流依赖球面小波背景误差协方差模型设计和初步试验

基于资料同化集合设计了流依赖球面小波背景场误差协方差模型中背景误差方差和局地垂直相关协方差的统计计算方法。为了提高背景误差方差的估计精度,采用客观滤波技术来减少因集合样本个数不足而引入的随机取样噪声。最后在银河四维变分同化业务系统(YH4DVar)上设计了集合资料同化的试验系统,以流依赖背景误差方差为重点验证了模型的有效性。结果表明:基于流依赖球面小波背景误差协方差模型能够有效估计出随天气状态变化的背景场误差方差,对台风等剧烈变化的天气过程的同化分析和预报都具有一定的正效果。 

Development and preliminary application of a wavelet flow-dependent background error covariance model

Accurate specification of the background error covariance matrix (B) is a fundamental prerequisite for a successful data assimilation scheme. However, most of operational variational data assimilation systems still rely on a static, climatological representation of the B, and thus implicitly renounce the representation of the flow-dependent covariance errors. To overcome this limitation, an Ensemble Data Assimilations (EDA) approach and spherical wavelet are used in this study to specify the flow-dependent background error variance and covariance respectively. In this model, both the background error variance and the vertical localization covariance matrix are replaced by that estimated from ensemble samples. Meanwhile, the EDA system proposed here is used to provide estimates of day-to-day background error statistics, which can represent the current meteorological situation through an ensemble of 10 lower-resolution four dimensional variational data assimilation(4DVar) analysis cycles that make use of perturbed observations, perturbed sea surface temperature (SST) fields and perturbed model physical tendencies. However, a key problem is that the sample variances computed from the EDA are affected by the random, which needs to be addressed. In this paper, objective filtering has been implemented due to its effectiveness in extracting the statistically significant part of the signal. This study also evaluates the ability of the EDA in representing flow-dependent background-error variances. In particular, the ensemble-based variances are examined in the case of hurricane KEBI, which is the 9th most intense hurricane that occurred in August 2013. Results show that, the EDA system with 10 members is able to correct errors in large scale structures associated with tropical cyclogenesis and produce a realistic estimation of the background error that varies with weather condition effectively and accurately. In this sense, the objective filtering technique provides a useful indication of the spatial scales the ensemble is able to resolve in a statistically robust way. Another interesting aspect is that the use of flow-dependent background error covariance shows some positive impacts on the analysis and forecast of this tremendously varying weather processes.