Development of the CMA-ChemRA: China Regional Weakly Coupled Chemical-Weather Reanalysis System with product since 2007.

Abstract

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_2.5 observations, the system incorporates a reconstructed PM_2.5 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 PM_2.5, 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 PM_2.5 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 PM_2.5 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.