SIMBA: ABidirectional Retrieval Forward Simulation Framework for Modeling FY-4A GIIRS Hyperspectral Infrared Radiances Toward NWP Applications
arXiv:2606.19943v1 Announce Type: cross 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 retri
The increasing sophistication of deep learning techniques, combined with the growing demand for more accurate weather prediction, makes this development timely.
Improved hyperspectral infrared radiance modeling enhances the accuracy of numerical weather prediction, a critical element for climate modeling, disaster preparedness, and various economic sectors.
Traditional one-way retrieval methods are being superseded by more comprehensive bidirectional frameworks, leading to more robust and accurate atmospheric state inference.
- · Meteorological organizations
- · Climate change researchers
- · AI/ML model developers
- · Satellite data providers
- · Legacy weather prediction models
- · Observation-limited forecasting methods
More precise atmospheric data feeds into global weather models, leading to better short-term forecasts and long-term climate projections.
Enhanced weather prediction capabilities could reduce economic losses from extreme weather events and improve resource management.
The development of highly reliable AI-driven weather prediction systems might reduce the demand for complex, ground-based sensor networks in some regions.
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Read at arXiv cs.AI