发表机构
University of British Columbia; Université du Québec à Montréal; University of Saskatchewan; Memorial University of Newfoundland（不列颠哥伦比亚大学; 蒙特利尔大学; 萨斯喀彻温大学; 纽芬兰纪念大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Suêtes，一个基于JAX的端到端可微分非静力有限区域大气动力核心，支持梯度诊断与模型开发，并通过多种数值实验验证其多功能性。
AI 中文摘要
我们介绍了Suêtes，一个在JAX中实现的完全可微分、非静力、有限区域大气动力核心。Suêtes包含半隐式半拉格朗日方案和带有声学子步的显式分裂Runge-Kutta方案，使其能够应用于从粗分辨率区域模拟到对流允许尺度的动力学。其地形跟随几何、空间离散化、时间积分、初始条件、侧边界处理以及包含的物理参数化方案均与反向模式自动微分兼容。因此，单次反向模式传递即可计算预报诊断对选定的初始场和边界场、物理参数、地形及计算几何的敏感性。我们提供了一系列数值实验——包括示踪源反演问题、三维飑线敏感性分析、对抗性扰动构建、ERA5驱动的下坡飓风级风事件、地形优化以及地形跟随坐标优化——展示了Suêtes作为区域及对流允许尺度下基于梯度的诊断和模型开发框架的多功能性。
英文摘要
We present Suêtes, a fully differentiable, non-hydrostatic, limited-area atmospheric dynamical core implemented in JAX. Suêtes includes both a semi-implicit semi-Lagrangian scheme and a Split-Explicit Runge-Kutta scheme with acoustic substepping, enabling applications from coarse regional simulations to convection-permitting dynamics. Its terrain-following geometry, spatial discretization, time integration, initial conditions, lateral boundary treatment, and included physical parameterizations are all compatible with reverse-mode automatic differentiation. A single reverse-mode pass can therefore compute sensitivities of forecast diagnostics to selected initial and boundary fields, physical parameters, topography, and computational geometry. We provide a series of numerical experiments---including a tracer-source inverse problem, a sensitivity analysis of a three-dimensional squall-line, adversarial perturbation construction an ERA5-driven downslope hurricane force wind event, topography optimization, and terrain-following coordinate optimization---demonstrating the versatility of Suêtes as a framework for gradient-based diagnosis and model development at regional and convection-permitting scale.
