The MODIS (Moderate-Resolution Imaging Spectroradiometer) cloud mask algorithm is designed to identify clear skies from clouds using a combination of spectral threshold tests. The algorithm operates at a global scale, producing a single-pixel resolution cloud mask daily. It utilizes 14 out of the 36 MODIS spectral bands to maximize reliable cloud detection and mitigate issues associated with coarser spatial resolution or fewer spectral bands. The cloud mask is used as an ancillary input for MODIS land, ocean, and atmosphere science algorithms to suggest processing options. The MODIS cloud mask algorithm identifies several conceptual domains based on surface type and solar illumination, including land, water, snow/ice, desert, and coast. For each domain, a series of threshold tests are applied to detect the presence of clouds. Each test returns a confidence level ranging from 1 (high confidence) to 0 (low confidence). The confidence levels from different tests are combined to determine the overall confidence in the cloud-free status of a pixel. The final output of the cloud mask is a binary decision (clear or cloudy) with four levels of confidence: confident clear, probably clear, undecided, and cloudy or obstructed. The algorithm's effectiveness is enhanced by grouping similar cloud detection tests, such as those for detecting thick high clouds, thin clouds, low clouds, and cirrus. Each group's confidence is determined by the minimum confidence of its individual tests. The MODIS cloud mask algorithm is designed to run in near real-time with limited computational resources and simple, easy-to-follow algorithm paths. The paper outlines the background, algorithm, inputs, outputs, and validation of the MODIS cloud mask. It discusses the strengths and weaknesses of the algorithm, emphasizing the importance of visual inspection and quantitative analysis for validation. The MODIS cloud mask algorithm aims to provide a robust and reliable tool for global cloud detection, complementing existing satellite and aircraft data.The MODIS (Moderate-Resolution Imaging Spectroradiometer) cloud mask algorithm is designed to identify clear skies from clouds using a combination of spectral threshold tests. The algorithm operates at a global scale, producing a single-pixel resolution cloud mask daily. It utilizes 14 out of the 36 MODIS spectral bands to maximize reliable cloud detection and mitigate issues associated with coarser spatial resolution or fewer spectral bands. The cloud mask is used as an ancillary input for MODIS land, ocean, and atmosphere science algorithms to suggest processing options. The MODIS cloud mask algorithm identifies several conceptual domains based on surface type and solar illumination, including land, water, snow/ice, desert, and coast. For each domain, a series of threshold tests are applied to detect the presence of clouds. Each test returns a confidence level ranging from 1 (high confidence) to 0 (low confidence). The confidence levels from different tests are combined to determine the overall confidence in the cloud-free status of a pixel. The final output of the cloud mask is a binary decision (clear or cloudy) with four levels of confidence: confident clear, probably clear, undecided, and cloudy or obstructed. The algorithm's effectiveness is enhanced by grouping similar cloud detection tests, such as those for detecting thick high clouds, thin clouds, low clouds, and cirrus. Each group's confidence is determined by the minimum confidence of its individual tests. The MODIS cloud mask algorithm is designed to run in near real-time with limited computational resources and simple, easy-to-follow algorithm paths. The paper outlines the background, algorithm, inputs, outputs, and validation of the MODIS cloud mask. It discusses the strengths and weaknesses of the algorithm, emphasizing the importance of visual inspection and quantitative analysis for validation. The MODIS cloud mask algorithm aims to provide a robust and reliable tool for global cloud detection, complementing existing satellite and aircraft data.