Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5
Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5
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DOI：
10.5194/gmd-15-2773-2022
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发表时间：
2022-04
影响因子：
5.1
通讯作者：
Soon-Young Park;U. Dash;Jinhyeok Yu;K. Yumimoto;I. Uno;C. Song
中科院分区：
文献类型：
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作者：
Soon-Young Park;U. Dash;Jinhyeok Yu;K. Yumimoto;I. Uno;C. Song
抽象的。在这项研究中，我们开发了一个数据同化（DA）系统的化学输送模式（CTM）的模拟使用集合卡尔曼滤波（EnKF）技术。这种DA技术很容易实现在现有的系统中，而不严重修改原始的CTM，并可以提供流量依赖的校正误差协方差的基础上，短期合奏传播。首先，在2016年5月1日至6月12日的KORUS-AQ活动期间，在韩国上空每6小时将地面站的PM2.5观测值同化在该DA系统中。与EnKF的DA性能进行了比较，没有DA的控制运行（CTR）和运行与三维变分（3D-Var）DA。一致的改进，由于同化与EnKF的初始条件（IC）中发现DA实验在6小时的间隔相比，CTR运行和运行与3D-Var。此外，我们试图同化来自中国的地面观测，以研究改善边界条件（BCs）对韩国PM2.5可预测性的影响。还量化了IC和BC对改善PM2.5可预测性的贡献。例如，6 h再分析运行的归一化平均偏倚（NMB）相对降低约为27.2%。每天00：00 UTC使用优化的IC进行一系列24小时PM2.5预测。当在UTC 00：00应用更新的IC时，对于24小时预测运行，NMB的相对减少为17.3%。这意味着在应用更新的BC后，韩国24小时PM2.5预测的NMB额外减少了9.0%。
Abstract. In this study, we developed a data assimilation (DA) system for chemical transport model (CTM) simulations using an ensemble Kalman filter (EnKF) technique. This DA technique is easy to implement in an existing system without seriously modifying the original CTM and can provide flow-dependent corrections based on error covariance by short-term ensemble propagations. First, the PM2.5 observations at ground stations were assimilated in this DA system every 6 h over South Korea for the period of the KORUS–AQ campaign from 1 May to 12 June 2016. The DA performances with the EnKF were then compared to a control run (CTR) without DA and a run with three-dimensional variational (3D-Var) DA. Consistent improvements owing to the initial conditions (ICs) assimilated with the EnKF were found in the DA experiments at a 6 h interval compared to the CTR run and to the run with 3D-Var. In addition, we attempted to assimilate the ground observations from China to examine the impacts of improved boundary conditions (BCs) on the PM2.5 predictability over South Korea. The contributions of the ICs and BCs to improvements in the PM2.5 predictability were also quantified. For example, the relative reductions in terms of the normalized mean bias (NMB) were found to be approximately 27.2 % for the 6 h reanalysis run. A series of 24 h PM2.5 predictions were additionally conducted each day at 00:00 UTC with the optimized ICs. The relative reduction of the NMB was 17.3 % for the 24 h prediction run when the updated ICs were applied at 00:00 UTC. This means that after the application of the updated BCs, an additional 9.0 % reduction in the NMB was achieved for 24 h PM2.5 predictions in South Korea.