Impact of conditional sampling and instrumental limitations on the statistics of cloud properties derived from cloud radar and lidar at SIRTA

Abstract

Clouds represent the largest uncertainty in future climate projections. As a result, unbiased long‐term vertically‐resolved cloud observations must be collected and analyzed in order to produce regional cloud climatologies. In the present study, we use model outputs to evaluate the impact of conditional temporal sampling and instrumental effects on the 2‐year statistics of frequency of cloud occurrence and cloud fraction. We then quantify the radiative significance of the ice clouds undetected by cloud radars. We find that in order to evaluate the representation of all types of clouds in operational models both a cloud radar and a lidar must be used. The cloud radar alone can do a reasonable job at describing cloud properties up to 8–9 km, however the lidar is mandatory to detect most of the high‐altitude clouds above 9 km. The sampling should be regular but not necessarily continuous, and should not be driven by meteorological conditions. This result applies to all sites having a lidar without a radome. It is finally suggested that a cloud radar of around −60 dBZ sensitivity at 1 km range would be required to detect almost all radiatively‐significant ice clouds.