Evaluation of boundary-layer type in a weather forecast model utilizing long-term Doppler lidar observations.

Many studies evaluating model boundary-layer schemes focus on either near-surface parameters or short-term observational campaigns. This reflects the observational datasets that are widely available for use in model evaluation. In this article, we show how surface and long-term Doppler lidar observations, combined in such a way as to match model representation of the boundary layer as closely as possible, can be used to evaluate the skill of boundary-layer forecasts. We use a two-year observational dataset from a rural site in the UK to evaluate a climatology of boundary-layer type forecast by the UK Met Office Unified Model. In addition, we demonstrate the use of a binary skill score (Symmetric Extremal Dependence Index, SEDI) to investigate the dependence of forecast skill on season, horizontal resolution and forecast lead time. A clear diurnal and seasonal cycle can be seen in the climatology of both model and observations, with the main discrepancies being the model overpredicting cumulus-capped and decoupled stratocumulus-capped boundary layers and underpredicting well-mixed boundary layers. Using the SEDI skill score, the model is most skilful at predicting the surface stability. The skill of the model in predicting cumulus-capped and stratocumulus-capped stable boundary-layer forecasts is low, but greater than a 24 h persistence forecast. In contrast, the prediction of decoupled boundary layers and boundary layers with multiple cloud layers is lower than persistence. This process-based evaluation approach has the potential to be applied to other boundary-layer parametrization schemes with similar decision structures. [ABSTRACT FROM AUTHOR]