深度学习观测算子用于人工智能天气预报模型
Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models
AI总结：
本文探讨了利用深度学习观测算子提升人工智能天气预报模型的潜力，展示其在预测数据同化中创新的有效性及在减少垂直层时的性能稳定性。
AI中文摘要：
卫星观测算子在大气数据同化中起关键作用，通过将模型状态变量转换为观测空间。先前研究表明，深度学习模拟器可有效预测经典观测算子（如社区辐射传输模型CRTM）的输出，且推理时间减少。本研究扩展了先前工作，展示将观测算子整合到人工智能（AI）天气预报模型中的潜力。具体而言，本研究表明（1）深度学习模型能有效预测数据同化模型使用的创新（或模拟与观测辐射之间的差异）；（2）深度学习观测模型在模型状态用较少垂直层表示时，性能仅略有下降。实验使用了统一预报系统（UFS）回放数据集，包括2022和2023年高级技术微波辐射计（ATMS）传感器的网格点统计插值（GSI）观测数据。代码可在https://github.com/mitre/deep-obs获取。
英文摘要：
Satellite observation operators play an essential role in atmospheric data assimilation by translating model state variables into observation space. Previous work has shown that deep-learned emulators can effectively predict the outputs of classic observation operators, like the Community Radiative Transfer Model (CRTM), with reduced inference time. This study expands previous work to show the potential for integrating observation operators into artificial intelligence (AI) weather forecasting models. Specifically, this study shows that (1) deep-learned models can effectively predict the innovations (or differences between the simulated and observed radiances) used by data assimilation models and (2) deep-learned observation models suffer only minor degradations in performance when the model state is represented with fewer vertical levels, as is commonly used by AI forecasting models. Experiments were performed using the Unified Forecast System (UFS) replay dataset, including Gridpoint Statistical Interpolation (GSI) observational data for the Advanced Technology Microwave Sounder (ATMS) sensor from 2022 and 2023. Code is available at https://github.com/mitre/deep-obs.
