Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system
Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system
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DOI：
10.5194/gmd-9-965-2016
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发表时间：
2015-09
影响因子：
5.1
通讯作者：
A. Mizzi;A. Arellano;D. Edwards;Jeffrey L. Anderson;G. Pfister
中科院分区：
文献类型：
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作者：
A. Mizzi;A. Arellano;D. Edwards;Jeffrey L. Anderson;G. Pfister
抽象的。本文介绍了WRF-Chem/DART化学输送预报/资料同化试验台（Weather Research and Forecasting Model with Chemistry/Data Assimilation Research Testbed，简称WRF-Chem/DART）系统，以及对卫星大气成分产品的紧致相空间反演资料的同化。WRF-Chem是一个最先进的化学传输模型。DART是一个灵活的软件环境，用于研究不同同化和预报模式选项的集合数据同化。DART的主要同化工具是集合调整卡尔曼滤波器。将WRF-Chem/DART应用于Terra/对流层污染测量（MOPITT）一氧化碳（CO）痕量气体反演廓线的同化。这些CO观测首先被同化为准最优检索（QOR）。我们的研究结果表明，同化的CO反演（i）减少WRF-Chem的CO偏差在检索和状态空间，（ii）提高CO预报技巧，减少均方根误差（RMSE）和增加的决定系数（R2）。这些CO预测改善在95%的水平上是显著的。痕量气体反演数据集包含（i）大量的数据，每次观测的信息量有限，（ii）误差协方差互相关，（iii）来自反演先验分布的贡献，在同化之前应该被删除。这些特点对检索的同化提出了挑战。本文通过引入紧凑相空间反演（CPSRs）的同化来解决这些挑战。CPSR是通过预处理检索数据集的算法，（i）压缩检索数据，（ii）对角化的误差协方差，和（iii）删除检索前配置文件的贡献。大多数现代集合同化算法可以有效地同化CPSR。我们的研究结果表明，同化的MOPITT CO CPSRs减少的观测数量（和同化计算成本）的10.35%，同时提供CO预报的改进与同化的MOPITT CO QOR相比或更好。
Abstract. This paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of compact phase space retrievals of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (R2). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by ∼ 35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.