Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

Abstract

Abstract. The statistics of cloud base vertical velocity simulated by the non-hydrostatic mesoscale model AROME are compared with Cloudnet remote sensing observations at two locations: the ARM SGP site in central Oklahoma, and the DWD observatory at Lindenberg, Germany. The results show that AROME significantly underestimates the variability of vertical velocity at cloud base compared to observations at their nominal resolution; the standard deviation of vertical velocity in the model is typically 4-8 times smaller than observed, and even more during the winter at Lindenberg. Averaging the observations to the horizontal scale corresponding to the physical grid spacing of AROME (2.5 km) explains 70-80 % of the underestimation by the model. Further averaging of the observations in the horizontal is required to match the model values for the standard deviation in vertical velocity. This indicates an effective horizontal resolution for the AROME model of at least 10 km in the presented case. Adding a TKE-term on the resolved grid-point vertical velocity can compensate for the underestimation, but only for altitudes below approximately the boundary layer top height. The results illustrate the need for a careful consideration of the scales the model is able to accurately resolve, as well as for a special treatment of sub-grid scale variability of vertical velocities in kilometer-scale atmospheric models, if processes such as aerosol-cloud interactions are to be included in the future.

Development of two-moment cloud microphysics for liquid and ice within the NASA Goddard Earth Observing System Model (GEOS-5)

“…The range of σ w shown in Fig. 4 is in good agreement with in situ measurements of vertical velocity at cloud base in marine stratocumulus (Peng et al, 2005;Guo et al, 2008), and continental regions (Fountoukis et al, 2007;Tonttila et al, 2011), showing σ w mostly between 0.2 and 1 m s −1 . However, global measurements of σ w have not been reported.…”

Characteristics of vertical air motion in isolated convective clouds

“…However, the complexity of the vertical velocity structure in convective clouds makes the parameterization non-straightforward (Wang and Zhang, 2014). Observations show that in most of convective clouds the vertical velocity is highly variable, and consequently the detailed structure of convection cannot be resolved in many models (Kollias and Albrecht, 2010;Tonttila et al, 2011). Additionally, using the same parameterization of vertical velocity for different grid resolutions may result in different cloud and precipitation properties (Khairoutdinov et al, 2009).…”

“…Airborne volumetric Doppler radars have also been used to study the dynamic structure of convective clouds (e.g., Jorgensen and Smull, 1993;Hildebrand et al, 1996;Jorgensen et al, 2000). Remote sensing has the advantage of being able to measure the vertical velocity at different heights simultaneously (Tonttila et al, 2011), and some of the techniques can detect the strongest updraft cores in convective clouds (Heymsfield et al, 2010;Collis et al, 2013). Volumetric radars can also provide three-dimensional (3-D) structure of air motion in convective clouds Nicol et al, 2015;Jorgensen et al, 2000).…”

“…In order to reasonably simulate convective clouds, the vertical air velocity must be parameterized reliably in numerical weather prediction models (NWPMs) and global circulation models (GCMs) (Donner et al, 2001;Tonttila et al, 2011;Wang and Zhang, 2014). However, the complexity of the vertical velocity structure in convective clouds makes the parameterization non-straightforward (Wang and Zhang, 2014).…”

Statistics of vertical velocities in supercooled cloud layers over Leipzig and Praia measured with Doppler lidar

“…Shallow mixed-phase cloud layers (altocumulus, stratocumulus) have been used repeatedly for the study of the interaction between aerosols, cloud droplets, and in-cloud dynamics on the basis of in-situ or remote-sensing measurements (Fleishauer et al, 2002;Ansmann et al, 2009;Tonttila et al, 2011;Zhang et al, 2010a;Bühl et al, 2016) and extensive modeling studies (Korolev and Field, 2008;Pinsky et al, 2015). Their potential impact on Earth's climate has recently been as-30 sessed by Bourgeois et al (2016).…”