PNNL

Abstract

Nearly all parameterisations of large-scale cloud require the specification of the critical relative humidity (RHcrit). This is the gridbox-mean relative humidity at which the subgrid fluctuations in temperature and water vapour become so large that part of a subsaturated gridbox becomes saturated and cloud starts to form. Until recently, the lack of high-resolution observations of temperature and moisture variability has hindered a reasonable estimate of the RHcrit from observations. However, with the advent of ground-based measurements from Raman lidar, it becomes possible to obtain long records of temperature and moisture (co-)variances with sub-minute sample rates. Lidar observations are inherently noisy and any analysis of higher-order moments will be very dependent on the ability to quantify and remove this noise. We present an exporatory study aimed at understanding whether current noise levels of lidar-retrieved temperature and water vapour are sufficient to obtain a reasonable estimate of the RHcrit. We show that vertical profiles of RHcrit can be derived for a gridbox length of up to about 30 km (120) with an uncertainty of about 4 % (2 %). RHcrit tends to be smallest near the scale height and seems to be fairly insensitive to the horizontal grid spacing at the scales investigated here (30 - 120 km). However, larger sensitivity was found to the vertical grid spacing. As the grid spacing decreases from 400 to 100 m, RHcrit is observed to increase by about 6 %, which is more than the uncertainty in the RHcrit retrievals.