Towards retrieving critical relative humidity from ground‐based remote‐sensing observations. (22nd August 2016) - Van Weverberg, Kwinten; Boutle, Ian A.; Morcrette, Cyril J.; Newsom, Rob K.
Abstract : Nearly all large‐scale cloud parametrizations require the specification of the critical relative humidity (RHcrit). This is the grid‐box mean relative humidity at which the subgrid fluctuations in temperature and water vapour are assumed to become so large that part of a subsaturated grid box becomes saturated and cloud starts to form. Until recently, the lack of high‐resolution observations of temperature and moisture variability has hindered achievement of a reasonable estimate of RHcrit. However, the advent of ground‐based Raman lidar now allows the acquisition of long records of temperature and moisture with subminute sample rates. Lidar observations are inherently noisy and any analysis of higher‐order moments will be dependent on the ability to quantify and remove this noise. We present an exploratory study aimed at understanding whether current noise levels of lidar‐retrieved temperature and water vapour are sufficiently low to obtain a reasonable estimate of RHcrit. We show that vertical profiles of RHcrit can be derived with an uncertainty of a few per cent. RHcrit tends to be smallest near the boundary‐layer top and seems to be insensitive to the horizontal grid spacing at the scales investigated here (30–120 km). However, larger sensitivity was found to the vertical grid spacing. RHcrit is observed to decrease by 10% as the vertical grid spacing quadruples. By way of example, the lidar‐retrieved RHcrit profiles were used to evaluate a parametrization that estimates RHcrit from variances diagnosed from the boundary‐layer parametrization. It is shown that this parametrization overestimates RHcrit by up to 10%, but captures the diurnal variability of RHcrit well, with lower values of RHcrit near the boundary‐layer top. While we show that the uncertainties associated with the retrievals are large, lidar observations seem promising to diagnose and evaluate a very important parameter to predict cloud fraction in climate and numerical weather prediction models.