用机器学习建模辐射传输，提速百倍还带误差估计。
Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
- 用神经网络构建CRTM的概率性模拟器，预测亮温及误差。
- 全通道均方误差0.3K，9个红外通道在晴空下误差<0.1K。
- 模型可解释性强，物理一致性高，适合气象同化应用。
过去几十年天气预报精度的提升主要得益于卫星观测数据量的增加及其在业务预报系统中的同化。实现数据同化需要基于辐射传输模型的观测算子。尽管已有大量工作致力于提升这些模型的计算效率，但计算成本仍是瓶颈，导致大量可用数据无法用于同化。为此，我们采用机器学习构建了一个基于神经网络的社区辐射传输模型（CRTM）的概率性模拟器，应用于GOES先进基线成像仪数据。该训练后的神经网络模拟器可预测CRTM输出的亮温及其相对于CRTM的误差。所有通道平均的亮温预测均方根误差（RMSE）为0.3 K；在晴空条件下，10个红外通道中有9个的误差低于0.1 K。误差预测在广泛气象条件下均表现可靠。可解释人工智能方法表明，该模型复现了相关物理机制，增强了其在新数据上表现良好的信心。
原文摘要 · Abstract (English)
The continuous improvement in weather forecast skill over the past several decades is largely due to the increasing quantity of available satellite observations and their assimilation into operational forecast systems. Assimilating these observations requires observation operators in the form of radiative transfer models. Significant efforts have been dedicated to enhancing the computational efficiency of these models. Computational cost remains a bottleneck, and a large fraction of available data goes unused for assimilation. To address this, we used machine learning to build an efficient neural network based probabilistic emulator of the Community Radiative Transfer Model (CRTM), applied to the GOES Advanced Baseline Imager. The trained NN emulator predicts brightness temperatures output by CRTM and the corresponding error with respect to CRTM. RMSE of the predicted brightness temperature is 0.3 K averaged across all channels. For clear sky conditions, the RMSE is less than 0.1 K for 9 out of 10 infrared channels. The error predictions are generally reliable across a wide range of conditions. Explainable AI methods demonstrate that the trained emulator reproduces the relevant physics, increasing confidence that the model will perform well when presented with new data.
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