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Related Experiment Video

Updated: Mar 6, 2026

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer

Published on: May 29, 2019

Advancing operational global aerosol forecasting with machine learning.

Ke Gui¹, Xutao Zhang¹, Huizheng Che²

¹State Key Laboratory of Severe Weather Meteorological Science and Technology (LaSW) and Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, China.

|March 4, 2026

Summary

This summary is machine-generated.

A new AI system, the Global Aerosol-Meteorology Forecasting System (AI-GAMFS), provides fast and reliable 5-day aerosol forecasts. This advancement improves air quality and pollution event warnings.

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Last Updated: Mar 6, 2026

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer

Published on: May 29, 2019

Area of Science:

Atmospheric Science
Environmental Science
Computer Science

Background:

Aerosol forecasting is crucial for air quality, health, and climate management.
Existing methods face challenges due to complex aerosol-meteorology interactions, leading to uncertainty and high computational costs.

Purpose of the Study:

To develop a machine learning-driven system for accurate and efficient aerosol forecasting.
To improve the prediction of aerosol optical components and surface concentrations.

Main Methods:

Developed the Global Aerosol-Meteorology Forecasting System (AI-GAMFS) using a vision transformer and U-Net architecture.
Trained the model on 42 years of reanalysis data and initialized with GEOS-FP analyses.
Achieved rapid 5-day forecasts in just 1 minute.

Main Results:

AI-GAMFS demonstrated improved performance over existing services like Copernicus Atmosphere Monitoring Service and regional dust models.
Showcased lower root-mean-square error for global aerosol optical depth compared to GEOS-FP.
Provided enhanced surface aerosol component forecasts over the USA and China.

Conclusions:

Machine learning significantly refines aerosol forecasting capabilities.
AI-GAMFS offers a promising tool for early warnings of aerosol pollution events, including dust storms and wildfires.