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https://doi.org/10.5194/acp-2020-356
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-356
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 May 2020

27 May 2020

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This preprint is currently under review for the journal ACP.

Retrieval of Aerosol Combined with Assimilated Forecast

Mayumi Yoshida1, Keiya Yumimoto2, Takashi M. Nagao3, Taichu Tanaka4, Maki Kikuchi5, and Hiroshi Murakami5 Mayumi Yoshida et al.
  • 1Remote Sensing Technology Center of Japan, Tsukuba, 305-8505, Japan
  • 2Research Institute for Applied Mechanics, Kyushu University, Fukuoka, 816-8580, Japan
  • 3Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, 277-8568, Japan
  • 4Meteorological Research Institute, Tsukuba, 305-0052, Japan
  • 5Japan Aerospace Exploration Agency, Tsukuba, 305-8505, Japan

Abstract. We developed a new aerosol retrieval algorithm combining a numerical aerosol forecast. In the retrieval algorithm, the short-term forecast from an aerosol data assimilation system was used for a priori estimate instead of spatially and temporally constant values. This method was demonstrated using the Advanced Himawari Imager onboard the Japan Meteorological Agency’s geostationary satellite Himawari-8, and the results showed spatially finer distributions than the model forecast and less noisy distributions than the original algorithm. We validated the new algorithm using ground observation data and found that the aerosol parameters detectable by satellite sensors were retrieved more accurately than a priori model forecast by adding satellite information. Moreover, the retrieval accuracy was improved by using the model forecast as compared with using constant a priori estimates. By using the assimilated forecast for a priori estimate, information from previous observations can be propagated to future retrievals, thereby leading to better retrieval accuracy. Observational information from the satellite and aerosol transport by the model is incorporated cyclically to effectively estimate the optimum field of aerosol.

Mayumi Yoshida et al.

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Mayumi Yoshida et al.

Mayumi Yoshida et al.

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Latest update: 24 Sep 2020
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Short summary
We developed a new aerosol retrieval algorithm combining a numerical aerosol forecast. This is the first study that utilizes forecast from an aerosol data assimilation system for a priori estimate of the retrieval. Retrieval with high accuracy can be performed by effectively using model and satellite information. By using the assimilated forecast for a priori estimate, information from previous observations can be propagated to future retrievals, thereby leading to better retrieval accuracy.
We developed a new aerosol retrieval algorithm combining a numerical aerosol forecast. This is...
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