发表机构
Zhejiang University; Renmin University of China; Institute of Atmospheric Physics, Chinese Academy of Sciences（浙江大学; 中国人民大学; 中国科学院大气物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于连续动力学建模的降尺度神经算子，将粗尺度风场映射至细尺度，优于基线方法，可泛化至未见ESMs和未来气候情景，无需重新训练。
AI 中文摘要
准确的高分辨率风场模拟对于解析精细尺度的大气动力学至关重要，然而地球系统模型（ESMs）中的风场模拟仍受限于粗空间分辨率和系统性偏差。为解决这一问题，数据驱动的降尺度技术已被广泛用于增强粗分辨率ESM输出。然而，现有方法通常依赖于固定离散化，限制了在不同原生分辨率的模型之间的泛化能力。在此，我们将全球近地面风降尺度问题表述为连续大气状态场上的算子学习问题，并开发了一种降尺度神经算子，能够将粗尺度场映射到异构离散化下的细尺度对应场。基于算子学习的神经降尺度框架优于主流基线方法，恢复了细尺度物理结构，并在无需重新训练的情况下泛化到先前未见过的ESMs和未来气候情景，同时保持了长期风投影趋势。这些发现为跨多样模拟输出和未来情景的高分辨率气候降尺度建立了一种可泛化的范式。
英文摘要
Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth System Models (ESMs) remains limited by coarse spatial resolution and systematic biases. To address this, data-driven down scaling techniques have been widely used to enhance coarse-resolution ESM outputs. However, existing methods are typically tied to fixed discretizations, limiting generalization across models with different native resolutions. Here we formulate global near-surface wind downscaling as an operator-learning problem on continuous atmospheric state fields and develop a downscaling neural operator that maps coarse-scale fields to fine-scale counterparts across heterogeneous discretizations. The operator learning-based neural downscaling framework outperforms dominant baselines, recovers fine-scale physical structures, and generalizes to previously unseen ESMs and future climate scenarios without retraining, while preserving long-term wind projection trends. These findings establish a generalizable paradigm for high-resolution climate downscaling across diverse simulation outputs and future scenarios.
