Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1821-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1821-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations
Daichun Wang
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410005, China
Wei You
CORRESPONDING AUTHOR
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410005, China
Zengliang Zang
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410005, China
Xiaobin Pan
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410005, China
Yiwen Hu
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410005, China
School of Atmospheric Physics, Nanjing University of Information
Science & Technology, Nanjing, 211101, China
Yanfei Liang
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410005, China
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
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This study uses WRF-Chem to assess how meteorological conditions and emission reductions affected fine particulate matter (PM2.5) in the North China Plain (NCP). It highlights regional disparities: in the northern NCP, adverse weather negated emission reduction effects. In contrast, the southern NCP featured a PM2.5 decrease due to favorable weather and emission reductions. The research highlighted the interaction between emissions, meteorology, and PM2.5.
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This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF–Chem to optimise SO2 emissions. The 4DVAR system was applied to obtain the SO2 emissions during the early period of the COVID-19 pandemic over China. The results showed that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
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We develop a new inversion method of emission sources based on sensitivity analysis and the three-dimension variational technique. The novel explicit observation operator matrix between emission sources and the receptor’s concentrations is established. Then this method is applied to a typical heavy haze episode in North China, and spatiotemporal variations of SO2, NO2, and O3 concentrations simulated using a posterior emission sources are compared with results using an a priori inventory.
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Short summary
This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
This paper presents a 3D variational data assimilation system for aerosol optical properties,...