An intercomparison of ozone taken from the Copernicus atmosphere monitoring service and the second Modern-Era retrospective analysis for research and applications over China during 2018 and 2019

Zhang, Yujing; Li, Jie; Li, Jianjun; Pan, Xiaole; Wang, Wei; Zhu, Lili; Wang, Zixi; Chen, Xueshun; Yang, Weihong; Wang, Zifa

doi:10.1016/j.jes.2022.01.045

Cited by 3 publications

(1 citation statement)

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“…O 3 with a longer life has a strong photochemical oxidation in the atmosphere. Anthropogenic emissions near the ground rapidly generate and accumulate NO x and VOCs, which are the precursors of ozone (Sillman and He, 2002;Ding et al, 2022;Zhang et al, 2022), and thus accelerate the generation of O 3 . As evident from the spatial distribution shown in Figure 6, the neural network emulator can well capture and fit the data characteristics, simulate the ozone generation and change trend of each region on land, and achieve a good simulation of each region in the world at the same time.…”

Section: Evaluation Of Spatial Simulation Effect Of Key Speciesmentioning

confidence: 99%

Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case

Zixi

et al. 2022

Front. Environ. Sci.

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The global atmospheric chemical transport model has become a key technology for air quality forecast and management. However, precise and rapid air quality simulations and forecast are frequently limited by the model’s computational performance. The gas-phase chemistry module is the most time-consuming module in air quality models because its traditional solution method is dynamically stiff. To reduce the solving time of the gas phase chemical module, we built an emulator based on a deep residual neural network emulator (NN) for Carbon Bond Mechanism Z (CBM-Z) mechanism implemented in Global Nested Air Quality Prediction Modeling System. A global high resolution cross-life multi-species dataset was built and trained to evaluate multi-species concentration changes at a single time step of CBM-Z. The results showed that the emulator could accelerate to approximately 300–750 times while maintaining an accuracy similar to that of CBM-Z module (the average correlation coefficient squared was 0.97) at the global scale. This deep learning-based emulator could adequately represent the stiff kinetics of CBM-Z, which involves 47 species and 132 reactions. The emulated ozone (O3), nitrogen oxides (NOx), and hydroxyl radical (OH) were consistent with those of the original CBM-Z module in different global regions, heights, and time. Our results suggest that data-driven emulations have great potential in the construction of hybrid models with process-based air quality models, particularly at larger scales.

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Section: Evaluation Of Spatial Simulation Effect Of Key Speciesmentioning

confidence: 99%