Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Computing, Performance and Communication Systems
  4. Vol 10, Issue 1, 2026

Seamless Hourly AOD Fusion Based on Himawari Satellite and Reanalysis Data

Download as PDF

DOI: 10.23977/cpcs.2026.100105 | Downloads: 2 | Views: 46

Author(s)

Meng Wu 1,2, Ning Wang 1

Affiliation(s)

1 Aerospace Information Research Institute,Chinese Academy of Sciences, 100094, Beijing, China
2 University of Chinese Academy of Sciences, 100049, Beijing, China

Corresponding Author

Ning Wang

ABSTRACT

Aerosol Optical Depth (AOD) is a key parameter for characterizing the total columnar aerosol load in the atmosphere, which is of great significance for climate change research, air quality monitoring, and global radiation budget assessment. Geostationary satellites can provide high-frequency hourly observations and have unique advantages in capturing rapid intra-day variations of aerosols. However, their AOD products generally suffer from large-scale spatiotemporal gaps due to factors such as cloud cover and high surface albedo, and a single product often struggles to balance accuracy and coverage simultaneously. To address this issue, this paper focuses on East Asia (20°N-50°N, 100°E-150°E) and utilizes three Himawari L3 hourly AOD products (AOT_L2_Mean, AOT_Merged, AOT_Pure) together with MERRA-2 reanalysis AOD data as data sources. A seamless hourly AOD fusion framework is constructed using the Optimal Interpolation method. Firstly, based on AERONET ground-based observations, the accuracy and coverage of the four products are validated and analyzed, clarifying their error characteristics and complementary relationships. Secondly, based on the validation accuracy of the three Himawari products and MERRA-2, the products are fused sequentially according to their accuracy levels. A 3×3 neighborhood spatial consistency constraint is introduced to suppress fusion noise, generating a seamless hourly AOD product that balances accuracy and coverage. Experimental results show that among the three Himawari products, AOT_Pure has the highest accuracy (r=0.807, RMSE=0.077) but the lowest coverage, while AOT_L2_Mean has the highest coverage but lower accuracy (r=0.662, RMSE=0.158). The MERRA-2 product offers the advantage of spatiotemporal continuity but has limited accuracy (r=0.617, RMSE=0.121). The fused product achieves 100% spatiotemporal seamless coverage while demonstrating higher accuracy compared to the MERRA-2 AOD product (r=0.662, RMSE=0.118, EE ratio 68.4%) and retains the local detailed features of the original Himawari observations. The fused product effectively fills daytime cloud-induced gaps and nighttime observation blind areas, providing reliable data support for dynamic monitoring of pollution processes and air quality research in East Asia.

KEYWORDS

Aerosol Optical Depth (AOD); Himawari satellite; Optimal Interpolation; Hourly seamless product

CITE THIS PAPER

Meng Wu, Ning Wang. Seamless Hourly AOD Fusion Based on Himawari Satellite and Reanalysis Data. Computing, Performance and Communication Systems (2026). Vol. 10, No. 1, 38-52. DOI: http://dx.doi.org/10.23977/cpcs.2026.100105.

REFERENCES

[1] Rosenfeld D ,Lohmann U ,Raga B G , et al. Flood or Drought: How Do Aerosols Affect Precipitation?[J].Science,2008,321(5894):1309-1313.DOI:10.1126/science.1160606.
[2] ZHANG T, SHEN H, XIA X, et al. 2023. Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations[J/OL]. IEEE Transactions on Geoscience and Remote Sensing, 61: 1-15. DOI:10.1109/TGRS.2023.3262785.
[3] Feng L , Su X , Wang L ,et al. Accuracy and error cause analysis, and recommendations for usage of Himawari-8 aerosol products over Asia and Oceania[J]. Science of The Total Environment, 2021, 796(12):148958. DOI:10.1016/j.scitotenv.2021.148958.
[4] Yunhui T ,Quan W ,Zhaoyang Z .Assessing spatiotemporal variations of AOD in Japan based on Himawari-8 L3 V31 aerosol products: Validations and applications[J]. Atmospheric Pollution Research, 2022, 13(6): DOI:10.1016/J.APR.2022.101439.
[5] Chatterjee A ,Michalak M A ,Kahn A R , et al. A geostatistical data fusion technique for merging remote sensing and ground‐based observations of aerosol optical thickness[J].Journal of Geophysical Research: Atmospheres, 2010, 115(D20): DOI:10.1029/2009JD013765.
[6] Ronald G ,Will M ,J M S , et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).[J].Journal of climate,2017,30(13):5419-5454.DOI:10.1175/JCLI-D-16-0758.1.
[7] Singh S ,Singh A ,Srivastava K A , et al. Assessment of Surface PM2.5 Concentrations over India using Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) Reanalysis Data[J].Pure and Applied Geophysics,2025,182(4):1-23.DOI:10.1007/S00024-025-03666-6.
[8] Holben B N , Eck T F , Slutsker I ,et al. AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization[J].Remote Sensing of Environment, 1998, 66(1):1-16.DOI:10.1016/S0034-4257(98)00031-5.
[9] Eck T F , Holben B N , Reid J S ,et al. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols[J].Journal of Geophysical Research Atmospheres, 1999, 104(D24):31333-31349. DOI:10.1029/1999JD900923.
[10] Carnevale C , De Angelis E , Finzi G ,et al. Optimal Interpolation Based Data Fusion Techniques to Improve Deterministic Air Quality Forecast[C]//International Technical Meeting on Air Pollution Modelling and its Application. Springer, Berlin, Heidelberg, 2021.DOI:10.1007/978-3-662-63760-9_22.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 3863
Visits: 251448

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.