Lighting the Night with Generative Artificial Intelligence
It addresses a limitation in meteorological observations by enabling continuous all-day weather monitoring, though it is incremental as it applies an existing generative method to a new domain-specific data gap.
This study tackled the problem of missing visible light reflectance data at night for weather monitoring by developing a generative diffusion model called RefDiff, which generates high-precision visible light reflectance from thermal infrared data, achieving an SSIM index of 0.90 and comparable performance to daytime data.
The visible light reflectance data from geostationary satellites is crucial for meteorological observations and plays an important role in weather monitoring and forecasting. However, due to the lack of visible light at night, it is impossible to conduct continuous all-day weather observations using visible light reflectance data. This study pioneers the use of generative diffusion models to address this limitation. Based on the multi-band thermal infrared brightness temperature data from the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun-4B (FY4B) geostationary satellite, we developed a high-precision visible light reflectance generative model, called Reflectance Diffusion (RefDiff), which enables 0.47~μ\mathrm{m}, 0.65~μ\mathrm{m}, and 0.825~μ\mathrm{m} bands visible light reflectance generation at night. Compared to the classical models, RefDiff not only significantly improves accuracy through ensemble averaging but also provides uncertainty estimation. Specifically, the SSIM index of RefDiff can reach 0.90, with particularly significant improvements in areas with complex cloud structures and thick clouds. The model's nighttime generation capability was validated using VIIRS nighttime product, demonstrating comparable performance to its daytime counterpart. In summary, this research has made substantial progress in the ability to generate visible light reflectance at night, with the potential to expand the application of nighttime visible light data.