High resolution modelling of stable boundary layers over complex terrain

在复杂的地形上，与稳定的夜间条件相关的近地表温度、雾的形成和污染物的捕获通常预测得很差，但在英国，这往往与不利的经济和安全影响有关。这种情况通常与山谷内的冷空气汇集和排水流事件有关。改进对这些条件的预报，以及在粗分辨率数值天气预报（NWP）和气候模式中的表现，是英国气象局的战略重点。这个合作项目通过学术和气象局研究人员的工作来解决重要的科学问题，并将其贯彻到模型改进的操作实施中。这项科学研究具有挑战性，因为地形的小尺度意味着排水流、重力波以及由此产生的稳定边界层（SBL）变率和雾的形成都发生在小空间尺度上，这通常小于当前预测模式的分辨率。更根本的是，导致山谷中空气与高空气流分离的物理过程还没有得到很好的理解。在这个项目中，将进行高分辨率的模式模拟，并与最近的实地观测进行比较，以改进导致冷空气池和山谷雾形成过程的表现。最近的COLPEX野外活动提供了一套独特的高密度中期观测数据，覆盖了典型的英国中等尺度地形。这种方法将提供预测能力的有效改进。学术界和气象局合作者在所有阶段的整合将确保科学进步转化为改进的预测技能。

Near-surface temperatures, fog formation and pollutant trapping associated with stable night-time conditions over complex terrain are frequently poorly forecast and yet are often associated with adverse economic and safety impacts in the UK. Such conditions are often associated with cold air pooling and drainage flow episodes within the valleys. Improving forecasting of such conditions and also their representation in coarse resolution numerical weather prediction (NWP) and climate models are strategic priorities for the Met Office. This collaborative project addresses important scientific questions through work by academic and Met Office researchers and follows this through to operational implementation of model improvements. The science is challenging because the small scale of the terrain means that drainage flows, gravity waves and hence stable boundary layer (SBL) variability and fog formation all occur on small spatial scales, which are often smaller than the resolution of current forecast models. More fundamentally, the physical processes leading to the decoupling of the air in a valley from the flow aloft are not well understood. In this project high-resolution model simulations will be conducted and compared with recent field observations in order to improve the representation of processes leading to the formation of cold air pools and valley fog. The recent COLPEX field campaign provides a unique set of high density, medium term observations over moderate scale terrain typical of the UK. This approach will deliver validated improvements to forecasting capability. Integration at all stages between the academic and Met Office collaborators will ensure that the scientific advances are translated into improved forecast skill.