JAXA Special Publication JAXA-SP-19-003E

Atmospheric Environmental Monitoring Simulation
Report Number: R18ER2401
Subject Category: Space Technology
URL: https://www.jss.jaxa.jp/en/ar/e2018/9160/
Responsible Representative
Naoto Matsuura, Director of Earth Observation Research Center
Contact Information
Maki Kikuchi (kikuchi.maki@jaxa.jp)
Members
Daisuke Goto, Maki Kikuchi, Kentaroh Suzuki, Tie Dai, Yueming Cheng
Abstract
This project is aimed at development of an integrated system of numerical modeling and satellite observation ofair pollution for its reliable monitoring and prediction. For this purpose, seamless atmospheric modeling systemis operated on JSS2 to conduct simulations of air pollution transport over East Asia. The results are also validated
against satellite observations of aerosols.
Reasons for using JSS2
In the project, we need to conduct numerical simulations with high spatial resolutions over wide area including
Japan and East Asia for reliable monitoring and predictions of air pollutions over the region. This requires us toexploit high performance super computing resources of JSS2 available at JAXA.
Achievements of the Year
We have continued operations of aerosol simulations with the NICAM-SPRINTARS model installed on JSS2.
Regional simulations have been conducted over East Asia area. In this fiscal year, in order to deeply understandthe 4-dimensional structure of aerosols over Japan, the simulated results were well validated by multiple
measurements such as geostationary satellites of Himawari-8/AHI and COMS/GOCI, in-situ sampling of thesurface monitoring under the Atmospheric Environmental Regional Observation System (AEROS) in Ministry ofthe Environment, Japan, and NIES-lidar network. Figure 1 illustrates an example of the model evaluation in termsof 4-dimensional atmospheric aerosol distributions over Northern Japan. The observed products include aerosol
optical thickness (AOT; upper panel), surface PM2.5 concentrations (middle panel) and vertical profile ofextinction for spherical aerosols (lower panel). These results show that the model generally reproduced theobserved AOT (column amount of aerosols) but sometimes overestimated the observed extinction coefficients(vertical profile of aerosols). Such a comparison enables us to better evaluate the model in the context of itsrepresentation of detailed characteristics of aerosol transport processes. This finding shows how to improve themodel to better reproduce the realistic distributions of atmospheric aerosols. These results have been published－356－

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JAXA Supercomputer System Annual Report (April 2018-March 2019)from Goto et al. (2019).
In parallel, we have continued the aerosol assimilation coupled with NICAM-SPRINTARS to better estimate theaerosol transport characteristics over East Asia area by a further developing of the model-satellite synergy.
Figure 2 shows that how the simulations with assimilation better represent the time series of aerosol optical
thickness at 7 AERONET ground-based measurements, compared to those without assimilation. The statistic
metrics obtained from the assimilation were much improved than those obtained from the non-assimilation. In theassimilation results at Osaka as an example, the root-mean-square-error (RMSE) decreases by 20% and thecorrelation coefficient (CORR) increases by 0.3 compared to the non-assimilation results. Especially, we haveconfirmed that a new assimilation system of 4-dimensional localized ensemble transform Kalman filter (4DLETKF) is successfully working on JSS2. Figure 3 also shows the improvement of the assimilation results bycomparing independent satellite measurement of MODIS. These results demonstrate how it is unified withHimawari-8 satellite as well as the other satellite measurements through assimilations for better predictions ofaerosol pollutions over East Asia area. These results have been published from Dai et al. (2019).

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JAXA Special Publication JAXA-SP-19-003EFig. 1: Hourly variations in the AOT, surface PM2.5 concentrations, and extinction coefficients forspherical particles according to multiple measurements (left panels) and as simulated by NICAM(right panels) in northern Japan, including Hokkaido (138.5E-146.0E, 41.5N-46.0N) and Niigata(138.9E, 37.8N). The AOT was retrieved from the Himawari-8/AHI and GOMS/GOCI. The surface
PM2.5 concentrations were observed by the Atmospheric Environmental Regional Observation
System (AEROS) at Hokkaido. The extinction coefficients were retrieved from NIES-LIDAR. The
shaded gray areas in the vertical profiles represent undefined values because of cloud and raindrop
contamination and altitude effects. The marks of closed squares in colors around the top in the panelsof NICAM-simulated AOT and surface PM2.5 concentrations represent the periods of the highcontribution of sulfate in blue (>30% of the total AOT) and in cyan (>50% of the total AOT) to thetotal AOT and those in the high (>50%) contribution of carbonaceous aerosols in red (for Siberianbiomass burning) to the total AOT. The bottom-right panel represents the temporal variation of themajor species for the extinction of spherical aerosols obtained by NICAM. The results were definedas those in the high (>50%) contribution of sulfate to the total extinction and those in the high(>50%) contribution of carbonaceous aerosols (Siberian biomass burning, BB, and non-Siberian BB)
to the total extinction when the total extinction exceeded 0.02 km-1. This figure is referred from
Figure 8 in Goto et al. (2019).

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JAXA Supercomputer System Annual Report (April 2018-March 2019)

Fig. 2: Hourly time series of the simulated aerosol optical thicknesses (AOTs) for the four experiments
and the observed AOTs from Himawari-8, Moderate Resolution Imaging Spectroradiometer (MODIS),and AErosol RObotic NETwork (AERONET), over seven selected AERONET sites. The four modelexperiments include results without assimilation (CONTROL), those with assimilation (LETKF), thosewith 6-hourly 4-dimentional assimilation (4D-LETKF-6H) and 24-hourly 4-dimentional assimilation(4D-LETKF-24H). The root-mean-square error (RMSE) and correlation coefficient (CORR) between
the simulated AOTs for the four experiments and the AERONET observed AOTs are also shown. Thisfigure is referred from Figure 12 in Dai et al. (2019).

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JAXA Special Publication JAXA-SP-19-003EFig. 3: Spatial distributions of (first row) the simulated monthly mean aerosol optical thickness(AOT) and the (second row) bias, (third row) root-mean-square error, and (forth row) correlation
coefficient between the simulated and Himawari-8 observed AOTs during November 2016 for thefour experiments. The term ‘ave’ represents the region-averaged value. Hereafter, the bias is definedas the simulated AOT minus the observed AOT. The points marked by black dots in the second andthird rows indicate that the differences in means and variances are not significant at the 95%
significance level. The black dots in the fourth row indicate that the correlations are significant at the95% significance level. This figure is referred from Figure 6 in Dai et al. (2019).

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JAXA Supercomputer System Annual Report (April 2018-March 2019)

Publications- Peer-reviewed papers
Dai T., Cheng Y., Zhang P., Shi G., Sekiguchi M., Suzuki K., Goto D., Nakajima T. (2018) Impacts ofmeteorological nudging on the global dust cycle simulated by NICAM coupled with an aerosol model.
Atmospheric Environment, 190, 99-115, doi:10.1016/j.atmosenv.2018.07.016
Goto D., Kikuchi M., Suzuki K., Hayasaki M., Yoshida M., Nagao T.M., Choi M., Kim J., Sugimoto N., Shimizu
A., Oikawa E., Nakajima T. (2019) Aerosol model evaluation using two geostationary satellites over East Asia inMay 2016. Atmospheric Research, 217, 93-113, doi:10.1016/j.atmosres.2018.10.016
Dai T., Cheng Y., Suzuki K., Goto D., Kikuchi M., Schutgens N.A.J., Yoshida M., Zhang P., Husi L., Shi G.,
Nakajima T. (2019) Hourly aerosol assimilation of Himawari-8 AOT using the four-dimensional local ensemble
transform Kalman filter. Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2018MS001475.
- Oral Presentations
Goto D., Kikuchi M., Suzuki K., Hayasaki M., Yoshida M., Nagao T., Sugimoto N., Shimizu A., Nakajima T.
(2018) Understanding of Atmospheric Aerosol Behavior Using a Semi-Regional Model, a Geostationary Satelliteand in Situ Measurements over Japan in May 2016, 15th Annual Meeting: Asia Oceania Geosciences Society(AOGS), Honolulu, Hawaii, USA, June 2018
Goto D. (2018) Analysis of aerosol transport to Japan by combining seamless regional model and multiple
observations including geostationary satellite: A case study of May 2016, Land Cover/Land Use Changes(LC/LUC) and Impacts on Environment in South/Southeast Asia -International Regional Science Meeting, Manila,Philippine, May 2018

Usage of JSS2

Computational Information
Process Parallelization Methods

MPI

Thread Parallelization Methods

Automatic Parallelization

Number of Processes

1 - 160

Elapsed Time per Case

20 Hour (s)Resources Used
Fraction of Usage in Total Resources*1 (%): 0.04－361－

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JAXA Special Publication JAXA-SP-19-003E

Details
Computational Resources
System Name

Amount of Core Time(core x hours)

Fraction of Usage*2 (%)

SORA-MA

27,369.32

0.00

SORA-PP

0.00

0.00

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage Assigned (GiB)Fraction of Usage*2 (%)/home

1,645.09

1.70

/data

39,920.83

0.70/ltmp

12,695.32

1.09

Archiver Resources
Archiver Name

Storage Used (TiB)

J-SPACE

Fraction of Usage*2 (%)
0.00

0.00

*1

: Fraction of Usage in Total Resources: Weighted average of three resource types (Computing, File System,and Archiver).
*2: Fraction of Usage: Percentage of usage relative to each resource used in one year－362－This document is provided by JAXA.