Using vertical profiles of atmospheric backscatter as observed with the Met Office ceilometer network to improve high resolution weather forecasts.

激光雷达天花板多年来一直被用来通过发射一系列激光脉冲并观察反射信号来测量云底高度，但在欧盟的FP6 Cloudnet项目中，人们意识到天花板可以提供准确的垂直后向散射廓线，可用于评估天气预报模式中云和气溶胶的表现。在成本行动EG-CLIMET(www.ig-peatet.org)中，英国气象局(MO)报告了首次将观测到的后向散射廓线与MO业务1.5公里预报模式(UKV)预测的云和气溶胶模拟廓线进行比较，并证明在欧洲部署的数百个天花板可能成为预报初始化和验证的新数据来源。这次PHD是及时的，因为一项新的成本行动(tinyurl.com/toprof)将于2013年10月开始，涉及14个国家气象服务，具体目标是通过解决常见的校准、检索算法和数据质量问题，在整个欧洲提供实时的Ceileter数据。到2014年底，MO将有43个天花板报告从34个站点实时观测到的后向散射分布；在9个站点，2台不同的仪器将并行运行。在CAA的资助下，气象局正在采购9台更先进的仪器，能够直接测量光学消光，明确的目标是监测未来火山喷发产生的火山灰。目前MO同化Ceileter云基观测，但在接下来的两年里正在发展常规的监测和同化整个后向散射廓线。PHD的第一个目标是研究模拟后向散射的误差特征，并找出模拟器或UKV输入参数的任何问题。例如，初步工作表明，在模拟的后向散射中，云底太低。学生可以调查对假设的云、气溶胶和微物理参数的敏感性，以及调查预报和观测中的错误。Ceileter数据及其同化以及其他协同观测提供了一个独特的机会来研究气溶胶和云的相互作用，并检查UKV是否预测了云和气溶胶的真实分布。观测和模拟的后向散射所看到的雨、毛毛雨、冰、液云、气溶胶负荷和相对湿度(根据气溶胶膨胀)应该为UKV预报中的这些场提供重要的验证。最后，将研究Ceileter数据测量雾演变的能力以及数据同化对雾预报的益处。该学生的工作将对改进高分辨率天气预报模式的初始化产生直接的、积极的好处，这反过来将改善对雾、云、雨、气溶胶和毛毛雨的预报。博士工作将解决这些主题：第一年：比较位于同一位置的Ceileter仪器的性能。定义和分析观测误差。建立后向散射数据同化模式与观测值差异的统计量。第2年：将前向模式扩展到冰云、过冷云和雾。检查改变假定的颗粒大小分布和假定的气溶胶混合物对模型与观测值比较的统计量的影响。第3年：评估包括后向散射观测对业务数据同化和数值天气预报验证和制定的潜在影响。第四年：综合结果。写论文。学生每年夏天将在埃克塞特的MO与运营Ceileter网络的团队一起度过一个月，以深入了解：第一年：激光雷达散射、仪器和校准误差；第二年：数据质量问题。云底高度的测定。校准的稳定性。第3年：结果评估。预报模式中的参数化和数据同化方案的含义。

Lidar ceilometers have been used for many years to measure the height of cloud base by transmitting a series of laser pulses and observing the reflected signal, but during the EU FP6 Cloudnet project it was realised that ceilometers can provide accurate vertical backscatter profiles which can be used to evaluate the representation of clouds and aerosols in weather forecast models.In COST Action EG-CLIMET (www.eg-climet.org) the Met Office (MO) reported the first comparisons of observed backscatter profiles with simulated profiles using cloud and aerosol predicted by the MO operational 1.5km forecast model (UKV) and demonstrated that the hundreds of ceilometers deployed in Europe could be a novel source of data for initialisation and validation of forecasts. This PhD is timely because a new COST action (tinyurl.com/toprof) involving 14 national weather services will start in October 2013 with the specific aim of providing real-time ceilometer data across Europe by addressing common calibration, retrieval algorithms and data quality issues.By the end of 2014 the MO will have 43 ceilometers reporting observed backscatter profiles in real time from 34 sites; at 9 sites 2 different instruments will operate side by side. With CAA funding, the MO is procuring 9 more advanced instruments able to directly measure optical extinction with the express aim of monitoring ash from future volcanic eruptions. The MO currently assimilates ceilometer cloud-base observations but over the next 2 years is developing routine monitoring and assimilation of the full backscatter profiles.The first aim of the PhD is to study the error characteristics of the simulated backscatter and identify any problems with the simulator or the UKV input parameters. For instance, preliminary work indicates that cloud base is too low in the simulated backscatter. The student can investigate sensitivity to assumed cloud, aerosol and microphysics parametrizations as well as investigate errors in the forecasts and the observations. The ceilometer data and its assimilation alongside other synergistic observations provide a unique opportunity to investigate aerosol and cloud interactions and to check whether the UKV predicts realistic distributions of clouds and aerosols. Rain, drizzle, ice, liquid clouds, aerosol loading and relative humidity (in terms of aerosol swelling) as seen by observed and simulated backscatter should provide important verification of those fields in UKV forecasts. Finally, the ability of the ceilometer data to measure the evolution of fog and the benefit of the assimilation of the data to fog forecasting will be investigated. The student's work will have a direct, positive benefit of improving initialisation of high-resolution weather forecast models which in turn will improve forecasts of fog, clouds, rain, aerosol and drizzle.The PhD work will address these topics: Year 1: Compare performance of co-located ceilometer instruments. Define and analyse observation errors. Establish the statistics of differences between model and observation for backscatter data assimilation. Year 2: Extend forward model to ice and super-cooled clouds and fog. Examine effect of changing the assumed particle size distributions and the assumed mixture of aerosols on the statistics of model comparisons with observations. Year 3: Evaluate potential impact of including backscatter observations on operational data assimilation and numerical weather prediction validation and formulation. Year 4: Synthesize results. Write thesis. Student will spend one month each summer at the MO in Exeter with the team operating the ceilometer network to gain an in-depth understanding of: Year 1: Lidar scattering, instrumental and calibration errors Year 2: Data quality issues. Determination of cloud base height. Stability of calibration. Year 3: Assessment of results. Implications for parametrization and data assimilation schemes in forecast models.