FRAGILE: Exploring the role of FRAGmentation of ice particles by combining super-partIcle modelling, Laborotary studies, and polarimEtric radar observations

在该项目中，脆弱光谱多频和极化云雷达观测将与新的拉格朗日超粒子模拟和实验室研究相结合，以研究云中的冰微物理过程。与以前的研究一致，我们在偏振雷达观测中发现了新粒子形成的迹象，特别是在树枝状生长区。多年来，冰雪颗粒的碎裂一直被认为是解释这些雷达特征的潜在候选者，但我们目前对这一过程的了解仍然非常薄弱。在Fragile，我们现在的目标是进行新的实验室实验，包括在冷室和风洞中进行碰撞实验，以直接观察和量化可能的雪花或带边粒子的碎裂。实验室的结果将被用来改进模型参数，然后通过将正向模型雷达签名与我们现有的真实云的多个月极化云雷达数据集进行比较来限制这些参数。一旦与数值昂贵的一维拉格朗日超粒子模型达到一定程度的收敛，我们将应用机器学习方法来推导在ICON模型中实现的整体格式的参数化。如果碎裂最终被证明是小冰晶的一个重要来源，我们预计它在模型中的实施将显著提高我们模拟云和降水演变的能力。

In the project FRAGILE spectral multi-frequency and polarimetric cloud radar observations will be combined with novel Lagrangian super-particle modelling, and laboratory studies to investigate ice microphysical processes in clouds. In agreement with previous studies, we find indications in polarimetric radar observations of new particle formation especially within the dendritic growth zone. Fragmentation of ice and snow particles has been debated for many years as potential candidate to explain those radar features but our current knowledge about this process is still very poor. In FRAGILE, we aim now to perform new laboratory experiments including collision experiments in a cold room and in a wind tunnel, to directly observe and quantify possible fragmentation of snowflakes or rimed particles. The results from the lab will be used to refine model parameterizations which will then be constrained by comparing the forward modelled radar signatures with our existing multi-months polarimetric cloud radar dataset of real clouds. Once a certain level of convergence is reached with the numerically expensive 1D Lagrangian super-particle model, we will apply machine learning methods to derive parameterizations for bulk schemes implemented in the ICON model. If fragmentation turns out to be a substantial source of small ice crystals, we expect that its implementation in models will provide significant improvements of our ability to simulate evolution of clouds and precipitation.