MODIS/GLI cloud mask: Results, comparisons, and validation

Clouds are a crucial component in all meteorological and climate models. The Japanese Global Imager (GLI) and US MODIS instruments make it possible to improve upon the existing remote sensing studies by identifying cloud susing a multi-spectral approach at high spatial resolution. The MODIS and GLI Cloud Masks aim to minimize the potential errors in retrieval algorithms resulting from cloud contamination by labeling every pixel of data as either confident clear, probably clear, uncertain, or cloudy. Cloud masking from the MODIS/GLI observations are produced routinely and distributed to the earth system scientists. . The MODIS/GLI cloud mask classifies each pixel as either confident clear, probably clear, uncertain, or cloudy. The cloud mask algorithm uses a series of threshold tests to detect the presence of clouds in the instrument field-of-view. Designed to operate globally during the day and night, the specific tests executed are a function of surface type, including land, water, snow/ice, desert, and coast, and solar illumination. Each cloud detection test returns a confidence level that the pixel is clear ranging in value from 1 (high confidence clear) to 0 (low confidence clear). Tests capable of detecting similar cloud conditions are grouped together and a minimum confidence is determined for each group. The final cloud mask (Q) is then determined from the product of the results from each group. This approach is clear-sky conservative in the sense that if any test is highly confident that the scene is cloudy (Fi = 0), the final clear sky confidence is 0. The four confidence levels included in the cloud mask output are: (1) confident clear (Q > 0.99); (2) probably clear (Q > 0.95); (3) uncertain (Q > 0.66); and (4) cloudy (Q £ 0.66). For many regions of the globe, the uncertain classification can be considered probably cloudy. For comparison with the expert analysis, confident clear and probably clear are considered clear pixels and the uncertain and cloudy confidences are labeled as cloudy. The paper will present the latest updates to the MODIS and GLI cloud masks, results from this cloud detection algorithm, and along with validation using ground based, aircraft and other satellite instruments.