Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

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  • Tie Dai
    State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing China
  • Yueming Cheng
    Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science and Technology Nanjing China
  • Kentaroh Suzuki
    Atmosphere and Ocean Research Institute University of Tokyo Kashiwa Japan
  • Daisuke Goto
    National Institute for Environmental Studies Tsukuba Japan
  • Maki Kikuchi
    Earth Observation Research Center Japan Aerospace Exploration Agency Tsukuba Japan
  • Nick A. J. Schutgens
    Faculty of Science Free University of Amsterdam Amsterdam Netherlands
  • Mayumi Yoshida
    Earth Observation Research Center Japan Aerospace Exploration Agency Tsukuba Japan
  • Peng Zhang
    National Satellite Meteorological Center, China Meteorological Administration Beijing China
  • Letu Husi
    State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences Beijing China
  • Guangyu Shi
    State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing China
  • Teruyuki Nakajima
    Earth Observation Research Center Japan Aerospace Exploration Agency Tsukuba Japan

書誌事項

公開日
2019-03
資源種別
journal article
権利情報
  • http://creativecommons.org/licenses/by-nc-nd/4.0/
DOI
  • 10.1029/2018ms001475
公開者
American Geophysical Union (AGU)

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説明

<jats:title>Abstract</jats:title><jats:p>The next‐generation geostationary satellite Himawari‐8 has a much higher observation frequency of the aerosol field than polar‐orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one‐monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari‐8‐retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non‐hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM‐SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four‐dimensional LETKF (4D‐LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari‐8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)‐ and AErosol RObotic NETwork (AERONET)‐retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root‐mean‐square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari‐8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D‐LETKF experiments are generally not so different, but the computational load of the 4D‐LETKF is much lighter than that of the LETKF.</jats:p>

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