Comparison of cloud cover measurement techniques

Clouds are vital for Earth's radiative balance and affect industrial sectors like solar energy, airports, and ground-to-space 
optical communication. Cloud cover day and night measurement uncertainties persist due to subjective observations. 
Various ground instruments like radiometers and ceilometers offer improved accuracy, while infrared all-sky cameras 
like the Sky InSight™ offer more granular cloud cover descriptions. We aim to compare cloud cover data from these 
methods and suggest ways to address any disparities. 

We analyze cloud cover time series from the NOAA Surfrad Radiation Budget Network (SURFRAD) Table Mountain site 
in Boulder, Colorado, USA, for 2023. Data are collected using a pyrgeometer (Eppley’s PIR), pyranometers (diffuse - Eppley 8-48, global - Spectrosun SR-75), a ceilometer (Vaisala’s CL51), and an infrared all-sky camera (Reuniwatt’s Sky InSight™). Different methodologies are 
applied depending on the instrument used. For the radiometers, we utilize the existing NOAA RadFlux product (Riihimaki 
et al. 2019). For the ceilometer, we calculate a weighted average of low-mid cloud occurrences over the previous 30 minutes 
following the approaches in Wagner et al. (2015). With the Sky InSight™, we employ the provided cloud mask image 
and compute the ratio of cloudy pixels over the total number of pixels with a zenith angle below 70°, as well as on a 
narrow view close to the zenith used to emulate a ceilometer. 

The predominant readings are clear skies (0 and 1 octa) or overcast (7 or 8 octas), accounting for 70.1% of cases for 
the pyrgeometer, 87.8% for the ceilometer, and approximately 76.1% for the Sky InSight™ hemispherical and ground 
cloud covers. Overall, cross-instrument accuracy within one octa across all methods ranges between 70% and 75%, 
although this must be considered in the context of the high occurrence of clear and overcast skies at the site. For 
partial cloud cover cases (octas 2-6), cross-instrument accuracies drop to 25%-46%, with the ceilometer showing the 
most divergence and the camera and pyrgeometer tending to agree more often. The camera emulation of a 
ceilometer significantly improves agreement between the two instruments, suggesting between 25% and 35% of the 
ceilometer’s variance is due to the reduced field of view and averaging window.  

Analysis reveals that ceilometer measurements often miss cloud edges or gaps in the cloud cover due to their narrow 
spatial constraints, making partial cloud cover readings less frequent. They are also more sensitive to optically thin 
clouds than the other instruments. Conversely, while both the camera and pyrgeometer are sensitive to the entire sky 
dome, the latter tends to underestimate cloud cover for thin overcast skies (altostratus), whereas the former may miss 
some high-altitude cirrus clouds.