Dipole Pattern of Summer Ozone Pollution in the east of China and Its Connection with Climate Variability

Ma, Xuexi; Zhicong, Yin

doi:10.5194/acp-2021-613

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…However, the trend of the ozone seasonal variation is weak in South China, including the PRD region. This is due to the warmer temperatures throughout the year and the Asian Summer monsoon, which brings cloudy and raining weather, marine air and strong deep convection and other unfavorable factors for ozone formation and accumulation (Lu et al., 2018; Ma & Yin, 2021; Yang et al., 2020). It is a challenge in existing air quality models when taking into account these complex meteorological factors.…”

Section: Resultsmentioning

confidence: 99%

Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks

Cheng

Fang

Navon

et al. 2022

J Adv Model Earth Syst

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Efficient and accurate real‐time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large‐scale numerical simulations. In this work, we introduce a hybrid model (VAE‐GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE‐GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead‐time beyond the training period. The performance of VAE‐GAN is demonstrated in hourly and daily spatio‐temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE‐GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE‐GAN is a cost‐effective tool for large data‐driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.

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Section: Resultsmentioning

confidence: 99%