Using Continuous Ground-Based Radar and Lidar Measurements for Evaluating the Representation of Clouds in Four Operational Models

Abstract

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud parameterization.

A critical assessment of surface cloud observations and their use for verifying cloud forecasts

“…The model forecast of TCA or CBH is an instantaneous time-step grid-box average. Hogan et al (2009) and Bouniol et al (2010) used instantaneous profiles of cloud fraction (not diagnosed quantities), and compared these to time-aggregated cloud radar observations to achieve comparability with a given model-grid resolution. Manual SYNOP observations are more 'instantaneous' but hemispheric, i.e.…”

“…The dataset has since been widely used by e.g. Hogan et al (2009), Bouniol et al (2010), and Morcrette et al (2012) to compare several numerical weather prediction (NWP) models and parametrisation schemes. These studies represent a site-specific time series assessment which provides detailed analyses of the vertical structure and distribution of cloud-related model parameters.…”

Evaluation of two cloud parametrization schemes using ARM and Cloud‐Net observations

“…These observations have been used to evaluate a number of numerical weather prediction (NWP) models (e.g. Mace et al, 1998;Hinkelman et al, 1999;Hogan et al, 2001;Morcrette, 2002;Guichard et al, 2003;Sengupta et al, 2004;Yang et al, 2006;Illingworth et al, 2007;Hogan et al, 2009;Bouniol et al, 2010;Paquin-Ricard et al, 2010). In the work presented here, we sample four months and compare short-range forecasts with ground-based observations of liquid and ice cloud fraction at five locations.…”

“…Hogan et al, 2001;Yang et al, 2006;Illingworth et al, 2007), cloud frequency of occurrence (e.g. Mace et al, 1998;Hinkelman et al, 1999;Hogan et al, 2001Hogan et al, , 2009Bouniol et al, 2010), cloud fraction histograms (e.g. Hogan et al, 2001;Illingworth et al, 2007;Bouniol et al, 2010;PaquinRicard et al, 2010) and contingency tables and skill scores (e.g.…”

From climate research to climate compatible development: experiences and progress in the Netherlands

“…The RACMO climate model was revised (van Meijgaard et al 2012) and refined with new knowledge about the boundary layer (Baas et al 2008), aerosols (Weijers et al 2011;Ten Brink et al 2009), soil hydrology parameters (Jong et al 2008), cloud formation (Bouniol et al 2010;Schutgens and Roebeling 2009) and evaluated with European remote sensing data (Wipfler et al 2011). This was done in a multidisciplinary approach in which insights from physics, soil science and hydrology (Brauer et al 2009), derived from monitoring projects (Russchenberg et al 2011), were combined with meteorology.…”