🤖 AI Summary
This study addresses the limitation of existing deep learning approaches, which typically focus solely on the one-way inversion from radiance observations to atmospheric profiles while neglecting forward radiative transfer simulation and the physical consistency between observations and atmospheric states. To overcome this, the authors propose a unified bidirectional framework that jointly performs atmospheric profile inversion and radiative transfer simulation, incorporating a cycle-consistency constraint to enforce physical coupling. A novel bidirectional Mamba state-space module is designed to capture long-range dependencies across pressure levels. Trained on collocated FY-4A GIIRS observations and ERA5 reanalysis data, the model significantly outperforms state-of-the-art deep learning baselines in both temperature and humidity profile retrieval and shortwave–longwave radiative flux reconstruction, demonstrating the efficacy of the bidirectional architecture and cycle-consistency mechanism.
📝 Abstract
Hyperspectral infrared observations are an important data source for numerical weather prediction (NWP) because they provide rich information on the vertical structure of atmospheric temperature and humidity. However, most existing deep learning methods mainly focus on one-way retrieval from radiances to atmospheric profiles, while the reverse radiance simulation process and the consistency between atmospheric state space and radiance observation space are insufficiently considered. In this study, we propose SIMBA, a unified bidirectional retrieval-forward simulation framework for FY-4A GIIRS hyperspectral infrared radiance modeling toward NWP applications. The framework jointly performs atmospheric profile retrieval and radiance reconstruction, introduces a cycle-consistency constraint to strengthen the coupling between the two processes, and employs a bidirectional Mamba state-space module to capture long-range dependencies along pressure levels. Using collocated FY-4A GIIRS observations and ERA5 reanalysis data, the proposed method is evaluated for temperature retrieval, specific humidity retrieval, long-wave radiance reconstruction, and medium-wave radiance reconstruction. Experimental results show that SIMBA outperforms several representative deep learning baselines across both retrieval and reconstruction tasks, while ablation experiments confirm the contribution of the bidirectional design and cycle-consistency mechanism. These results demonstrate that the proposed framework is effective for joint atmospheric profile retrieval and hyperspectral infrared radiance modeling, and suggest potential for future Jacobian-related analysis and NWP-oriented extensions.