Investigation of the adiabatic assumption for estimating cloud micro- and macrophysical properties from satellite and ground observations

Abstract

Abstract. Cloud properties from both ground-based as well as from geostationary passive satellite observations have been used previously for diagnosing aerosol–cloud interactions. In this investigation, a 2-year data set together with four selected case studies are analyzed with the aim of evaluating the consistency and limitations of current ground-based and satellite-retrieved cloud property data sets. The typically applied adiabatic cloud profile is modified using a sub-adiabatic factor to account for entrainment within the cloud. Based on the adiabatic factor obtained from the combination of ground-based cloud radar, ceilometer and microwave radiometer, we demonstrate that neither the assumption of a completely adiabatic cloud nor the assumption of a constant sub-adiabatic factor is fulfilled (mean adiabatic factor 0.63 ± 0.22). As cloud adiabaticity is required to estimate the cloud droplet number concentration but is not available from passive satellite observations, an independent method to estimate the adiabatic factor, and thus the influence of mixing, would be highly desirable for global-scale analyses. Considering the radiative effect of a cloud described by the sub-adiabatic model, we focus on cloud optical depth and its sensitivities. Ground-based estimates are here compared vs. cloud optical depth retrieved from the Meteosat SEVIRI satellite instrument resulting in a bias of −4 and a root mean square difference of 16. While a synergistic approach based on the combination of ceilometer, cloud radar and microwave radiometer enables an estimate of the cloud droplet concentration, it is highly sensitive to radar calibration and to assumptions about the moments of the droplet size distribution. Similarly, satellite-based estimates of cloud droplet concentration are uncertain. We conclude that neither the ground-based nor satellite-based cloud retrievals applied here allow a robust estimate of cloud droplet concentration, which complicates its use for the study of aerosol–cloud interactions.

Quantifying and correcting the effect of vertical penetration assumptions on droplet concentration retrievals from passive satellite instruments

“…These other potential error sources are numerous and include r e biases due to sub-pixel heterogeneity (Zhang and Plantnick, 2011;Zhang et al, 2012Zhang et al, , 2016; 3D radiative effects (Marshak et al, 2006); assumptions regarding the degree of cloud adiabaticity (f ad in Eqn. 10; Janssen et al, 2011;Merk et al, 2016); the choice of k value (assumed constant; Brenguier et al, 2011;Merk et al, 2016); the assumption of a vertically uniform N d ; the assumed droplet size distribution shape and width (Zhang, 2013); viewing geometry effects (Várnai and Davies, 1999;Horváth, 2004;Varnai and Marshak, 2007;Kato and Marshak, 2009;Liang et al, 2009;Di Girolamo et al, 2010;Maddux et al, 2010;Liang and Girolamo, 2013;Grosvenor and Wood, 2014;Liang et al, 2015;Bennartz and Rausch, 2017); upper level cloud and aerosol 10 layers (Haywood et al, 2004;Bennartz and Harshvardhan, 2007;Davis et al, 2009;Meyer et al, 2013;Adebiyi et al, 2015;Sourdeval et al, 2013Sourdeval et al, , 2016, etc. These errors have the potential to bias N d in a way that opposes the positive bias expected from the vertical penetration effect such that the overall biases may cancel out.…”

The HD(CP)<sup>2</sup> Observational Prototype Experiment (HOPE) – an overview

“…The technique is based on vertically pointing measurements from a millimetre-wavelength cloud radar and a microwave radiometer and produces height-resolved estimates of cloud particle effective radius and liquid water content. In addition, liquid water content profiles are produced operationally within Cloudnet (Illingworth et al, 2007), assuming either adiabatic profiles of liquid water content (LWC) between the lidar-derived cloud base and the radar-derived cloud-top or scaled-adiabatic profiles for which the adiabatic liquid water content is scaled to fit the liquid water path observed with the microwave radiometer (Merk et al, 2016).…”

“…IWC is measured 60 m below the base of the mixed-phase layer, where an observation of the falling ice particles is possible without influence of water droplets or turbulent motions. LWCs are mean values of the scaled-adiabatic approach (Merk et al, 2016) averaged over the complete height of the shallow mixed-phase top layer of the cloud where liquid water is present. As shown in Fig.…”

Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations

“…In fact, most authors use sub-adiabatic profiles and condensation rates around 80 % of their maximum value are often found in experimental studies (Wood, 2012). A recent study by Merk et al (2016) finds LWC at about 75 % of its adiabatic value in updrafts and about 60 % of its maximum adiabatic value in downdrafts.…”

“…A variety of observational case studies and process studies were also published using similar approaches for deriving CDNC (Boers et al, 2006;George and Wood, 2010;Painemal and Zuidema, 2010;Rausch et al, 2010;. Various authors have addressed shortcomings and issues related to CDNC climatologies (Merk et al, 2016;Grosvenor and Wood, 2014) as well as issues related to the cloud retrievals underlying the CDNC climatologies (Zhang and Platnick, 2011;Nakajima et al, 2010;Maddux et al, 2010;Horvath et al, 2014). Painemal and Zuidema (2011) validate MODIS-derived CDNC against in situ observations taken in the South Pacific during VOCALS-Rex (Wood et al, 2011).…”