Abstract
This study newly applies measurements from two geostationary satellites, the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 and the Geostationary Ocean Color imager (GOCI) onboard the geostationary satellite COMS, to evaluate a unique regional aerosol-transport model coupled to a non-hydrostatic icosahedral atmospheric model (NICAM) at a high resolution without any nesting technique and boundary conditions of the aerosols. Taking advantage of the unique capability of these geostationary satellites to measure aerosols with unprecedentedly high temporal resolution, we focus on a target area (115°E-155°E, 20°N-50°N) in East Asia in May 2016, which featured the periodic transport of industrial aerosols and a very heavy aerosol plume from Siberian wildfires. The aerosol optical thickness (AOT) fields are compared among the AHI, GOCI, MODIS, AERONET and NICAM data. The results show that both AHI- and GOCI-retrieved AOTs were generally comparable to the AERONET-retrieved ones, with high correlation coefficients of approximately 0.7 in May 2016. They also show that NICAM successfully captured the detailed horizontal distribution of AOT transported from Siberia to Japan on the most polluted day (18 May 2016). The monthly statistical metrics, including correlation between the model and either AHI or GOCI, are estimated to be >0.4 in 42–49% of the target area. With the aid of sensitivity model experiments with and without Siberian wildfires, it was found that a long-range transport of aerosols from Siberian wildfires (from as far as 3000 km) to Japan influenced the monthly mean aerosol levels, accounting for 7–35% of the AOT, 26–49% of the surface PM2.5 concentrations, and 25–66% of the aerosol extinction above 3 km in height over Japan. Therefore, the air pollutants from Siberian wildfire cannot be ignored for the spring over Japan.
| Original language | English |
|---|---|
| Pages (from-to) | 93-113 |
| Number of pages | 21 |
| Journal | Atmospheric Research |
| Volume | 217 |
| DOIs | |
| Publication status | Published - 2019 Mar 1 |
Bibliographical note
Funding Information:Data used to support this article can be obtained by request to the corresponding author ( goto.daisuke@nies.go.jp ). We acknowledge the developers and administrators of NICAM ( http://nicam.jp /), SPRINTARS ( https://sprintars.riam.kyushu-u.ac.jp/indexe.html ), NIES-LIDAR (acquired by contacting nsugimot@nies.go.jp and shimizua@nies.go.jp ), AHI ( http://www.eorc.jaxa.jp/ptree/index.html ), GOCI ( http://kosc.kiost.ac.kr /), MODIS ( https://modis.gsfc.nasa.gov /) and the relevant PIs of the AERONET sites ( https://aeronet.gsfc.nasa.gov /). The CAMS Global Fire Assimilation System (GFAS) inventory was provided by ECMWF ( http://apps.ecmwf.int/datasets/data/cams-gfas/ ), which was accessed on 12 March 2018. The PM2.5 surface measurements ( http://soramame.taiki.go.jp /) are provided by local government measurements under the operation of the Ministry of the Environment, Japan (MOEJ). The National Centers for Environmental Prediction (NCEP) FNL Operational Model Global Tropospheric Analyses were provided by NCEP, National Weather Service, NOAA, and the U.S. Department of Commerce (2000) ( https://rda.ucar.edu/datasets/ds083.2/ ), which was accessed on 22 March 2018. The back trajectories used in Fig. 2 were calculated by the NOAA HYSPLIT trajectory model ( https://www.ready.noaa.gov/HYSPLIT.php ), which was accessed on 17 October 2018. Some of the authors were supported by the Global Environment Research and Technology Development Fund S-12 of MOEJ, the Japan Aerospace Exploration Agency (JAXA)/Earth Observation Priority Research, and the Grant-in-Aid for Young Scientist B (grant 26740010) and A (grant 17H04711). Additionally, some of the authors were supported by the following projects: National Institute for Environmental Studies, Japan (NIES), and MOEJ GOSAT2, JAXA/EarthCARE, JAXA/GCOM-C, and the Japan Science and Technology (JST), CREST/EMS/TEEDDA. Some of the authors were supported by the National Strategic Project-Fine particle of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT , the Ministry of Environment , and the Ministry of Health and Welfare (NRF- 2017M3D8A1092021 ) in Korea. Development of the integrated data processing system for GOCI-II” funded by the Ministry of Ocean and Fisheries, Korea. The model simulations were performed by using the supercomputer resources JAXA/JSS2, NIES/NEC SX-ACE, K computer (150156, 160004, 170017 and 180012), and PRIMEHPC FX10 (University of Tokyo, Japan). We thank Tran Thi Ngoc Trieu for help in processing the nudging datasets.
Funding Information:
Data used to support this article can be obtained by request to the corresponding author (goto.daisuke@nies.go.jp). We acknowledge the developers and administrators of NICAM (http://nicam.jp/), SPRINTARS (https://sprintars.riam.kyushu-u.ac.jp/indexe.html), NIES-LIDAR (acquired by contacting nsugimot@nies.go.jp and shimizua@nies.go.jp), AHI (http://www.eorc.jaxa.jp/ptree/index.html), GOCI (http://kosc.kiost.ac.kr/), MODIS (https://modis.gsfc.nasa.gov/) and the relevant PIs of the AERONET sites (https://aeronet.gsfc.nasa.gov/). The CAMS Global Fire Assimilation System (GFAS) inventory was provided by ECMWF (http://apps.ecmwf.int/datasets/data/cams-gfas/), which was accessed on 12 March 2018. The PM2.5 surface measurements (http://soramame.taiki.go.jp/) are provided by local government measurements under the operation of the Ministry of the Environment, Japan (MOEJ). The National Centers for Environmental Prediction (NCEP) FNL Operational Model Global Tropospheric Analyses were provided by NCEP, National Weather Service, NOAA, and the U.S. Department of Commerce (2000) (https://rda.ucar.edu/datasets/ds083.2/), which was accessed on 22 March 2018. The back trajectories used in Fig. 2 were calculated by the NOAA HYSPLIT trajectory model (https://www.ready.noaa.gov/HYSPLIT.php), which was accessed on 17 October 2018. Some of the authors were supported by the Global Environment Research and Technology Development Fund S-12 of MOEJ, the Japan Aerospace Exploration Agency (JAXA)/Earth Observation Priority Research, and the Grant-in-Aid for Young Scientist B (grant 26740010) and A (grant 17H04711). Additionally, some of the authors were supported by the following projects: National Institute for Environmental Studies, Japan (NIES), and MOEJ GOSAT2, JAXA/EarthCARE, JAXA/GCOM-C, and the Japan Science and Technology (JST), CREST/EMS/TEEDDA. Some of the authors were supported by the National Strategic Project-Fine particle of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT, the Ministry of Environment, and the Ministry of Health and Welfare (NRF-2017M3D8A1092021) in Korea. Development of the integrated data processing system for GOCI-II” funded by the Ministry of Ocean and Fisheries, Korea. The model simulations were performed by using the supercomputer resources JAXA/JSS2, NIES/NEC SX-ACE, K computer (150156, 160004, 170017 and 180012), and PRIMEHPC FX10 (University of Tokyo, Japan). We thank Tran Thi Ngoc Trieu for help in processing the nudging datasets.
Publisher Copyright:
© 2018 The Authors
All Science Journal Classification (ASJC) codes
- Atmospheric Science