This paper presents a cloud-screening and quality control algorithm for the AERONET aerosol optical depth database. The algorithm was developed to ensure the reliability and consistency of aerosol optical depth measurements obtained from ground-based sunphotometers. The AERONET network is a globally distributed system that provides measurements of aerosol optical depth, which is crucial for understanding atmospheric aerosol loading and its impact on climate and air quality. The algorithm was tested on various experimental data sets under different geographical and optical conditions, including biomass burning events, hazy summer conditions, and clean air advected from the Arctic. The algorithm uses two main criteria: the triplet stability criterion and the smoothness criterion. The triplet stability criterion ensures that the aerosol optical depth remains stable within a 1-minute period, while the smoothness criterion limits the root mean square of the second derivative of the aerosol optical depth with time. These criteria help to eliminate data affected by clouds, ensuring that only cloud-free data is used for analysis. The algorithm was tested on data from various sites, and it was found that it effectively removed between 20% to 50% of the initial data, depending on cloud conditions. The algorithm also includes a three standard deviation criterion, which checks for measurements that fall outside the 3σ range of the mean aerosol optical depth and the Angstrom parameter. This helps to identify and eliminate measurements that are likely to be contaminated by clouds or other anomalies. The algorithm was found to be effective in reducing the impact of cloud contamination on aerosol optical depth measurements, while also preserving the integrity of the data. The paper also discusses the limitations of the algorithm, including the potential for missing some interesting aerosol optical situations and the need for further refinement. However, the algorithm was found to be effective in most cases and is recommended for use in the AERONET network. The algorithm is not a generalized model and is specific to the AERONET database and the CIMEL sun/sky radiometer used for measurements. The paper concludes that the algorithm provides a reliable method for cloud screening and quality control of aerosol optical depth data, ensuring that the data is accurate and representative of the true aerosol loading in the atmosphere.This paper presents a cloud-screening and quality control algorithm for the AERONET aerosol optical depth database. The algorithm was developed to ensure the reliability and consistency of aerosol optical depth measurements obtained from ground-based sunphotometers. The AERONET network is a globally distributed system that provides measurements of aerosol optical depth, which is crucial for understanding atmospheric aerosol loading and its impact on climate and air quality. The algorithm was tested on various experimental data sets under different geographical and optical conditions, including biomass burning events, hazy summer conditions, and clean air advected from the Arctic. The algorithm uses two main criteria: the triplet stability criterion and the smoothness criterion. The triplet stability criterion ensures that the aerosol optical depth remains stable within a 1-minute period, while the smoothness criterion limits the root mean square of the second derivative of the aerosol optical depth with time. These criteria help to eliminate data affected by clouds, ensuring that only cloud-free data is used for analysis. The algorithm was tested on data from various sites, and it was found that it effectively removed between 20% to 50% of the initial data, depending on cloud conditions. The algorithm also includes a three standard deviation criterion, which checks for measurements that fall outside the 3σ range of the mean aerosol optical depth and the Angstrom parameter. This helps to identify and eliminate measurements that are likely to be contaminated by clouds or other anomalies. The algorithm was found to be effective in reducing the impact of cloud contamination on aerosol optical depth measurements, while also preserving the integrity of the data. The paper also discusses the limitations of the algorithm, including the potential for missing some interesting aerosol optical situations and the need for further refinement. However, the algorithm was found to be effective in most cases and is recommended for use in the AERONET network. The algorithm is not a generalized model and is specific to the AERONET database and the CIMEL sun/sky radiometer used for measurements. The paper concludes that the algorithm provides a reliable method for cloud screening and quality control of aerosol optical depth data, ensuring that the data is accurate and representative of the true aerosol loading in the atmosphere.