 被引量:1

Ensemble-Based Estimation of Observation Impact-Simplified AGCM Perfect Experiments
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摘要 在Liu and Kalnay(2008)的研究基础上,将基于集合的观测资料影响性评价方法(简称LK08法)运用到一个简单的大气环流模式中,对模拟探空资料的预报影响性进行了综合评价,考察了LK08法在真实大气环流模式上的适用性。研究结果表明,应用基于集合的评价方法可以一次性计算出同化系统中每个观测的影响性,然后按观测手段、观测区域等进行影响性数值的简单累加,以此可以比较不同类型观测的相对影响性。比较结果显示,不同半球的模拟探空观测对预报的总影响性相差不大,但由于南半球资料个数要远远少于北半球,因此,南半球单个观测的影响性要大于北半球的单个观测。不同观测类型对预报的总影响性也不相同。有效性验证分析表明,按LK08法计算得到的总体观测影响性能解释实际影响性的70%~80%,且很好地抓住了其变化和走势。 Experiments of assessing observation impact with a simplified parameterization AGCM model are carried out to examine how the ensemble-based estimation method proposed by Liu and Kalnay(2008)(LK08)can be applied to the state-of-art models.The results show that LK08 can successfully estimate each observation's impact at once and then the impact values can be simply summed and grouped according to various types of observations or different areas.Although the summed observation impacts for the two hemispheres are similar,the individual observation impact in the Southern Hemisphere is much larger than that in the Northern Hemisphere due to the sparse observations there.The impacts for different observational types are also different.The estimated total observation impact accounts for 70%-80% of the actual impact and captures the variations of actual impact very well.
出处 《大气科学》 CSCD 北大核心 2010年第4期793-801,共9页 Chinese Journal of Atmospheric Sciences
基金 国家自然科学基金资助项目40975067 科技部公益性行业(气象)科研专项GYHY200806029 上海市气象局科技开发项目MS200806
关键词 观测资料影响性 集合 局地集合变换卡尔曼滤波(LETKF) SPEEDY模式 observation impact ensemble local ensemble transform Kalman filter(LETKF) SPEEDY model
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参考文献18

  • 1Chahine M T,Pagano T S,Aumann H H,et al.2006.AIRS:Im-proving weather forecasting and providing new data on greenhouse gases[J].Bull.Amer.Meteor.Soc.,87,911-926.
  • 2Danforth C M,Kalnay E,Miyoshi T,2007.Estimating and correc-ting global weather model error[J].Mon.Wea.Rev.,135 (2):281-299.
  • 3Goldberg MD.Qu Y N,McMillin L M,et al.2003.AIRS near-re-al-time products and algorithms in support of operational numeri-cal weather prediction[J].IEEE Trans.Geosci.Remote Sens.,41 (2):379-389.
  • 4Hunt B R,Kostelich E J,Szunyogh I.2007.Efficient data assimila-tion for spatiotemporal chaos.A local ensemble transform Kalman filter[J].Physica.D:Nonlinear Phenomena,230; 112-126.
  • 5Langland R H,Baker N L.2004.Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system[J].Tellus,56:189-201.
  • 6Li H,Kalnay E,Miyoshi T,et al.2009a.Accounting for model er-rors in ensemble data assimilation[J].Mon.Wea.Rev.,137,3407-3419.
  • 7Li H,Kalnay E,Miyoshi T.2009b.Simultaneous estimation of co-variance inflation and observation errors within an ensemble Kal-man filter[J].Quart.J.Roy.Meteor.Soc.,135:523-533.
  • 8Liu H,Zou X,2001.The impact of NORPEX targeted dropsondes on the analysis and 2-3-day forecasts of a landfalling Pacific win-ter storm using NCEP 3DVAR and 4DVAR systems[J].Moa Wea.Rev.,129 (8):1987-2004.
  • 9Liu J J,Kalnay E.2008.Estimating observation impact without ad-joint model in an ensemble Kalman filter[J].Quart.J.Roy.Meteor.Soc,134(634):1327-1335.
  • 10Le Marshall J,Jung J,Derber J,et al.2006.Improving global a-nalysis and forecasting with AIRS[J].Bull.Amer.Meteor.Soc.,87.891-894.

二级参考文献44

  • 1Kasahara A, Balgovind R C, Katz t3. Use of satellite radiometric imagery data for improvement in the analysis of divergent wind in the tropics. Mon. Wea. Rev. , 1988, 116: 866~883.
  • 2Sun J, Crook N A. 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. , 1997, 54:1642~1661.
  • 3Sun J, Crook N A. Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part Ⅱ: Retrieval experiments of an observed Florida convective storm. J. Atznos. Sci., 1998, 55: 835~852.
  • 4Sun J, Crook N A. Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting,2001, 16:117~132.
  • 5Synder C, Zhang F. Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea.Rev., 2003, 131:1663~1677.
  • 6Zhang F, Synder C, Sun J. Impacts of initial estimate and observations on the convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev. , 2004, 132:1238~1253.
  • 7Tong M, Xue M. Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSSE experiments. Mon. Wea. Rev. , 2005, 133:1789~1807.
  • 8Qiu C J, Xu Oo A simple adjoint method of wind analysis for single-Doppler data. J. Atmos. Oceanic Technol. , 1992, 9~588~598.
  • 9Qiu C J, Xu Q. A spectral simple adjoint method for retrieving low-altitude winds from single-Doppler data. J. Atmos.Oceanic Technol. , 1994, 11:927~936.
  • 10Gao J D, Xue M, Shapiro A, et al. Three-dimensional simple adjoint velocity retrievals from single-Doppler radar. J. Atmos. Oceanic Technol., 2001, 18:26~38.

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