PROM–IMPRINT: Understanding Ice Microphysical Processes by combining multi-frequency and spectral Radar polarImetry aNd super-parTicle modelling

PROMâIMPRINT：通过结合多频和光谱雷达极化测量和超粒子建模来了解冰微物理过程

• 批准号: 408011764
• 负责人: Dr. Stefan Kneifel
• 金额: --
• 依托单位: Deutscher Wetterdienst (DWD)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/408011764?language=en
• 关键词: PROM; IMPRINT; Understanding; Ice; Microphysical

Polarimetric radar observations provide rich and detailed information about the microphysics of clouds and precipitation. Using this data is challenging, though, due to the complication of the measured signals by the myriad of possible ice particle shapes and sizes. Disentangling this conundrum is the goal of this proposal. To achieve this goal state-of-the art polarimetric radar data is collected in a dedicated field campaigne targeting wintertime stratiform mixed-phase clouds. Through the combination of multi-frequency and spectral polarimetry, with the latter providing polarimetric information as a quasi-size resolved function of Doppler velocity, these measurements give us an unprecedented level of information. The richness of this data will allow to develop empirical hypotheses regarding the dominant micophysical processes in certain regions of the cloud. Such empirical hypotheses are sometimes called polarimetric fingerprints or signatures, and here we emphasize that especially for mixed-phase clouds the understanding of these polarimetric fingerprints is still uncertain, i.e., alternativ interpretations are possible. To refine and quantify these hypotheses and to develop an in-depth understanding state-of-the-art Monte-Carlo Lagrangian particle modeling is applied. Through a hierarchy of models starting from the 3d mesoscale limited-area model ICON with bulk microphysic down to a 1d spectrally-resolved Monte-Carlo process model, the observed cases will be simulated with the goal to interpret and understand the measurements. The chain of hypothesis testing does also work in the other direction. Alternative model formulations can be critically tested against the polarimetric radar observations to validate or falsify certain assumptions in the model. To bridge the gap between models and observations a reliable forward operator is needed and developed as part of the proposal. This includes new scattering calculations, e.g. for partially rimed aggregates. Through this powerful combination of advanced observing and modeling techniques with a consistent forward operator, microphysical processes like depositional growth, aggregation, riming, and ice multiplication will be investigated and our current knowledge of these processes will be put to the test. Based on this understanding of the microphysical processes the ability of the two-moment bulk microphysical model to simulate the corresponding polarimetric fingerprints will be reviewed and improved. Such improvements of the bulk process parameterizations will become possible through the unique combination of observations and process modelling, which will provide us with an almost complete knowledge of the detailed microphysical structure even in mixed-phase clouds. The ability of the two-moment bulk microphysical model to properly simulate polarimetric observations is the pre-requisite for the assimilation of such data in numericalweather prediction models or regional reanalysis.

偏振雷达观测提供了关于云和降水的微观物理的丰富而详细的信息。然而，使用这些数据是具有挑战性的，因为无数可能的冰颗粒形状和大小使测量信号复杂化。解决这个难题是本提案的目标。为了实现这一目标，最先进的偏振雷达数据收集在一个专门的领域campaign针对冬季层状混合相云。通过多频和光谱偏振测量的结合，后者提供了作为多普勒速度的准尺寸分辨函数的偏振信息，这些测量为我们提供了前所未有的信息水平。这些数据的丰富性将使我们能够就云的某些区域的主要微观物理过程提出经验性假设。这种经验假设有时被称为极化指纹或签名，在这里我们强调，特别是对于混合相云，对这些极化指纹的理解仍然是不确定的，即，可替换的解释是可能的。为了完善和量化这些假设，并发展一个深入的理解国家的最先进的蒙特-卡罗拉格朗日粒子建模应用。通过一个层次的模型，从三维中尺度有限区域模型ICON与散装microprocessor下降到一维光谱分辨蒙特-卡罗过程模型，观察到的情况下，将进行模拟，目的是解释和理解的测量。假设检验链也在另一个方向上起作用。替代模型公式可以严格测试对极化雷达观测，以验证或证伪模型中的某些假设。为了弥合模型和观测之间的差距，需要一个可靠的前向算子，并将其作为提案的一部分加以开发。这包括新的散射计算，例如部分镶边的聚集体。通过这种先进的观测和建模技术与一致的前向算子的强大组合，微物理过程，如沉积物的生长，聚集，成霜，和冰倍增将被调查，我们目前的知识，这些过程将被付诸测试。基于这种理解的微物理过程的能力的二阶矩体微物理模型，以模拟相应的极化指纹将进行审查和改进。通过观测和过程建模的独特结合，这种批量过程参数化的改进将成为可能，这将为我们提供几乎完整的知识，即使在混合相云的详细微物理结构。二阶矩整体微物理模式正确模拟偏振观测的能力是在数值天气预报模式或区域再分析中同化这些数据的先决条件。