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

多年来，激光雷达云高仪一直被用于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FP 6 Cloudnet项目期间，人们意识到云高仪可以提供准确的垂直后向散射剖面，可用于评估天气预报模型中云和气溶胶的代表性。在COST Action EG-CLIMET（www.eg-climet.org）中，气象局（MO）报告了观测到的后向散射剖面与模拟剖面的首次比较，模拟剖面使用了MO业务1.5公里预报模型（UKV）预测的云和气溶胶，并证明了部署在欧洲的数百个云高计可能是一个新的数据来源，用于初始化和验证预报。这个博士学位是及时的，因为一个新的成本行动（tinyurl.com/toprof）将于2013年10月启动，涉及14个国家气象局，具体目标是通过解决共同的校准、检索算法和数据质量问题，提供欧洲各地的实时云高计数据。到2014年底，气象组织将有43个云高计报告从34个站点观测到的真实的后向散射剖面;在9个站点，2台不同的仪器将同时运行。在CAA的资助下，MO正在采购9种更先进的仪器，能够直接测量光学消光，目的是监测未来火山爆发产生的火山灰。MO目前同化云底观测，但在未来2年内正在开发常规监测和同化的全部后向散射profiles.The博士的第一个目标是研究模拟的后向散射的误差特性，并确定任何问题的模拟器或UKV输入参数。例如，初步的工作表明，云基地是太低，在模拟的后向散射。学生可以调查对假设的云，气溶胶和微物理参数化的敏感性，以及调查预测和观测中的误差。云高仪数据及其同化与其他协同观测提供了一个独特的机会，调查气溶胶和云的相互作用，并检查是否UKV预测云和气溶胶的现实分布。雨，毛毛雨，冰，液云，气溶胶负载和相对湿度（气溶胶膨胀方面）所看到的观测和模拟的后向散射应提供重要的验证这些领域的UKV预测。最后，云高仪资料测量雾的演变能力和同化资料对雾预报的好处将被研究。学生的工作将对提高高分辨率天气预报模型的初始化有直接的、积极的好处，这反过来又会提高雾、云、雨、气溶胶和毛毛雨的预报。博士工作将涉及这些主题：第一年：比较位于同一地点的云高仪仪器的性能。定义和分析观测误差。建立了后向散射资料同化中模式与观测值差异的统计量。第二年：将前向模型扩展到冰和过冷云和雾。检查改变假设的颗粒尺寸分布和气溶胶的假设混合物对模式与观测比较的统计的影响。第三年：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。学生将花一个月，每年夏天在MO在埃克塞特与团队操作云高计网络，以获得深入的了解：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。第三年：评估结果。预报模式中参数化和资料同化方案的含义。

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.