The Burger distribution is investigated by examining the way in which this novel method of characterizing a frequency distribution represents cloud amount and sky opacity in Canada and Australia. It is shown that the Burger parameters, mean cloud amount and scale distance (C, r), provide an acceptable means of representing cloud frequency distributions and that the Burger distribution may be preferable to previously employed distributions because both parameters have interpretable meaning and because the Burger distribution permits representation and/or prediction of clear and overcast conditions. For the Canadian and Australian stations studied, the seasonal cycle of cloud type is found to be reflected in the computed seasonal cycle in the Burger scale distance but there are problems in interpreting the meaning of these variations in r because the area of observation for a surface observer changes as a function of cloud type. The real potential lies in the application of the Burger distribution to satellite-derived cloudiness where the area of observation is well defined and remains constant. The Burger characterization offers a unique feature, namely the possibility of a real rescaling to spatial dimensions different from those over which the original observations were made. Specific testing of this possibility for the Canadian cloud data considered here leads to the conclusion that rescaling to smaller areas is successful. Frequency distributions for ground areas commensurate with individual geostationary satellite pixels are achievable, the results are robust and lie only just beyond the demonstrated range of validity. Overall, the Burger distribution is a valid and useful method of characterizing cloudiness which has the demonstrable potential as a means of representing the spatial nature of cloud parameters derived from satellites.