AI 中文总结
研究利用神经网络学习大气对流中三相质量交换，在热和机械平衡假设下制定损失函数，将其以热力学一致方式用于二维垂直切片间断伽辽金模型模拟亚公里分辨率对流三相云形成，并与基于物理表示的结果比较。
AI 中文摘要
训练神经网络以学习大气对流中水汽、液态水和冰相之间的质量交换。该网络在具有多矩微物理参数化（CASIM）的LFRic模型区域配置的对流解析输出上进行训练。学习这些相交换的损失函数在热和机械平衡（所有相温度和压力相同）以及机械非平衡（所有相吉布斯自由能不同）的假设下制定。网络输出确定水汽、液态水和冰的交换以守恒质量，并根据网络输出确定熵的变化以守恒能量。神经网络以热力学一致的方式在二维垂直切片间断伽辽金模型中实现，以模拟亚公里分辨率下对流的三相云形成。将结果与基于热力学平衡的三相湿过程物理表示的结果进行比较。
英文摘要
A discrete formulation of the multiphase moist compressible Euler equations is presented for which the energy and tracer variance conserving dynamics is coupled to the non-equilibrium moist phase exchanges in a thermodynamically consistent manner. A neural network is then trained to learn the mass exchanges between vapour, liquid and ice phases in atmospheric convection. The network is trained on convection resolving output from a regional configuration of the LFRic model with a multi-moment microphysics parameterisation (CASIM). The loss function for learning these phase exchanges is formulated under the assumptions of thermal and mechanical equilibrium (same temperature and pressure for all phases), and chemical dis-equilibrium (different Gibbs free energies for all phases). The network outputs determine the exchanges of vapour, liquid and ice so as to conserve mass, and the resulting change in entropy is determined from the network outputs so as to conserve energy. The neural network is implemented in a thermodynamically consistent manner within the discontinuous Galerkin moist compressible Euler model in order to simulate the formation of three-phase clouds for convection at sub-km resolution. The results are compared to those from a physics based representation of three-phase moist processes at thermodynamic equilibrium, as well as to an idealised configuration of LFRic model.
