符号蒸馏为AI对流参数化引入记忆变量，改善气候统计与降水日循环
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
气候模拟里AI参数化没记忆的老问题，这篇用一条线性微分方程就补上了，还复现出热带降水的日循环。
针对约100公里分辨率地球系统模型中子网格过程缺乏历史记忆的问题，研究提出学习可预报变量来增强AI对流参数化。
方法先用自编码器把过去状态压缩进低维潜空间，再用一条受迫多元线性常微分方程替代自编码器，得到可与大气状态一同积分的记忆变量。在Lorenz-96模型的在线测试和高分辨率大气模拟的地面降水离线测试中，线性方程方案恢复了自编码器方案的大部分增益。
相比无记忆的诊断式参数化，该方法改善了气候统计和时间结构，包括热带陆地降水的真实日循环。
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, this so far mostly involves local-in-time, diagnostic parameterizations, in which the subgrid state depends only on the current coarse state with no memory of previous states, which is unrealistic for processes such as convection that have intrinsic persistence. To address this, we enhance local-in-time parameterizations by learning prognostic variables that compactly carry important, additional past information where no explicit sub-grid information is available. First we compress past information into a low-dimensional latent space using an autoencoder, which then informs a neural network trained to parameterize targeted subgrid-scale processes. We then replace the autoencoder with symbolic equations that govern the time evolution of the latent variables, yielding additional prognostic memory variables that can be integrated alongside the resolved atmospheric state. We evaluate this approach on two systems: the Lorenz-96 model (online) and surface precipitation from high-resolution atmospheric simulations (offline). A forced multivariate linear ordinary differential equation recovers most of the added value achieved by the autoencoder-based approach in both experiments. Benchmarked against diagnostic parameterizations without memory, our memory-informed approach improves climate statistics and temporal structure, including a realistic diurnal cycle of tropical land precipitation.
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：对流参数化、气候模拟、符号蒸馏、Lorenz-96，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Jurij Schönfeld, Tom Beucler, Julien Savre, Steven Sherwood, Veronika Eyring），经 AITOP 多维评分管线结构化提炼。