Generation of Nighttime Visible Reflectance and Its Applications

Visible-band satellite observations provide critical information on cloud structure and organization but are fundamentally unavailable at night, creating a long-standing gap in all-day Earth system monitoring. This limitation restricts the use of visible-band information in tracking cloud evolution, characterizing diurnal variability, and assessing nighttime tropical cyclone (TC) intensity and structure. Here, we present RefDiff, a diffusion-based probabilistic generative framework that generates nighttime visible reflectance by learning the statistical mapping between thermal infrared brightness temperature (BT) and daytime visible reflectance from geostationary satellites. Trained exclusively on daytime data and applied to nighttime conditions without nighttime supervision, the proposed approach generates spatially coherent, daytime-consistent visible reflectance and enables uncertainty estimation. Quantitative evaluation shows that RefDiff achieves clear accuracy improvements relative to deterministic deep-learning baselines, with the most pronounced gains for cloud systems characterized by complex structures and high optical thickness. We further show that the generated visible reflectance (GVR) significantly improves the accuracy of TC intensity estimation during nighttime. These results establish a new paradigm for all-day visible satellite observations, enabling continuous monitoring of clouds and storms across the diurnal cycle.