Performance Evaluation of Satellite Observation of Sand/Dust Weather and Its Application in Assessing the Accuracy of Numerical Models
Source abstract
Abstract
Air quality monitoring and forecasting has been a challenging problem for years. In addition to traditional ground-based observational stations, in recent years there have been more geostationary and polar orbiting satellite observations on air quality. However, evaluation of performance of these observations is lacking, especially for the region of southern China, which is rarely affected by severe sand/dust weather. In the spring of 2025, two events of sand/dust weather, one case of sand/dust spreading to southern China in April and another case of sand/dust confining to northern China in May, provide a good opportunity for detailed case study and examination of the performance of the tools. The surface particulate matter (PM) concentration retrieved from a geostationary satellite, Geostationary Korea Multi-Purpose Satellite—2B (GEO-KOMPSAT-2B, or GK2B), is studied by checking consistency with the analysis of two numerical models: the Copernicus Atmosphere Monitoring Service model of the European Centre of Medium Range Weather Forecast (ECMWF-CAMS) and Chinese Unified Atmospheric Chemistry Environment model of the China Meteorological Administration (CMA-CUACE). The former shows comparable PM concentration with satellite observations, while overestimation is found with the latter. It is also found that there may be latitude dependence of the quality of the satellite-based data. To further validate the satellite observation data, it is directly compared with the ground-based station measurements in Hong Kong for the event in mid-April 2025, the performance of satellite data points near Hong Kong is generally satisfactory. For polar orbiting satellite, there is information about the aerosol classification in addition to aerosol optical depth, and the classification result is found to be reasonable by comparison with ground-based observation, though some refinements appear to be necessary. The geostationary satellite images provide high spatial coverage and frequently updated air quality data, which are confirmed to be useful in monitoring the southward spread of sand/dust weather to southern China which is a very rare event. The monitoring can be both qualitative and quantitative. The performance of various monitoring and forecasting tools is examined in details based on the cases. It also forms a reference for the use in operation, and opens up a new era for air quality study for southern China.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Atmospheric aerosols and clouds · Meteorological Phenomena and Simulations · Atmospheric chemistry and aerosols
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hong Kong Observatory, Hong Kong, China
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
References (26)
Show source-provided references
1. Estimating Ground-Level Particulate Matter Concentrations Using Satellite-Derived Data: A Review10.1080/15481603.2019.1703288 ↗
2. Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm10.1016/j.atmosenv.2019.04.002 ↗
3. Fine Particulate Concentrations over East Asia Derived from Aerosols Measured by the Advanced Himawari Imager Using Machine Learning10.1016/j.atmosres.2023.106787 ↗
4. Estimation of Ground-Level Particulate Matter Concentrations through the Synergistic Use of Satellite Observations and Process-Based Models over South Korea10.5194/acp-19-1097-2019 ↗
5. Geostationary Satellite-Derived Ground-Level Particulate Matter Concentrations Using Real-Time Machine Learning in Northeast Asia10.1016/j.envpol.2022.119425 ↗
6. Retrieval of Hourly PM2.5 Using Top-of-Atmosphere Reflectance from Geostationary Ocean Color Imagers I and II10.1016/j.envpol.2023.121169 ↗
7. Near-Real-Time Hourly PM2.5 Prediction over East Asia Using Geostationary Satellite Products and Machine Learning10.1016/j.atmosenv.2024.120700 ↗
8. A Continuous 2011–2022 Record of Fine Particulate Matter (PM2.5) in East Asia at Daily 2-km Resolution from Geostationary Satellite Observations: Population Exposure and Long-Term Trends10.1016/j.atmosenv.2025.121068 ↗
9. A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI: Development and Verification over Asia10.1016/j.eng.2023.09.023 ↗
10. Ma, Y., Zhang, W., Chen, X., Zhang, L., and Liu, Q. (2023). High Spatial Resolution Nighttime PM2.5 Datasets in the Beijing–Tianjin–Hebei Region from 2015 to 2021 Using VIIRS/DNB and Deep Learning Model. Remote Sens., 15.10.3390/rs15174271 ↗
11. Agbehadji, I.E., and Obagbuwa, I.C. (2024). Systematic Review of Machine Learning and Deep Learning Techniques for Spatiotemporal Air Quality Prediction. Atmosphere, 15.10.3390/atmos15111352 ↗
12. Satellite-based air quality monitoring using artificial intelligence: Research trends and future perspectives10.5572/KOSAE.2025.41.2.179 ↗
13. Challenges and Opportunities Offered by Geostationary Space Observations for Air Quality Research and Emission Monitoring10.1175/BAMS-D-23-0145.1 ↗
14. Air quality trends and regimes in South Korea inferred from 2015–2023 surface and satellite observations10.5194/acp-25-3233-2025 ↗
15. (2025, September 10). Geostationary Environment Monitoring Spectrometer (GEMS) Algorithm Theoretical Basis Document on Ground-Level Particulate Matter Estimation Algorithm. Available online: https://nesc.nier.go.kr/en/html/satellite/doc/doc.do.
16. (2025, September 10). Sand and Dust Storm Warning Advisory and Assessment System Asian Regional Center. Available online: http://www.asdf-bj.net/.
17. (2025, September 11). EPD AQHI Air Quality Monitoring Network of Hong Kong, Available online: https://www.aqhi.gov.hk/en/monitoring-network/air-quality-monitoring-network.html.
18. (2025, September 18). Air Quality Data from the China National Environmental Monitoring Centre. Available online: https://www.cnemc.cn/sssj/.
19. (2025, September 11). Visible Infrared Imaging Radiometer Suite (VIIRS) Sensor Data Record (SDR) User’s Guide, Available online: https://ncc.nesdis.noaa.gov/documents/documentation/viirs-users-guide-tech-report-142a-v1.3.pdf.
20. (2025, September 11). EPS Aerosol Optical Depth (AOD) Algorithm Theoretical Basis Document, Available online: https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/ATBD_EPS_Aerosol_AOD_v3.4.pdf.
21. (2025, September 11). VIIRS Deep Blue Aerosol Algorithm Theoretical Basis Document, Available online: https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives/Document%20Archive/Science%20Data%20Product%20Documentation/Product%20Generation%20Algorithms/VIIRS_Deep_Blue_Aerosol_ATBD_March_2025.pdf.
22. (2025, September 11). VIIRS Deep Blue Aerosol Product User Guide, Available online: https://ladsweb.modaps.eosdis.nasa.gov/api/v2/content/archives//Document%20Archive/Science%20Data%20Product%20Documentation/VIIRS_Deep_Blue_Aerosol_UG_v2_March_2025.pdf.
23. (2025, September 11). NASA Worldview, Available online: https://worldview.earthdata.nasa.gov/.
24. Chan, P.W., Chan, Y.W., Ho, C.K., Tse, W.P., Jin, J.Q., Wong, M.S., and Lau, A.K.H. A Very Rare Event of Sand/dust Weather in Hong Kong in Late Spring in 2025—Observational and Forecasting Aspects. Atmos. Environ., 2025, submitted.10.1016/j.atmosenv.2025.121575 ↗
25. The PM2.5 to PM10 Ratio as a Possible Indicator for Identifying Atmospheric Sand Dust in Hong Kong—An Exploratory Analysis
26. Chan, Y.W., Tang, W.H., and Lau, T.K. (2025, October 24). A Rare Event on 1–2 April 2024: Reduced Visibility in Hong Kong Under Southerly Winds, Available online: https://www.hko.gov.hk/en/Observatorys-Blog/108747/A-rare-event-on-1-2-April-2024-reduced-visibility-in-Hong-Kong-under-southerly-winds.