Comparing Adaptive Prior and Posterior Inflation for Ensemble Filters Using an Atmospheric General Circulation Model
Comparing Adaptive Prior and Posterior Inflation for Ensemble Filters Using an Atmospheric General Circulation Model
复制标题
DOI：
10.1175/mwr-d-18-0389.1
复制
发表时间：
2019-07-01
影响因子：
3.2
通讯作者：
Wang, Xuguang
中科院分区：
文献类型：
--
作者：
El Gharamti, Mohamad;Raeder, Kevin;Wang, Xuguang
采样误差和模型误差是集合卡尔曼滤波器的主要缺点。抽样误差的出现是因为使用了有限的集合规模，而模型误差是动态和基本参数化中的缺陷，可能会在模型的预测中产生偏差。在这项研究中，我们提出了一个新的时间自适应后验膨胀算法，在该算法中，所分析的集合异常是局部膨胀的。所提出的膨胀策略是计算效率高的，其目的是在同化观测后恢复足够的分析集合中的扩散。该方案的性能进行了测试，对松弛先验分布（RTPS）和自适应先验膨胀。为此，使用了两个模式：三变量Lorenz 63系统和社区大气模式（CAM）。在CAM中，从几个来源的全球气候活动，温度和风观测被纳入使用数据同化研究试验台（DART）进行一组同化实验。所提出的计划，以产生更好的质量预测比RTPS。同化结果进一步表明，当模型误差很小时，先验和后验通货膨胀都能够减轻采样误差，并略优于后验通货膨胀。当大的模型误差，如风和温度偏差，存在，事先通货膨胀被证明是更准确的比后通货膨胀。在北方半球的密集观察区域提出了许多挑战后验膨胀算法。一个令人信服的增强滤波器的性能是通过结合两个自适应膨胀计划。
Sampling errors and model errors are major drawbacks from which ensemble Kalman filters suffer. Sampling errors arise because of the use of a limited ensemble size, while model errors are deficiencies in the dynamics and underlying parameterizations that may yield biases in the model's prediction. In this study, we propose a new time-adaptive posterior inflation algorithm in which the analyzed ensemble anomalies are locally inflated. The proposed inflation strategy is computationally efficient and is aimed at restoring enough spread in the analysis ensemble after assimilating the observations. The performance of this scheme is tested against the relaxation to prior spread (RTPS) and adaptive prior inflation. For this purpose, two model are used: the three-variable Lorenz 63 system and the Community Atmosphere Model (CAM). In CAM, global refractivity, temperature, and wind observations from several sources are incorporated to perform a set of assimilation experiments using the Data Assimilation Research Testbed (DART). The proposed scheme is shown to yield better quality forecasts than the RTPS. Assimilation results further suggest that when model errors are small, both prior and posterior inflation are able to mitigate sampling errors with a slight advantage to posterior inflation. When large model errors, such as wind and temperature biases, are present, prior inflation is shown to be more accurate than posterior inflation. Densely observed regions as in the Northern Hemisphere present numerous challenges to the posterior inflation algorithm. A compelling enhancement to the performance of the filter is achieved by combining both adaptive inflation schemes.