A Fast and Accurate Calculation Method of Water Vapor Transmission: Based on LSTM and Attention Mechanism Model

Atmospheric water vapor plays a significant impact on the climate system, radiative transfer models, and optoelectronic engineering applications. Fast and accurate calculation of its optical depth and transmittance is a crucial step to studying the radiation characteristics of water vapor. Although the traditional physics-based, line-by-line radiative transfer model (LBLRTM) meets the accuracy requirements, it is too slow and computationally expensive for practical applications. In this study, to facilitate the accuracy and efficiency requirements of atmospheric water vapor optical depth and transmittance calculation, we propose a Stack LSTM-AT model that combines a double-layer Long Short-Term Memory (LSTM) network and a self-attention mechanism method, and different configurations of the hybrid model are extensively examined. The results show that, compared to the LBLRTM model, the Stack LSTM-AT model significantly improves computational efficiency while maintaining accuracy. Overall, the R-squared, mean absolute error (MAE), and root mean square error (RMSE) of optical depth is 0.9999945, 0.00568, and 0.02033, respectively, while the R-squared, MAE, and RMSE of atmospheric transmittance is 0.9999964, 5.5586 × 10−4, and 9.4 × 10−4, respectively. Moreover, the difference in optical depths and transmittance between the prediction results of the Stack LSTM-AT model and the calculation results of the LBLRTM are no greater than 0.3 and 0.008, respectively, across various pressures, temperatures, and water vapor amounts. The computation time for calculating the transmittance of a single spectrum (1–5000 cm−1) is about 9.784 × 10−2 s, with a spectrum resolution of 1 cm−1, which is about 1000 times faster than that of LBLRTM. The proposed Stack LSTM-AT model could significantly enhance the efficiency and accuracy of atmospheric radiative transfer simulations, demonstrating its broad potential in real-time meteorological monitoring and atmospheric component inversion. This study may provide new insights and technical support for the study of radiative transfer, climate change, and atmospheric environmental monitoring.