Improving aerosol interaction with clouds and precipitation in a  regional chemical weather modeling system

Zhou, Chen; Zhang, X.; Gong, Sunling; Wang, Yan; Xue, Min

doi:10.5194/acp-16-145-2016

Cited by 23 publications

(8 citation statements)

References 69 publications

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“…WRF-CMAQ was applied in only 16 studies due to its later initial release in 2012 (Wong et al, 2012). GRAPES-CUACE was developed by the China Meteorological Administration and introduced in details in Zhou et al (2008Zhou et al ( , 2012Zhou et al ( , 2016, then firstly utilized in Wang et al (2010) to estimate impacts of aerosol feedbacks on meteorology and dust cycle in EA. The coupled version of WRF-NAQPMS was developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences and could improve the prediction accuracy of haze pollution in the North China Plain (NCP) (Wang Z.F.…”

Section: Statistics Of Published Literature 31 Summary Of Applications Of Coupled Models In Asiamentioning

confidence: 99%

Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality

Gao

Xiu

Zhang

et al. 2021

Preprint

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Abstract. Atmospheric aerosols can exert influence on meteorology and air quality through aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI) and this two-way feedback has been studied by applying two-way coupled meteorology and air quality models. As one of regions with high aerosol loading in the world, Asia has attracted many researchers to investigate the aerosol effects with several two-way coupled models (WRF-Chem, WRF-CMAQ, GRAPES-CUACE and WRF-NAQPMS) over the last decade. This paper attempts to offer bibliographic analysis regarding the current status of applications of two-way coupled models in Asia, related research focuses, model performances and the effects of ARI or/and ACI on meteorology and air quality. There are total 157 peer-reviewed articles published between 2010 and 2019 in Asia meeting the inclusion criteria, with more than 81 % of papers involving the WRF-Chem model. The number of relevant publications has an upward trend annually and East Asia, India, China, as well as the North China Plain are the most studied areas. The effects of ARI and both ARI and ACI induced by natural aerosols (particularly mineral dust) and anthropogenic aerosols (bulk aerosols, different chemical compositions and aerosols from different sources) are widely investigated in Asia. Through the meta-analysis of surface meteorological and air quality variables simulated by two-way coupled models, the model performance affected by aerosol feedbacks depends on different variables, simulation time lengths, selection of two-way coupled models, and study areas. Future research perspectives with respect to the development, improvement, application, and evaluation of two-way coupled meteorology and air quality models are proposed.

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Section: Statistics Of Published Literature 31 Summary Of Applications Of Coupled Models In Asiamentioning

confidence: 99%

Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality

Gao

Xiu

Zhang

et al. 2021

Preprint

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“…One major challenge is to disentangle the effects of different aerosol types on different types of clouds (Jiang et al., 2018). Also, most previous studies about the impact of aerosol on precipitation are based on limited local or cases analysis (Fan et al., 2012; Li et al., 2019; Zhou et al., 2016), from which a long‐term data analysis is lacking and the results remain controversial. In this study, we evaluate the influence of different types of aerosols on DCC precipitation, and the changes caused by atmospheric dynamics and/or thermodynamics are effectively minimized through the application of long‐term satellite observations.…”

Section: Discussionmentioning

confidence: 99%

Type‐Dependent Impact of Aerosols on Precipitation Associated With Deep Convective Cloud Over East Asia

Han¹,

Zhao

Lin³

et al. 2022

JGR Atmospheres

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The water circulation among atmosphere, land, surface, and ground water, that is, hydrological cycle, is crucial for life on Earth (Gleick, 1989;Levin & Cotton, 2009). Precipitation is one of the most important links in the hydrologic cycle, which determines the global water balance and the total amount of water resources in a region (Betts, 2007;Loaiciga et al., 1996;Morrissey & Graham, 1996). Given the manifested role of rainfall in the hydrologic cycle, the spatiotemporal redistribution of surface precipitation caused by the small changes in precipitating-related processes in clouds may largely affect climate and human society (

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“…Because irregular, non-spherical particles can be reasonably approxim- ated by the super-spheroidal shape (Bi et al, 2018;Sun et al, 2021), the DNN model developed in this study is expected to be quite useful in aerosol optics modeling. In future work, we will implement the DL scheme and DNN models into the Global/Regional Assimilation and Prediction Enhanced System (GRAPES) (Chen et al, 2008;Xue et al, 2008) and the Chinese Unified Atmospheric Chemistry Environment (CUACE) systems (Gong and Zhang, 2008;Zhou et al, 2008Zhou et al, , 2012Zhou et al, , 2016Wang et al, 2015Wang et al, , 2018.…”

Section: Conclusion Rmentioning

confidence: 99%

Application of a Neural Network to Store and Compute the Optical Properties of Non-Spherical Particles

Han

et al. 2022

Adv. Atmos. Sci.

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Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (∼127 GB) to that of the DNN parameters (∼20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.

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Improving aerosol interaction with clouds and precipitation in a regional chemical weather modeling system

Cited by 23 publications

References 69 publications

Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality

Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality

Type‐Dependent Impact of Aerosols on Precipitation Associated With Deep Convective Cloud Over East Asia

Application of a Neural Network to Store and Compute the Optical Properties of Non-Spherical Particles

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