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Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing.
Cheng, Xinghong; Liu, Yuelin; Xu, Xiangde; You, Wei; Zang, Zengliang; Gao, Lina; Chen, Yubao; Su, Debin; Yan, Peng.
Afiliação
  • Cheng X; State Key Lab of Severe Weather, Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China; Meteorological Observation Center, Chinese Meteorological Administration, Beijing 100081, China.
  • Liu Y; Key Laboratory of Atmospheric Sounding, Chinese Meteorological Administration, Chengdu 610225, China; College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China.
  • Xu X; State Key Lab of Severe Weather, Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
  • You W; Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China. Electronic address: ywlx_1987@163.com.
  • Zang Z; Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China.
  • Gao L; Meteorological Observation Center, Chinese Meteorological Administration, Beijing 100081, China.
  • Chen Y; Meteorological Observation Center, Chinese Meteorological Administration, Beijing 100081, China.
  • Su D; Key Laboratory of Atmospheric Sounding, Chinese Meteorological Administration, Chengdu 610225, China; College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China.
  • Yan P; Meteorological Observation Center, Chinese Meteorological Administration, Beijing 100081, China. Electronic address: yanpeng@cma.gov.cn.
Sci Total Environ ; 682: 541-552, 2019 Sep 10.
Article em En | MEDLINE | ID: mdl-31129542
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article