Advances in Atmospheric Sciences

, Volume 35, Issue 7, pp 813–825 | Cite as

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

  • Chaoqun Ma
  • Tijian Wang
  • Zengliang Zang
  • Zhijin Li
Original Paper


Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation (DA) and model output statistics (MOS). However, the relative importance and combined effects of the two techniques have not been clarified. Here, a one-month air quality forecast with the Weather Research and Forecasting-Chemistry (WRF-Chem) model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3DVar DA in improving the operational forecasting ability of WRF-Chem.

Key words

data assimilation model output statistics WRF-Chem operational forecast 


因为大气化学模式中存在各种不确定性, 所以其预报冬季污染的能力较差. 一般来说, 可以使用数据同化或者模式后处理技术来大幅减小这些不确定性. 然而, 对于这两项技术的相对重要性以及同时使用的效果, 目前还缺乏足够的研究. 因此, 本文尝试用Weather Research and Forecasting–Chemistry (WRF-Chem) 模型进行了为期一个月的预报实验. 预报集中关注了中国河北省地区且以近似业务预报的形式展开. 在预报期间, 我们在不同的实验中分别使用了基于三维变分的数据同化和基于一维卡尔曼滤波的模式后处理技术, 以比较它们在改善模式预报上的性能. 在另一个实验中, 我们还同时使用了这两种技术. 预报结果与观测的对比表明: 使用了模式后处理技术的化学预报比使用了三维变分同化的表现要好, 并且这种优势体现在所有被测物种和整个72小时预报上. 另外, 同时使用两种技术的预报结果并不一定比只使用模式后处理的要好, 不过前者和后者的差距很小而且没有特定的规律. 综上所述, 模式后处理技术比三维变分数据同化更适合被用于提升WRF-Chem模式的业务预报能力.


数据同化 模式后处理 WRF-Chem 业务预报 


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Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Chaoqun Ma
    • 1
    • 2
    • 3
  • Tijian Wang
    • 1
    • 2
    • 3
  • Zengliang Zang
    • 4
  • Zhijin Li
    • 5
  1. 1.School of Atmospheric SciencesNanjing UniversityNanjingChina
  2. 2.CMA-NJU Joint Laboratory for Climate Prediction StudiesNanjingChina
  3. 3.Jiangsu Collaborative Innovation Center for Climate ChangeNanjingChina
  4. 4.Institute of Meteorology and OceanographyPLA University of Science and TechnologyNanjingChina
  5. 5.Joint Institute for Regional Earth System Science and EngineeringUniversity of CaliforniaLos AngelesUSA

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