发表机构
University of Illinois Urbana-Champaign; University of Wisconsin–Madison; Sandia National Laboratories（伊利诺伊大学厄巴纳-香槟分校; 威斯康星大学麦迪逊分校; 桑迪亚国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出AeroMELD-Coag，利用尺度协变神经ODE在潜空间高效预测气溶胶凝并，以十坐标状态显著降低误差并大幅加速计算，为大气模型提供新范式。
AI 中文摘要
粒子分辨气溶胶模型保留了控制云激活、光学性质和冻结的联合尺寸-组成结构，但其计算成本限制了它们在大尺度大气模型中的应用。我们将AeroMELD从气溶胶群体的紧凑表示扩展为凝并的预报模型。由此产生的AeroMELD-Coag通过一个尺度协变神经常微分方程推进九个用于群体形状的学习坐标和一个用于总粒子数的坐标，该方程结合了已知的凝并浓度依赖性。在跨越48小时的2,000条留出轨迹中，潜形状的中位对称误差为3.5%，总粒子数的为0.52%。这个十坐标状态还保留了随时间演化的尺寸分辨组成以及相关的云凝结核（CCN）激活、光学性质和冻结分数。与评估的具有320个预报坐标的20箱分段模型相比，AeroMELD-Coag将合并平均CCN激活误差从1.98%降至1.38%，冻结分数误差从1.72%降至0.56%。在匹配的积分基准测试中，每轨迹的GPU积分时间（在批次上摊销）比两个单核CPU参考值低三个数量级以上。这些凝并结果为在紧凑的学习状态中高效推进气溶胶微物理学同时保留气溶胶混合状态信息提供了概念验证。它们为未来大尺度大气模型在实用计算预算内以更高的混合状态细节表示气溶胶微物理学奠定了基础。
英文摘要
Particle-resolved aerosol models preserve the joint size-composition structure that governs cloud activation, optical properties, and freezing, but their computational cost limits their use in large-scale atmospheric models. We extend AeroMELD from a compact representation of aerosol populations to a prognostic model of coagulation. The resulting AeroMELD-Coag advances nine learned coordinates for population shape and one for total particle number through a scale-covariant neural ordinary differential equation that incorporates the known concentration dependence of coagulation. Across 2,000 held-out trajectories spanning 48 h, median symmetric errors are 3.5% for latent shape and 0.52% for total number. This ten-coordinate state also retains the evolving size-resolved composition and associated cloud-condensation-nuclei (CCN) activation, optical properties, and frozen fraction. Compared with the evaluated 20-bin sectional model with 320 prognostic coordinates, AeroMELD-Coag lowers pooled mean CCN activation error from 1.98% to 1.38% and frozen-fraction error from 1.72% to 0.56%. In a matched integration benchmark, GPU integration time per trajectory, amortized over a batch, is more than three orders of magnitude lower than for both single-core CPU references. These coagulation results provide a proof of concept for advancing aerosol microphysics efficiently in a compact learned state while retaining information about aerosol mixing state. They establish a foundation for future large-scale atmospheric models to represent aerosol microphysics with greater mixing-state detail within practical computational budgets.
Comments42 pages, 15 figures, 10 tables, including appendices
