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The representation of orographic drag remains a major source of uncertainty for numerical weather prediction (NWP) and climate models. Its accuracy depends on contributions from both the model grid‐scale orography and the subgrid‐scale orography (SSO). Different models use different source orography data sets and different methodologies to derive these orography fields. This study presents the first comparison of orography fields across several operational global NWP models. It also investigates the sensitivity of an orographic drag parameterization to the intermodel spread in SSO fields and the resulting implications for representing the Northern Hemisphere winter circulation in a NWP model. The intermodel spread in both the grid‐scale orography and the SSO fields is found to be considerable. This is due to differences in the underlying source data set employed and in the manner in which this data set is processed (in particular how it is smoothed and interpolated) to generate the model fields. The sensitivity of parameterized orographic drag to the intermodel variability in SSO fields is shown to be considerable and dominated by the influence of two SSO fields: the standard deviation and the mean gradient of the SSO. NWP model sensitivity experiments demonstrate that the intermodel spread in these fields is of first‐order importance to the intermodel spread in parameterized surface stress, and to current known systematic model biases. The revealed importance of the SSO fields supports careful reconsideration of how these fields are generated, guiding future development of orographic drag parameterizations and reevaluation of the resolved impacts of orography on the flow.

The fifth Workshop on Systematic Errors (WSE) in weather and climate models was hosted by Environment and Climate Change Canada (ECCC) on under the auspices of the Working Group on Numerical Experimentation (WGNE), jointly sponsored by the Commission of Atmospheric Sciences of the World Meteorological Organization (WMO) and the World Climate Research Programme (WCRP). This major event welcomed over 200 scientists from the weather and climate communities. The workshop primary goal was to increase the understanding of the nature and cause of systematic errors in numerical models across timescales. Out of 240 abstracts submitted to the workshop, 48 talks and 132 posters were presented.

The analysis of soil moisture for the initialization of a mesoscale numerical weather-prediction (NWP) model is considered subject to operational constraints, both in terms of computational cost and data availability. A variational technique is used to analyse the soil moisture by assimilating screen-level observations of temperature and relative humidity. We consider a simplified bi-dimensional (z and t) variational approach (simplified 2D-VAR), where the estimate of the observation operator is obtained from extra integration(s) of the numerical model. The fundamental assumptions of the method are first evaluated: linearity of the observation operator, horizontal decoupling between grid points, and truncation of the control variable space (variable decoupling), that allow the simplified 2D formalism. Thus, the variational method is applied at each grid point separately and the gain matrix is computed from finite differences given the small dimension involved. The 2D-VAR technique keeps count of the full physics of the model, so the corrections applied to the control variable are adapted to the current meteorological conditions and the grid-point characteristics (texture and vegetation), as well as to the previous soil state. The linear estimate of the observation operator is studied in detail to optimize its evaluation. The validation of the method is shown with simulated observations, and the assimilation of real observations is performed with different time-windows. A sequential assimilation cycle on a 6-hour time-window allows the comparison with the optimum interpolation technique, while a 24-hour window is considered to extend the temporal consistency of the assimilated observations in the analysis. Results from the performed analyses with the simplified 2D-VAR method show a good retrieval of soil moisture, and a comparison with other initialization methods is also provided. Copyright © 2004 Royal Meteorological Society.

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.