What is already known:
What this study adds:
We use a cloud detection algorithm that detects cloudy pixels from MODIS images by characterizing individual pixels as cloudy or non-cloudy based on the brightness values of the pixels and a predetermined threshold. The algorithm then produces mean fields of daytime cloudiness over different geographical regions. Although the cloud climatologies produced initially appeared realistic, it was found that the algorithm largely underestimated the cloud frequencies over some regions when using a threshold of 215. Analyzing various MODIS images and recording cloudiness over different sectors served as the “ground truth” data which we compared to the algorithm output. After comparing the subjective estimates and the algorithm output for four regions of the world, we found that the algorithm underestimates cloudiness over these additional regions and that lowering the thresholds to 170-190 over oceans and 190-215 over land generally identified the thick clouds most accurately. Studying more regions or extending research on certain regions will allow us to better understand how the algorithm behaves with certain types of cloudiness and geography. Even though the thresholding technique is somewhat arbitrary, by better understanding how the algorithm behaves we can modify the algorithm to ensure that the output more accurately describes cloud climatologies around the world. If we are able to do this, then our algorithm could be used for many applications such as validating the numerical model simulations of cloud climatologies or assessing climate and potential climate change.