Discovering the dynamics of cloud development through the embedding space of a self-supervised neural network

该项目的目的是改进最近开发的用于研究云如何演变的新技术[1]，并通过使用该技术来研究冷空气爆发的动力学[2]，将该技术应用于一个新的领域。该技术使用自监督学习来训练神经网络对云的观察和/或模拟，以创建这些输入的低维表示，然后使用拓扑方法来研究云在这个低维所谓的嵌入空间中的发展。该技术已经（并且正在）应用于EUREC 4A项目[3]，以1）产生有史以来第一张所有可能形式的对流云组织的“地图”，2）研究不同形式云组织模式的大气条件; 3）绘制组织模式之间的转换图。冷空气爆发的研究涉及了解什么影响浅层混合云的形成和发展，当冷空气从寒冷的陆地上流出，遇到相对温暖的海洋沃茨时形成的阶段性云。虽然已知海洋提供高的感热和潜热通量，随着时间的推移导致云分解成中尺度细胞结构，但中尺度组织的这种转变究竟是如何发生的，特别是它将如何受到温暖气候的影响，仍然是未知的。迫切需要更好地了解冷空气爆发中云的发展是什么影响，因为这些浅云是气候模型中最差的云之一[4]，北极目前变暖速度比世界上任何其他地方都快[5]，气候模型预测的速度更快[6]。特别重要的是这些云的辐射效应，随着中尺度组织的演变而变化，因为这直接影响地球的能量平衡。根据申请人的兴趣，博士项目可以采取许多方向，使项目更加技术或应用驱动。这项技术提供了一个独特的机会，可以绘制出在冷空气爆发期间云形态的变化。有了这种冷空气爆发动力学的时间映射，可以研究驱动因素和影响因素。此外，这一技术还可扩展到进一步吸收遥感和实地观测，如云的垂直横截面（例如来自地球关怀卫星和COMBLE实地活动的雷达/激光雷达测量）。随着进一步数据集的纳入，该项目可以研究冷空气爆发的全部空间和时间演变。该技术还可以扩展到用于测量大涡模拟和云解析模拟中冷空气爆发的代表性，以确定模型能够捕获这些云的发展。最后，发展了流形提取和遍历方法、神经网络结构和训练方法。

The aim of this project is to improve on a recently developed novel technique for studying how clouds evolve [1] and to apply this technique to a new domain by using it to study the dynamics of cold air out-breaks [2]. The technique uses self-supervised learning to train a neural network on observations and/or simulations of clouds, to create a low-dimensional representation of these inputs and then uses topological methods to study the development of clouds in this low-dimensional so-called embedding space. The technique has been (and is being) applied within the EUREC4A project [3] to 1) produce the first ever "map" of all possible forms of convective cloud organisation, 2) to study the atmospheric conditions of different forms of cloud organisation patterns and 3) to map out transitions between organisation regimes.The study of cold-air outbreaks concerns learning what effects the formation and development of shallow mixed-phased clouds which form as cold air flowing off cold land-masses meets relatively warm ocean waters. Although it is known that the ocean provides high sensible and latent heat fluxes which over time leads to a break-up of clouds into mesoscale cellular structures, exactly how this transition in mesoscale organisation occurs and in particular how it will be effected by a warmer climate, is still unknown. There is an urgent need to better understand what effects the development of clouds in cold-air outbreaks as these shallow clouds are among the poorest represented in climate models [4], and the Arctic is currently warming faster than anywhere else in the world [5], and faster predicted by climate models [6]. Of particular importance is the radiative effect of these clouds, which changes with evolution of the mesoscale organisation, as this directly impacts the Earth's energy balance.There are numerous directions in which the PhD project could be taken depending on the interests of the applicant, making the project either more technique or application driven. The technique presents a unique opportunity to map out how changes in cloud morphology occur during cold-air outbreaks. With this temporal mapping of the dynamics of cold-air outbreaks the drivers and influencing factors can be studied. In addition the technique could be extended to ingest further remote sensing and in-situ observations such as vertical cloud cross-sections (for examples from the EarthCare satellite and RADAR/LIDAR measurements from the COMBLE field campaign). With the inclusion of further datasets the project could study the full spatial and temporal evolution of cold-air outbreaks. The technique could also be extended to be used to measure the representation of cold-air outbreaks in Large-Eddy Simulations and Cloud-Resolving simulations, to assert how well models are able to capture the development of these clouds. And finally, the manifold extraction and traversal methods, the neural network architecture and training methods employed in the technique could be developed.