摘要
利用WRF(Weather research and forecasting)模式及模式模拟的资料,采用Hybrid ETKF-3DVAR(ensemble transform Kalman filter-three-dimensional variational data assimilation)方法同化模拟雷达观测资料。该混合同化方法将集合转换卡尔曼滤波(ensemble transform Kalman filter)得到的集合样本扰动通过转换矩阵直接作用到背景场上,利用顺序滤波的思想得到分析扰动场;然后通过增加额外控制变量的方式把"流依赖"的集合协方差信息引入到变分目标函数中去,在3DVAR框架基础下与观测数据进行融合,从而给出分析场的最优估计。试验结果表明,Hybrid ETKF-3DVAR同化方法相比传统3DVAR可以提供更为准确的分析场,Hybrid方法雷达资料初始化模拟的台风涡旋结构与位置比3DVAR更加接近"真实场",对台风路径预报也有明显改进。通过对比Hybrid S试验与Hybrid F试验发现,Hybrid的正效果主要来源于混合背景误差协方差中的"流依赖"信息,集合平均场代替确定性背景场带来的效果并不显著。
The hybrid ensemble transform Kalman filter—three-dimensional variational data assimilation（ Hybrid ETKF-3DVAR） method is used to assimilate the simulated Doppler radial velocity observations based on Weather research and forecasting（ WRF） model. The hybrid scheme updates the ensemble mean using a hybrid ensemble and static background-error covariance on the basis of 3DVAR framew ork. The ensemble perturbations in the hybrid scheme are updated by the ETKF scheme,w hich updates the background perturbation through a transform matrix. The results show that Hybrid ETKF-3DVAR provides more accurate analysis than traditional 3DVAR. Additionally,significant positive impact from the hybrid data assimilation is found in vortex structure and position as w ell as the track forecast. It is found that such positive improvements are mostly provided by the flow-dependent covariance other than the use of ensemble mean by comparing the results from 3DVAR and the Hybrid S experiment,w hich uses static background-error covariance and ensemble mean as the first guess.
出处
《大气科学学报》
CSCD
北大核心
2016年第1期81-89,共9页
Transactions of Atmospheric Sciences
基金
国家重点基础研究计划(973计划)项目(OPPAC-2013CB430102)
国家自然科学基金资助项目(41430427
41375025
41205082
41505089)
江苏省气象局北极阁基金项目(BJG201510)
