为了验证风云三号D星MERSI传感器的气溶胶光学厚度(AOD)数据对地面PM_(2.5)的污染过程预报的效果,本文基于WRF-Chem(Weather Research and Forecasting model coupled with Chemistry)大气化学模式和三维变分同化方法,针对2020-02-10—2...为了验证风云三号D星MERSI传感器的气溶胶光学厚度(AOD)数据对地面PM_(2.5)的污染过程预报的效果,本文基于WRF-Chem(Weather Research and Forecasting model coupled with Chemistry)大气化学模式和三维变分同化方法,针对2020-02-10—2020-02-12中国北方地区的一次PM_(2.5)重污染过程,进行了同化和预报试验研究。同化数据来自常规地面站点的PM_(2.5)浓度数据和风云三号D星MERSI传感器的气溶胶光学厚度(AOD)数据。控制试验不同化任何资料,3组同化试验分别为仅同化地面PM_(2.5),仅同化卫星AOD,以及同时同化PM_(2.5)和卫星AOD两种资料。结果表明,3组同化试验都可以有效提高初始场准确率,以地面PM_(2.5)作为检验标准,仅同化PM_(2.5)、仅同化AOD、同时同化两种资料相对于控制试验,初始场的平均偏差分别降低54.9%、21.9%和49.0%,平均相关系数分别提升51.4%、16.0%和34.0%,平均均方根误差分别降低50.6%、17.2%和42.3%。以卫星AOD作为检验标准,3组同化试验相对于控制试验,初始场的平均偏差分别降低37.6%、78.4%和83%,平均均方根误差分别降低31.6%、62.2%和65.2%。同化后的初始场对预报有显著的改进,改进持续时间达24 h,以地面PM_(2.5)作为检验标准,同时同化两种资料的试验对24 h预报的平均偏差减少19.7%,相关系数提升8.8%,均方根误差减少17.2%;以卫星AOD作为检验标准,24 h预报的平均偏差减少40.1%,相关系数提升25.9%,均方根误差降低34.7%。试验结论为,相对于仅同化地面PM_(2.5)资料,同化风云卫星AOD资料可以提升后期预报效果。展开更多
This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations.We present a hybrid three-dimensional variation（3DVar） and particle piltering（PF） method,which combine...This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations.We present a hybrid three-dimensional variation（3DVar） and particle piltering（PF） method,which combines the advantages of 3DVar and particle-based filters.By minimizing the cost function,this approach will produce a better proposal distribution of the state.Afterwards the stochastic resampling step in standard PF can be avoided through a deterministic scheme.The simulation results show that the performance of the new method is superior to the traditional ensemble Kalman filtering（EnKF） and the standard PF,especially in highly nonlinear systems.展开更多
In order to solve the so-called ＂bull-eye＂ problem caused by using a simple bilinear interpolation as an observational mapping operator in the cost function in the multigrid three-dimensional variational （3DVAR） d...In order to solve the so-called ＂bull-eye＂ problem caused by using a simple bilinear interpolation as an observational mapping operator in the cost function in the multigrid three-dimensional variational （3DVAR） data assimilation scheme, a smoothing term, equivalent to a penalty term, is introduced into the cost function to serve as a means of troubleshooting. A theoretical analysis is first performed to figure out what on earth results in the issue of ＂bull-eye＂, and then the meaning of such smoothing term is elucidated and the uniqueness of solution of the multigrid 3DVAR with the smoothing term added is discussed through the theoretical deduction for one-dimensional （1D） case, and two idealized data assimilation experiments （one- and two-dimensional （2D） cases）. By exploring the relationship between the smoothing term and the recursive filter theoretically and practically, it is revealed why satisfied analysis results can be achieved by using such proposed solution for the issue of the multigrid 3DVAR.展开更多
文摘为了验证风云三号D星MERSI传感器的气溶胶光学厚度(AOD)数据对地面PM_(2.5)的污染过程预报的效果,本文基于WRF-Chem(Weather Research and Forecasting model coupled with Chemistry)大气化学模式和三维变分同化方法,针对2020-02-10—2020-02-12中国北方地区的一次PM_(2.5)重污染过程,进行了同化和预报试验研究。同化数据来自常规地面站点的PM_(2.5)浓度数据和风云三号D星MERSI传感器的气溶胶光学厚度(AOD)数据。控制试验不同化任何资料,3组同化试验分别为仅同化地面PM_(2.5),仅同化卫星AOD,以及同时同化PM_(2.5)和卫星AOD两种资料。结果表明,3组同化试验都可以有效提高初始场准确率,以地面PM_(2.5)作为检验标准,仅同化PM_(2.5)、仅同化AOD、同时同化两种资料相对于控制试验,初始场的平均偏差分别降低54.9%、21.9%和49.0%,平均相关系数分别提升51.4%、16.0%和34.0%,平均均方根误差分别降低50.6%、17.2%和42.3%。以卫星AOD作为检验标准,3组同化试验相对于控制试验,初始场的平均偏差分别降低37.6%、78.4%和83%,平均均方根误差分别降低31.6%、62.2%和65.2%。同化后的初始场对预报有显著的改进,改进持续时间达24 h,以地面PM_(2.5)作为检验标准,同时同化两种资料的试验对24 h预报的平均偏差减少19.7%,相关系数提升8.8%,均方根误差减少17.2%;以卫星AOD作为检验标准,24 h预报的平均偏差减少40.1%,相关系数提升25.9%,均方根误差降低34.7%。试验结论为,相对于仅同化地面PM_(2.5)资料,同化风云卫星AOD资料可以提升后期预报效果。
基金Project supported by the National Natural Science Foundation of China (Grant No. 41105063)
文摘This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations.We present a hybrid three-dimensional variation（3DVar） and particle piltering（PF） method,which combines the advantages of 3DVar and particle-based filters.By minimizing the cost function,this approach will produce a better proposal distribution of the state.Afterwards the stochastic resampling step in standard PF can be avoided through a deterministic scheme.The simulation results show that the performance of the new method is superior to the traditional ensemble Kalman filtering（EnKF） and the standard PF,especially in highly nonlinear systems.
基金The National Basic Research Program of China under contract No. 2013CB430304the National High-Tech R&D Program of China under contract No. 2013AA09A505the National Natural Science Foundation of China under contract Nos 41030854,40906015,40906016,41106005 and 41176003
文摘In order to solve the so-called ＂bull-eye＂ problem caused by using a simple bilinear interpolation as an observational mapping operator in the cost function in the multigrid three-dimensional variational （3DVAR） data assimilation scheme, a smoothing term, equivalent to a penalty term, is introduced into the cost function to serve as a means of troubleshooting. A theoretical analysis is first performed to figure out what on earth results in the issue of ＂bull-eye＂, and then the meaning of such smoothing term is elucidated and the uniqueness of solution of the multigrid 3DVAR with the smoothing term added is discussed through the theoretical deduction for one-dimensional （1D） case, and two idealized data assimilation experiments （one- and two-dimensional （2D） cases）. By exploring the relationship between the smoothing term and the recursive filter theoretically and practically, it is revealed why satisfied analysis results can be achieved by using such proposed solution for the issue of the multigrid 3DVAR.
