Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images
Abstract. Cloud cover estimates of single-layer shallow cumuli obtained from narrow
field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are
compared over an extended period (2000–2017 summers) at the established
United States Atmospheric Radiation Measurement mid-continental Southern
Great Plains site. We quantify the impacts of two factors on hourly and
sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection
and data merging criteria and (2) FOV configuration. Enhanced observations
at this site combine the advantages of the ceilometer, micropulse lidar
(MPL) and cloud radar in merged data products. Data collected by these three
instruments are used to calculate narrow-FOV cloud fraction (CF) as a
temporal fraction of cloudy returns within a given period. Sky images
provided by TSI are used to calculate the wide-FOV fractional sky cover
(FSC) as a fraction of cloudy pixels within a given image. To assess the
impact of the first factor on CF obtained from the merged data products, we
consider two additional subperiods (2000–2010 and 2011–2017 summers) that
mark significant instrumentation and algorithmic advances in the cloud
detection and data merging. We demonstrate that CF obtained from ceilometer
data alone and FSC obtained from sky images provide the most similar and
consistent cloud cover estimates; hourly bias and root-mean-square
difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from
merged MPL–ceilometer data provides the largest estimates of the multiyear
mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for
the first and second subperiods, respectively. CF from merged
ceilometer–MPL–radar data has the strongest subperiod dependence with a
bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no
bias for the second subperiod. The strong period dependence of CF obtained
from the combined ceilometer–MPL–radar data is likely results from a change
in what sensors are relied on to detect clouds below 3 km. After 2011, the
MPL stopped being used for cloud top height detection below 3 km, leaving
the radar as the only sensor used in cloud top height retrievals. To
quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We
demonstrate that FOV configuration does not modify the bias but impacts the
RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is
significant for sub-hourly observations, where 41 % of narrow- and
wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced
to visualize impacts of these two factors through integration of CF and FSC
data with novel TSI-based images of the spatial variability in cloud cover.
The influence of cloud field organization, such cloud streets parallel to
the wind direction, on narrow- and wide-FOV cloud cover estimates can be
visually assessed.