Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities

Abstract

[1] CloudSat radar reflectivities are simulated in the Met Office global forecast model in a manner which is consistent with the CloudSat observations. The method is described and applied in an evaluation study of the model's performance over the period December 2006 to February 2007. The study uses both statistical and case study approaches and examines the model's simulation of cloud systems globally and in three regions of contrasting weather and cloud regimes: the tropical warm pool, the North Atlantic Ocean, and the stratocumulus region off the west coast of California. In general, the model shows a good representation of the vertical structure of clouds systems, although a lack of midlevel cloud is ubiquitous. The model shows a nondrizzling cloud mode and a clearly separated drizzling mode that is not seen in the observations, independent of the geographical region. The comparisons suggest that the intensity of drizzle is too high, confirming on a global basis what recent ground-based measurements have also shown. They also suggest that the parameterization of ice cloud fraction as a monotonic function of the grid box mean ice water content is not consistent with the observations.

Interpreting an evaluation of the ECMWF global model with CloudSat observations: ambiguities due to radar reflectivity forward operator uncertainties

“…Previous work in the literature evaluating model radar reflectivity has mentioned uncertainties, but as a secondary issue (e.g. Bodas-Salcedo et al, 2008). The purpose of this article is to show that an appropriate treatment of uncertainty should be a necessary part of the evaluation process to determine the origin of model error.…”

“…equivalent radar reflectivity factor). This is the approach followed, for example, by Bodas-Salcedo et al (2008), Marchand et al (2009), Zhang et al (2010), and by Wilkinson et al (2008) for lidar observations. In the second approach, the observations are transformed to model variables, often using a priori information or combining with additional data from other observing instruments, for example the CloudSat Level-2 products of cloud mask, cloud phase and water contents or the combined radar-lidar algorithm described by Delanoë and Hogan (2010).…”

Evaluation of ice cloud representation in the ECMWF and UK Met Office models using CloudSat and CALIPSO data

“…In contrast to the operational ECMWF model, the IWC variable includes both 'cloud ice' and 'precipitating snow'. The model data used are similar to those used in Bodas-Salcedo et al (2008); data are produced every 3 h from a two-time-step forecast run from each of the four analyses per day at 0000, 0600, 1200 and 1800 UTC and from each of subsequent forecast states at T+3. The instantaneous model snapshots are extracted along the CloudSat track to create a continuous time series of model profiles always within 1.5 h of the CloudSat overpass time.…”

“…(Stephens et al, 2002, found more than a factor 2-3 spread in ice water path (IWP) in models.) There are two complementary ways to assess the ability of GCMs to represent cloud properties using active remote-sensing observations: comparison of forward modelled measurements using model cloud properties to radar observations (Bodas-Salcedo et al, 2008) or lidar observations (Chiriaco et al, 2007;Wilkinson et al, 2008), or direct comparison between retrieved cloud properties from observations with the values in models (e.g. Waliser et al, 2009).…”

Doppler lidar measurements of oriented planar ice crystals falling from supercooled and glaciated layer clouds

“…Polarimetric radar measurements and in situ sampling led Hogan et al (2003a) and Field et al (2004) to suggest that concentrations of these pristine crystals were often associated with the presence of mixed-phase cloud layers, in which the crystals were nucleated and grew rapidly by vapour deposition. These supercooled layer clouds are a frequent occurrence in the atmosphere (Hogan et al, 2003b(Hogan et al, , 2004) but their microphysics is little studied (Fleishauer et al, 2002), and they are poorly simulated in numerical weather models at present (Bodas-Salcedo et al, 2008).…”