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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2020-223
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-223
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 31 Jul 2020

Submitted as: development and technical paper | 31 Jul 2020

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF-Chem model v3.9.1 and its application in PM2.5 forecasts across China

Yanfei Liang1,2, Zengliang Zang1, Dong Liu3, Peng Yan4, Yiwen Hu5, Yan Zhou6, and Wei You1 Yanfei Liang et al.
  • 1Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing, China
  • 2No.32145 Unit of PLA, Xinxiang, China
  • 3Key Laboratory of Atmospheric Optics, Institute of Optics & Fine Mechanics, Chinese Academy of Sciences, Hefei, China
  • 4Meteorological Observation Center, Chinese Meteorological Administration, Beijing, China
  • 5Nanjing University of Information Science & Technology, Nanjing, China
  • 6No.78127 Unit of PLA, Beijing, China

Abstract. For the aerosol variables in the model for simulating aerosol interactions and chemistry (MOSAIC)-4bin chemical scheme in the Weather Research and Forecasting–Chemistry (WRF–Chem) model, this study presents an observation forward aerosol extinction coefficient (AEC) and aerosol mass concentration (AMC) operator and corresponding adjoint based on the interagency monitoring of protected visual environments (IMPROVE) equation, and then a three-dimensional variational (3-DVAR) data assimilation system (DA) is developed for lidar AECs and AMCs. DA experiments are conducted based on AEC profiles measured by five light detection and ranging (lidar) systems as well as mass concentration (MC) data measured at over 1,500 ground environmental monitoring stations across China for particulate matter 2.5 µm or less in diameter (PM2.5) and PM between 2.5 and 10 µm in diameter (PM10). An experiment comparing assimilated and without assimilated measurements finds the following. While only five lidars were available within the simulation region (approximately 2.33 million km2 in size), assimilating lidar AEC data alone can effectively improve the accuracy of the initial field of the WRF–Chem as well as its forecast performance for PM2.5MCs. Compared to the without assimilated experiment, DA reduces the root mean square error of surface PM2.5MCs in the initial field of the model by 10.5 μg/m3 (17.6 %). Moreover, the positive effect resulting from the optimization of the initial field for AMCs can last for more than 24 h. By taking advantage of lidar aerosol vertical profile information and the near-surface PM MC observations, assimilating lidar AEC and surface PM2.5 (PM10) simultaneously can effectively integrate their observed information and generate a more accurate 3D aerosol analysis field.

Yanfei Liang et al.

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Yanfei Liang et al.

Yanfei Liang et al.

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