Evaluation of boundary-layer type in a weather forecast model utilizing long-term Doppler lidar observations
Evaluation of boundary-layer type in a weather forecast model utilizing long-term Doppler lidar observations
复制标题
利用长期多普勒激光雷达观测评估天气预报模型中的边界层类型
DOI：
10.1002/qj.2444
复制
发表时间：
2014
影响因子：
8.9
通讯作者：
Harvey N
中科院分区：
文献类型：
--
作者：
Harvey N
许多评估模式边界层方案的研究都集中在近地表参数或短期观测活动上。这反映了可广泛用于模型评估的观测数据集。在这篇文章中，我们展示了如何将地面和长期的多普勒激光雷达观测结合在一起，以尽可能接近地匹配边界层的模式表示，来评估边界层预报的技巧。我们使用一个来自英国农村地区的两年观测数据集来评估英国气象局统一模型预测的边界层类型的气候学。此外，我们还使用一个二元技能得分(对称极值依赖指数，SEDI)来考察预报技能对季节、水平分辨率和预报提前期的依赖性。在模式和观测的气候学中都可以看到明显的日和季节循环，主要的差异是模式过高地预测了积云覆盖的和分离的层积云覆盖的边界层，而低估了良好混合的边界层。使用SEDI技能评分，该模型在预测表面稳定性方面最为熟练。该模式对积云和层积云稳定边界层预报的预报技巧较低，但高于24小时持续性预报。相比之下，去耦合边界层和多云层边界层的预报要低于持续性预报。这种基于过程的评估方法有可能应用于其他具有类似决策结构的边界层参数化方案。
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.