Tao Zhang, Zijiang Zhou, Zhisen Zhang, Junting Zhong, Zhiquan Liu, Xiaoye Zhang, Wenhui Xu, Lipeng Jiang, Jie Liao, Yaping Ma, Yike Zhou, Huiying Wang, Jie Chen, Lu Zhang, Yan Yao, Hui Jiang, Wenjing Jiang
{"title":"CMA-ChemRA:中国区域弱耦合化学-天气再分析系统及其产品自 2007 年以来的发展情况。","authors":"Tao Zhang, Zijiang Zhou, Zhisen Zhang, Junting Zhong, Zhiquan Liu, Xiaoye Zhang, Wenhui Xu, Lipeng Jiang, Jie Liao, Yaping Ma, Yike Zhou, Huiying Wang, Jie Chen, Lu Zhang, Yan Yao, Hui Jiang, Wenjing Jiang","doi":"10.1016/j.scitotenv.2024.177552","DOIUrl":null,"url":null,"abstract":"<p><p>The CMA-ChemRA (China Regional Weakly Coupled Chemical-Weather Reanalysis System) was developped using China's first-generation global atmospheric reanalysis product (CRA-40) as initial fields and boundary conditions, coupled with the WRF-Chem atmospheric chemical model and the WRFDA/3DVar assimilation system. By constructing a joint background error covariance matrix, CMA-ChemRA achieves weak coupling between atmospheric chemistry and meteorological variables, enabling simultaneous assimilation of diverse data sources, including hourly observations from ground stations, wind profilers, upper-air soundings, aircraft reports, and atmospheric composition measurements. To extend the dataset to periods before 2013 when China lacked PM<sub>2.5</sub> observations, the system incorporates a reconstructed PM<sub>2.5</sub> dataset derived by AI from visibility inversion alongside various emission inventories. The CMA-ChemRA system produces a reanalysis product from 2007 to the present, with a spatial resolution of 15 km and an hourly temporal resolution. It includes three-dimensional isobaric and near-surface layers for 6 key elements PM<sub>2.5</sub>, PM<sub>10</sub>, O<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, and CO, as well as meteorological variables. This product is updated in near real-time, with a 50-min lag for forecast updates. Evaluation of the system shows substantial improvements in accuracy, with significant reductions in root mean square error (RMSE) for the six elements in the near-surface atmospheric layer post-assimilation. The model's depiction of ground-level PM<sub>2.5</sub> concentrations aligns well with independent observational data across five urban regions, showing a narrow RMSE range of 15.5 to 32.8 μg/m<sup>3</sup>. Additionally, CMA-ChemRA demonstrates strong performance in capturing the evolution of dust storms and pollution events, particularly in accurately modeling PM<sub>2.5</sub> concentrations during severe pollution episodes. Our innovative approach in constructing a joint background error covariance matrix and the resulting high-resolution, real-time updating CMA-ChemRA product. This represents significant advancement in the field of atmospheric and chemical weather reanalysis. The product serves as an crucial tool for environmental monitoring and forecasting in China.</p>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":" ","pages":"177552"},"PeriodicalIF":8.0000,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of the CMA-ChemRA: China Regional Weakly Coupled Chemical-Weather Reanalysis System with product since 2007.\",\"authors\":\"Tao Zhang, Zijiang Zhou, Zhisen Zhang, Junting Zhong, Zhiquan Liu, Xiaoye Zhang, Wenhui Xu, Lipeng Jiang, Jie Liao, Yaping Ma, Yike Zhou, Huiying Wang, Jie Chen, Lu Zhang, Yan Yao, Hui Jiang, Wenjing Jiang\",\"doi\":\"10.1016/j.scitotenv.2024.177552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The CMA-ChemRA (China Regional Weakly Coupled Chemical-Weather Reanalysis System) was developped using China's first-generation global atmospheric reanalysis product (CRA-40) as initial fields and boundary conditions, coupled with the WRF-Chem atmospheric chemical model and the WRFDA/3DVar assimilation system. By constructing a joint background error covariance matrix, CMA-ChemRA achieves weak coupling between atmospheric chemistry and meteorological variables, enabling simultaneous assimilation of diverse data sources, including hourly observations from ground stations, wind profilers, upper-air soundings, aircraft reports, and atmospheric composition measurements. To extend the dataset to periods before 2013 when China lacked PM<sub>2.5</sub> observations, the system incorporates a reconstructed PM<sub>2.5</sub> dataset derived by AI from visibility inversion alongside various emission inventories. The CMA-ChemRA system produces a reanalysis product from 2007 to the present, with a spatial resolution of 15 km and an hourly temporal resolution. It includes three-dimensional isobaric and near-surface layers for 6 key elements PM<sub>2.5</sub>, PM<sub>10</sub>, O<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, and CO, as well as meteorological variables. This product is updated in near real-time, with a 50-min lag for forecast updates. Evaluation of the system shows substantial improvements in accuracy, with significant reductions in root mean square error (RMSE) for the six elements in the near-surface atmospheric layer post-assimilation. The model's depiction of ground-level PM<sub>2.5</sub> concentrations aligns well with independent observational data across five urban regions, showing a narrow RMSE range of 15.5 to 32.8 μg/m<sup>3</sup>. Additionally, CMA-ChemRA demonstrates strong performance in capturing the evolution of dust storms and pollution events, particularly in accurately modeling PM<sub>2.5</sub> concentrations during severe pollution episodes. Our innovative approach in constructing a joint background error covariance matrix and the resulting high-resolution, real-time updating CMA-ChemRA product. This represents significant advancement in the field of atmospheric and chemical weather reanalysis. 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Development of the CMA-ChemRA: China Regional Weakly Coupled Chemical-Weather Reanalysis System with product since 2007.
The CMA-ChemRA (China Regional Weakly Coupled Chemical-Weather Reanalysis System) was developped using China's first-generation global atmospheric reanalysis product (CRA-40) as initial fields and boundary conditions, coupled with the WRF-Chem atmospheric chemical model and the WRFDA/3DVar assimilation system. By constructing a joint background error covariance matrix, CMA-ChemRA achieves weak coupling between atmospheric chemistry and meteorological variables, enabling simultaneous assimilation of diverse data sources, including hourly observations from ground stations, wind profilers, upper-air soundings, aircraft reports, and atmospheric composition measurements. To extend the dataset to periods before 2013 when China lacked PM2.5 observations, the system incorporates a reconstructed PM2.5 dataset derived by AI from visibility inversion alongside various emission inventories. The CMA-ChemRA system produces a reanalysis product from 2007 to the present, with a spatial resolution of 15 km and an hourly temporal resolution. It includes three-dimensional isobaric and near-surface layers for 6 key elements PM2.5, PM10, O3, SO2, NO2, and CO, as well as meteorological variables. This product is updated in near real-time, with a 50-min lag for forecast updates. Evaluation of the system shows substantial improvements in accuracy, with significant reductions in root mean square error (RMSE) for the six elements in the near-surface atmospheric layer post-assimilation. The model's depiction of ground-level PM2.5 concentrations aligns well with independent observational data across five urban regions, showing a narrow RMSE range of 15.5 to 32.8 μg/m3. Additionally, CMA-ChemRA demonstrates strong performance in capturing the evolution of dust storms and pollution events, particularly in accurately modeling PM2.5 concentrations during severe pollution episodes. Our innovative approach in constructing a joint background error covariance matrix and the resulting high-resolution, real-time updating CMA-ChemRA product. This represents significant advancement in the field of atmospheric and chemical weather reanalysis. The product serves as an crucial tool for environmental monitoring and forecasting in China.
期刊介绍:
The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere.
The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.