Diabatic influences on mesoscale structures in extratropical storms

非绝热对温带风暴中尺度结构的影响

• 批准号: NE/I005196/1
• 负责人: John Methven
• 金额: $ 128.57万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FI005196%2F1
• 关键词: Diabatic; influences; mesoscale; structures; extratropical

The project is aimed at a better understanding and prediction of mesoscale structures in synoptic-scale storms. Such structures include fronts, rain bands, secondary cyclones, sting jets etc, and are important because much of the extreme weather we experience (e.g. strong winds, heavy rain) comes from such regions. Weather forecasting models are able to capture some of this activity correctly, but there is much still to learn. By a combination of measurements and modelling, mainly using the Met Office Unified Model (UM), we will work to better understand how mesoscale processes in cyclones give rise to severe weather and how they can be better represented in models and better forecast. The project is organised into three broad work packages. The first of these aims to look at real mesoscale structures in the atmosphere, using high-resolution in situ and radar measurements to derive their morphology and dynamics. The key to the latter is to calculate the production of potential vorticity by diabatic processes - especially phase changes of water (vapour/liquid/ice) and air-sea fluxes of sensible and latent heat. The associated high-resolution modelling programme will use the UM to simulate a representative number of events, diagnosing the PV tendency in the model and comparing with the measurements. Sensitivity studies and further diagnostics with the model will reveal the sensitivity of the forecasts to the correct representation of these processes and the dynamical consequences of diabatically-generated PV, both on the mesoscale and larger scales. Two student projects will investigate the role of boundary-layer processes in storm behaviour and conduct a statistical investigation of mesoscale precipitation features, based on archived radar and wind profiler data. The second WP examines particular physical processes and the way these are represented in forecast models. Convection cannot be explicitly represented in current large-scale models (it is just beginning to be resolvable by high-resolution local-area models) so it needs to be parameterised. The schemes that are used are not optimised for mid-latitude storms, where convection often initiates at altitude rather than at the Earth's surface. A combination of novel diagnostics and new (or modified) schemes aimed at improving the representation of convection will be developed in this WP. Also addressed here will be the derivation of air-sea fluxes of heat and momentum from aircraft flights, and their use (as part of a larger, ongoing international project) to derive a better parameterisation for these quantities in high wind conditions. Lastly, microphysical measurements made with the FAAM aircraft will be used to derive latent heating/cooling rates as a function of the microphysical environment and used to improve the model simulations in the first WP and to improve microphysical parameterisations in the UM The final WP addresses the problem of predictability, using a combination of ensemble and data assimilation techniques. A unique archive of forecast ensembles produced at the Met Office will be exploited to determine how well the forecast ensemble actually generates realistic mesoscale features, and the skill with which this is done (using standard measures of skill). Model errors in representing convection, air-sea fluxes and microphysics will be investigated to determine their impact on the forecasts for different flow conditions. The relationship between different model variables on the mesoscale is poorly known at present and this will be investigated using ensembles and the results of the measurement programme. Finally, novel approaches to data assimilation will be investigated through a student project.

该项目旨在更好地了解和预测天气尺度风暴中的中尺度结构。这些结构包括锋面、雨带、次级气旋、刺喷流等，它们很重要，因为我们经历的大部分极端天气（如强风、暴雨）都来自这些地区。天气预报模型能够正确地捕捉到其中的一些活动，但仍有许多东西需要学习。通过测量和建模的结合，主要使用气象局统一模型（UM），我们将努力更好地了解旋风中尺度过程如何引起恶劣天气，以及如何在模型中更好地表示它们并更好地预测。该项目分为三大工作包。其中第一个目的是观察大气中真实的中尺度结构，使用高分辨率的现场和雷达测量来得出它们的形态和动态。后者的关键是计算由非绝热过程产生的位涡--特别是水（蒸汽/液体/冰）的相变和感热和潜热的海气通量。相关的高分辨率建模方案将使用UM模拟具有代表性的事件，诊断模型中的PV趋势并与测量结果进行比较。敏感性研究和进一步的诊断模型将揭示这些过程的正确表示的预测和非绝热生成的PV的动力学后果的敏感性，无论是在中尺度和更大的尺度。两个学生项目将调查边界层过程在风暴行为中的作用，并根据存档的雷达和风廓线仪数据对中尺度降水特征进行统计调查。第二个WP检查特定的物理过程和这些在预测模型中的表现方式。对流无法在当前的大规模模型中显式表示（高分辨率局部区域模型才刚刚开始解决它），因此需要对其进行参数化。所使用的方案并没有针对中纬度风暴进行优化，因为对流通常在高空而不是在地球表面开始。一个新的诊断和新的（或修改后的）计划，旨在提高对流的代表性相结合，将在这个WP。这里还将讨论从飞机飞行中推导出的热量和动量的海气通量，以及它们的使用（作为一个更大的，正在进行的国际项目的一部分），以获得更好的参数化这些量在大风条件下。最后，用FAAM飞机进行的微物理测量将用于获得作为微物理环境的函数的潜热加热/冷却速率，并用于改进第一次WP中的模型模拟和改进UM中的微物理参数化。最后一次WP使用集合和数据同化技术的组合来解决可预测性问题。将利用气象局制作的预报集合的独特档案，以确定预报集合实际生成现实中尺度特征的程度，以及完成这一任务的技能（使用标准技能措施）。将研究模型在表示对流、海气通量和微观物理方面的误差，以确定它们对不同流动条件的预报的影响。目前对中尺度上不同模式变量之间的关系知之甚少，将利用集合和测量方案的结果对此进行研究。最后，将通过学生项目研究数据同化的新方法。

项目成果

A Diabatically Generated Potential Vorticity Structure near the Extratropical Tropopause in Three Simulated Extratropical Cyclones

三个模拟温带气旋中温带对流层顶附近非绝热产生的位涡结构

• DOI: 10.1175/mwr-d-14-00092.1
• 发表时间: 2015
• 期刊: Monthly Weather Review
• 影响因子: 3.2
• 作者: Chagnon J

Diabatic processes modifying potential vorticity in a North Atlantic cyclone

• DOI: 10.1002/qj.2037
• 发表时间: 2013-07
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: J. Chagnon;S. Gray;J. Methven

Systematic model forecast error in Rossby wave structure

罗斯贝波结构的系统模型预报误差

• DOI: 10.1002/2014gl059282
• 发表时间: 2014
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Gray S

Representation of model error in a convective-scale ensemble prediction system

对流规模集合预测系统中模型误差的表示

• DOI: 10.5194/npg-21-19-2014
• 发表时间: 2014
• 期刊: Nonlinear Processes in Geophysics
• 影响因子: 2.2
• 作者: Baker L

Does the Representation of Flow Structure and Turbulence at a Cold Front Converge on Multiscale Observations with Model Resolution?

冷锋流动结构和湍流的表示是否收敛于具有模型分辨率的多尺度观测？

• DOI: 10.1175/mwr-d-16-0479.1
• 发表时间: 2017
• 期刊: Monthly Weather Review
• 影响因子: 3.2
• 作者: Harvey B