发表机构
Chongben Honors College, Ocean University of China; Key Laboratory of Physical Oceanography, Ministry of Education, and Institute for Advanced Ocean Study, and Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), College of Oceanic and Atmospheric Sciences, Ocean University of China; Qingdao Leice Transient Technology Co., Ltd.; College of Marine Technology, Ocean Remote Sensing Institute, Ocean University of China; Qingdao Marine and Meteorological Institute; Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China; College of Intelligent Systems Science and Engineering, and Engineering Research Center of Navigation Instruments, Ministry of Education, Harbin Engineering University; State Key Laboratory of Physical Oceanography and Artificial Intelligence（中国海洋大学崇本荣誉学院; 中国海洋大学海洋与大气科学学院; 青岛雷磁瞬态技术有限公司; 中国海洋大学海洋技术学院; 青岛海洋气象研究所; 中国海洋大学; 哈尔滨工程大学; 物理海洋学与人工智能国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于F2PY协议开发Hf2pMDA基础设施，实现Python与Fortran混合编程以融合AI与物理模型，将其应用于气候、天气数据同化系统，推动AI与科学建模发展。
AI 中文摘要
AI为推进物理数值建模提供了前所未有的机遇，其中数据同化是一项高效且至关重要的工具，可加深我们对地球系统的理解并拓展其应用。与此同时，将AI与物理建模深度融合，通过注入长期基于物理的建模发展所积累的丰富物理知识，能极大推动AI的进步。然而，由于这类物理模型通常用Fortran编写，而AI算法通常在Python中便捷设计，直接将AI算法融入物理模型（反之亦然）存在困难。在此，基于F2PY协议，我们开发了一套程序，该程序实现了可便捷运行Hf2pMDA的基础设施，以形成一个程序实体，使AI算法与物理模型能够相互调用。作为示例，在Hf2pMDA框架内，气候耦合数据同化（CDA）系统可自然升级为强耦合数据同化（SCDA）系统，且在具有不同嵌套层多尺度数据同化的多层降尺度模型中，可便捷实现1公里高分辨率天气数据同化（DA）系统。在气候SCDA系统中，耦合通用环流模型（CGCM）与多尺度滤波算法由Python主控制器（PMC）集成，该控制器调用Fortran编写的CGCM组件、弱耦合数据同化（Weakly-CDA）模块，以及Python中通过潜在空间自编码器训练数据得到的SCDA算法。在高分辨率天气DA系统中，降尺度模型由所有母域的传统Fortran DA模块和中央子域的Python自编码器（AE）DA算法组成，由PMC组织这些组件进行集成。凭借任意AI算法与物理模型深度融合的便捷实现，Hf2pMDA在推动AI与科学建模两方面均具有巨大潜力。
英文摘要
AI provides an unprecedented opportunity for advancing physics numerical modeling including data assimilation, which is a highly efficient and critically-important tool for advancing our understanding on Earth system and its applications. At the same time, deep incorporation of AI and physical modeling can make great driving to advance AI by injecting it rich physics from long time physics-based modeling development. However, since such physics models are conventionally coded in Fortran and AI algorithms usually are conveniently designed in Python, difficulties exist to directly incorporate AI algorithms into physics models, vice versa. Here, based on the F2PY protocol, we have developed a procedure that implements an infrastructure which conveniently conducts Hf2pMDA to form a program entity so that AI algorithms and physical models can invoke mutually. As examples, within Hf2pMDA, a climate coupled data assimilation (CDA) system is naturally upgraded to a strongly CDA (SCDA) system, and a 1 km high-resolution weather DA system is conveniently implemented within a multi-layer downscaling model that has multiscale DA in different nesting layers. In the climate SCDA system, a coupled general circulation model (CGCM) and a multiscale filtering algorithm is integrated by a Python main controller (PMC) that calls Fortran CGCM components and Weakly-CDA modules as well as a data-trained SCDA algorithm by latent space autoencoder in Python. In the high-resolution weather DA system, the downscaled model consisting of traditional Fortran DA modules in all mother domains and Python AE DA algorithm in the central child domain is integrated by a PMC that organizes these components. With convenient realization of deep incorporation of any AI algorithm and physics model, the Hf2pMDA has a great potential to make progress on both AI and scientific modeling.
CommentsThe initial archive: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-6479/. Here we offers our revised revision
