All‐Sky Microwave Radiance Observation Operator Based on Deep Learning With Physical Constraints

Abstract Satellite data assimilation relies on the radiative transfer models (RTMs) to establish the relationships between model state variables and satellite radiances. However, atmospheric radiative transfer calculations are computationally expensive, especially when involving multiple‐scattering calculations in cloudy areas. In recent years, deep learning (DL) models have been increasingly applied to emulate and accelerate physical models. This study, for the first time, explores DL techniques to emulate all‐sky radiative transfer in microwave bands. The FengYun‐3E (FY‐3E) Microwave Humidity Sounder‐2 (MWHS‐2) was selected as the target instrument due to its comprehensive spectral coverage, with the radiative transfer for TOVS scattering module (RTTOV‐SCATT) serving as the reference model. Three DL architectures were trained and compared, including multilayer perceptron (MLP), Bidirectional Long Short‐Term Memory with Attention (BiLSTM‐Attention), and Transformer. The BiLSTM‐Attention architecture demonstrated superior performance in both clear‐sky and cloudy radiance simulations. This may be attributed to its bidirectional recurrent structure resembling physical radiative transfer processes and the attention mechanism's ability to link MWHS‐2 channels with corresponding vertical layers. Although DL models achieve high accuracy in forward prediction, they often struggle with instability in Jacobian calculations. To address this issue, the trained BiLSTM‐Attention model was fine‐tuned using the reference model Jacobians as physical constraints. The fine‐tuned BiLSTM‐Attention model accurately characterized radiance sensitivities to temperature, water vapor, and hydrometeors under different cloud conditions, indicating its potential to serve as a radiance observation operator in data assimilation and physical retrieval applications.