Inter-Instrument Variability of Vaisala CL61 Lidar-Ceilometer's Attenuated Backscatter, Cloud Properties and Mixed-Layer Height

Characterizing inter-instrument variability of sensors is crucial to assessing uncertainties in observational campaigns, networks, and for data assimilation. Here, we co-locate six high signal-to-noise ratio Vaisala CL61 lidar-ceilometers for a period of 10 days to quantify instrument-related differences in several observed variables: profiles of attenuated backscatter, its components (parallel- and cross-polarized backscatter) and the volume linear depolarisation ratio ($\delta$), as well as derived cloud variables and mixed-layer height. Analysing intervals between 5 and 60 min, median absolute differences between sensors (AD${}_{50}$) and percentiles (e.g., AD${}_{75}$) are used to quantify instrument related uncertainties. For backscatter and $\delta$, we differentiate between conditions with rain, clear sky, and clouds. Here we address instrument precision rather than accuracy, with instrument accuracy assumed. The detected agreement between instruments suggests a distributed measurement network should be capable of providing context for interpretation of spatial differences. If instruments measure accurately, it is possible to resolve spatial differences (e.g., urban–rural) for attenuated backscatter, derived cloud variables and layer heights. However, differences exist and vary with signal-to-noise ratio and atmospheric conditions. The AD${}_{50}$ inter-sensor results for 15 min intervals for total cloud-cover fraction (excluding clear sky and fully overcast conditions) is 1.9%, and for cloud base height 7.3 m. Agreement of all cloud variables is better for boundary layer clouds (when first cloud layer 4 km agl) than for all five cloud layers recorded by the sensor firmware. The 15 min mixed-layer height AD${}_{50}$ is 0 m and the AD${}_{75}$ 21.5 m. We show that instrument precipitation flags are in good agreement, but do not link closely with ground-level rainfall observations, hence an alternative algorithm is proposed. We provide quality control recommendations for data processing to improve inter-instrument agreement of cloud variables and mixed-layer height.