OPTIMice-close: Optimal combination of Polarimetric and Triple frequency radar techniques for Improving Microphysical process understanding of cold clouds and associated rainfall

云和降水仍然是天气预报和气候模型面临的最大挑战之一。政府间气候变化专门委员会的最新报告指出，人们对冰和液态水混合物组成的云中的微物理过程知之甚少，这反过来又阻碍了它们的正确建模。这些混合相云经常在高纬度地区观测到，但在中纬度地区也经常观察到。大多数云是由温度低于冰点的地区组成的，而且中纬度地区的大部分降水是通过冰相产生的。为了更好地了解冰粒是如何成核的，它们如何成长为更大的冰粒、雪花或小行星，我们需要全面和协同的观测来验证和进一步发展这些过程的模式参数化。在这个项目中，我们的目标是将最先进的遥感技术，即扫描偏振雷达、三频雷达、雷达多普勒谱与新型现场传感器和被动遥控器结合起来。这样的传感器组合是必要的，因为使用一个传感器不可能缠绕底层复杂云过程的所有组件。新仪器与现有基础设施的结合将为我们提供一个强大的工具，以前所未有的详细程度针对这些过程。遥感观测总是对数值云模式预测的量(例如冰水含量)进行间接测量(例如雷达反射率)。因此，我们将建立一个辐射传输框架，允许基于模型输出模拟观测。通过这种方式，可以直接比较真实观测和合成观测。新的辐射传输框架的中心目标将是更好地描述冻结粒子的散射特性。为此，我们将执行数值散射计算，但也会将现有的散射数据集收集到一个开放的散射库中。最后，我们将把这个框架应用于来自德国天气预报模式的长期模拟，以及来自一维光谱模式的详细微物理模拟。利用一维模型，我们的目标是通过模拟案例研究来识别当前过程理解中的弱点，在这些案例研究中，观察揭示了特定的微物理指纹，例如通过在过冷液态水层中形成冰粒的边缘。这项研究的结果也将对改进数值天气预报和气候模式中使用的不太详细的参数化方法具有重要价值。在这个项目中，我们的目标不仅是提供一个全新的观测数据集，而且还提供新的战略，如何最好地实现从观测到改进的冷云微物理过程模型的最佳知识转移。

Clouds and precipitation are still one of the biggest challenges for weather prediction and climate models. The latest IPCC report points out that especially the microphysical processes in clouds comprised by mixtures of ice and liquid water are poorly understood which in turn hampers their proper modelling. These mixed-phase clouds are frequently observed at high latitudes but also at mid-latitudes most clouds are comprised by areas with temperatures below freezing and also most of the precipitation in mid-latitudes is produced via the ice phase.In order to make progress in a better understanding of how ice particles nucleate, how they grow to larger ice particles, snowflakes, or graupel, we need comprehensive and synergistic observations to validate and further develop model parametrizations of these processes. Within this project we aim to combine state-of-the-art remote sensing techniques i.e. scanning polarimetric radar, triple-frequency radar, radar Doppler spectra with novel in-situ sensors and passive remote sensors. Such a sensor combination is necessary because with a single sensor it is impossible to entangle all components of the underlying complex cloud processes. The combination of new instruments with existing infrastructure will provide us a powerful tool to target these processes in unprecedented detail.Remote sensing observation are always indirect measurements (e.g. radar reflectivity) of the quantities predicted by a numerical cloud model (e.g. ice water content). Therefore, we will build a radiative transfer framework which allows to simulate the observations based on model output. In this way real and synthetic observations can be directly compared. A central goal of the new radiative transfer framework will be to better characterize the scattering properties of frozen particles. For this we will perform numerical scattering calculations but also collect existing scattering datasets into an open-access scattering library.Finally, we will apply this framework to long-term simulations from the german weather forecast model but also to detailed microphysical simulations from a 1D spectral model. With the 1D model we aim to identify weaknesses in current process understanding by simulating case studies where the observations reveal a specific microphysical fingerprint e.g. by riming of ice particles within a layer of super-cooled liquid water. The results from this studies will be of great value also for the improvement of the less-detailed parametrizations used in numerical weather prediction and climate models. Within this project we aim to provide not only a completely novel observational dataset but also new strategies how an optimal knowledge transfer from observations to an improved modelling of cold cloud microphysical processes can be best achieved.