Radiation is typically the most time-consuming physical process in numerical models. One solution is to use machine learning methods to simulate the radiation process to improve computational efficiency. From an operational standpoint, this study investigates critical limitations inherent to hybrid forecasting frameworks that embed deep neural networks into numerical prediction models, with a specific focus on two fundamental bottlenecks: coupling compatibility and long-term integration stability. A residual convolutional neural network is employed to approximate the Rapid Radiative Transfer Model for General Circulation Models (RRTMG) within the global operational system of China Meteorological Administration. We adopted an offline training and online coupling approach. First, a comprehensive dataset is generated through model simulations, encompassing all atmospheric columns both with and without cloud cover. To ensure the stability of the hybrid model, the dataset is enhanced via experience replay, and additional output constraints based on physical significance are imposed. Meanwhile, a LibTorch-based coupling method is utilized, which is more suitable for real-time operational computations. The hybrid model is capable of performing ten-day integrated forecasts as required. A two-month operational reforecast experiment demonstrates that the machine learning emulator achieves accuracy comparable to that of the traditional physical scheme, while accelerating the computation speed by approximately eightfold.

翻译：辐射过程通常是数值模式中最耗时的物理过程。一种解决方案是利用机器学习方法模拟辐射过程以提高计算效率。从业务应用角度出发，本研究探讨了将深度神经网络嵌入数值预报模式的混合预报框架存在的关键局限性，特别聚焦于两个基本瓶颈：耦合兼容性与长期积分稳定性。本研究采用残差卷积神经网络，在中国气象局全球业务系统中对通用环流模式快速辐射传输模型（RRTMG）进行近似模拟。我们采用离线训练与在线耦合相结合的策略。首先，通过模式模拟生成包含所有大气柱（有云覆盖与无云覆盖）的综合性数据集。为确保混合模式的稳定性，通过经验回放技术增强数据集，并基于物理意义施加额外的输出约束。同时，采用基于LibTorch的耦合方法，该方法更适用于实时业务计算。该混合模式能够按要求完成十天积分预报。为期两个月的业务回算实验表明，机器学习模拟器在达到与传统物理方案相当精度的同时，将计算速度提升了约八倍。