Characterising the spatial variability of ice water content in and below mixed-phase clouds (B08#)

表征混合相云中及下方冰水含量的空间变化（B08

• 批准号: 457514262
• 金额: --
• 依托单位: Institut für Meteorologie
• 依托单位国家: 德国
• 项目类别: CRC/Transregios
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/457514262?language=en
• 关键词: Characterising; spatial; variability; ice; water

The processes determining spatial variability of ice water content (IWC) in mixed-phase clouds (MPCs) are not sufficiently understood. Therefore, we propose a project targeted at understanding and quantifying these processes. While it is challenging to observe MPC processes directly, we will advance techniques for quantifying IWC and snowfall rate (SR) with low uncertainty from airborne radar measurements so that we are able to observe fingerprints of the dominating processes. We will use data collected during the (AC)3 aircraft campaigns with a particular focus on the ACLOUD campaign performed in 2017, and the upcoming HALO-(AC)3 campaign planned within the current phase of (AC)3. For these campaigns, at least two closely collocated aircraft are flying in formation for obtaining collocated in situ and remote sensing observations. We will use these rare data collected during tandem flights to develop a seamless Bayesian Optimal Estimation retrieval for obtaining IWC and SR from combined radar and in situ measurements along a flight track ’curtain’. We will develop a novel retrieval approach where the in situ data are exploited not only for the observation point where they were obtained, but for the whole curtain by scaling their weight proportional to the autocorrelation lengths of the microphysical properties. By this, we can consider how the information content of the in situ instruments is reduced with increasing distance between in situ and remote sensing observation volume. Such a retrieval can combine all available information from radar and in situ observations and will close an important gap in our ability to observe the vertical and horizontal spatial variability of IWC in clouds with high accuracy and high spatial resolution. Based on the improved observations, we will link the observed IWC variability to other microphysical and macrophysical cloud properties (among others, dominating particle growth process, cloud type, liquid water content, cloud depth, cloud top phase variability, surface coupling). A particular emphasis will be put on vertical IWC variability and the resulting impact on precipitation mass fluxes. For this, we can rely on the extensive supporting aircraft data sets, but also on ground-based observations in Ny-Ålesund and during the PASCAL campaign. By this, we will identify the processes most relevant for IWC sources and sinks as well as the spatial scales on which these processes are active. Model simulations using ICON-LEM will be analysed for quantifying differences in the representation of IWC. By comparing to the same microphysical and macrophysical cloud classifications used for the observations, we will identify which model MPCs parameterisations need to be improved.

确定混合相云（MPC）中冰水含量（IWC）的空间变异性的过程还没有得到充分的理解。因此，我们提出了一个项目，旨在了解和量化这些过程。虽然直接观察MPC过程具有挑战性，但我们将推进量化IWC和降雪率（SR）的技术，使机载雷达测量的不确定性低，以便我们能够观察主导过程的指纹。我们将使用在（AC）3飞机活动期间收集的数据，特别关注2017年执行的ACLOUD活动，以及在（AC）3当前阶段计划的即将到来的HALO-（AC）3活动。在这些活动中，至少有两个紧密配置的飞机编队飞行，以获得配置在现场和遥感观测。我们将使用这些罕见的数据收集串联飞行过程中开发一个无缝的贝叶斯最优估计检索获得IWC和SR从组合雷达和现场测量沿着飞行轨迹“窗帘”。我们将开发一种新的检索方法，利用原位数据，不仅为观察点，他们获得，但为整个窗帘缩放他们的重量成比例的自相关长度的微物理特性。由此，我们可以考虑如何减少现场仪器的信息含量与现场和遥感观测体积之间的距离增加。这样的检索可以联合收割机结合所有可用的信息，从雷达和现场观测，并将关闭一个重要的差距，在我们的能力，观察垂直和水平的空间变化的IWC在云中的高精度和高空间分辨率。基于改进的观测结果，我们将把观测到的IWC变率与其他微物理和宏观物理云特性（其中包括主要粒子生长过程、云类型、液态水含量、云深度、云顶相位变率、表面耦合）联系起来。将特别强调IWC的垂直变化和由此产生的影响，降水质量通量。为此，我们可以依靠广泛的支持飞机数据集，但也可以依靠在Ny-A Mellesund和PASCAL活动期间的地面观测。通过这一点，我们将确定最相关的IWC源和汇的过程，以及这些过程是活跃的空间尺度。使用ICON-LEM的模型模拟将进行分析，以量化IWC代表性的差异。通过与用于观测的相同微物理和宏观物理云分类进行比较，我们将确定哪些模型MPC参数化需要改进。