Climate properties regulated by convection, such as water vapor, cloud cove
r, and related distributions, are undersampled in asynoptic data from an in
dividual orbiting platform, which must therefore be restricted to time-mean
distributions. A procedure is developed to identify small-scale undersampl
ed variance in asynoptic data and reject it, leaving a more accurate repres
entation of large-scale variance that describes the organization of climate
properties. The procedure is validated against high-resolution distributio
ns that have been constructed from six satellites simultaneously observing
the earth. Observing the high-resolution distributions asynoptically is sho
wn to result in sampling error at large scales that is as great as the larg
e-scale signal present, limiting the usefulness of the raw asynoptic data t
o time-mean distributions. However, processing the asynoptic data to reject
undersampled incoherent variability reduces the error variance to 10% or l
ess, yielding a fairly accurate representation of large-scale coherent vari
ability, which then can be mapped synoptically on periods as short as 2.0 d
ays. Made possible then are studies of how cloud, water vapor, and related
distributions are organized by unsteady elements of the general circulation
, which cannot be studied in the raw asynoptic data.