Retrieve Nighttime Cloud Microphysical Parameters from VIIRS Observations: A Physics-Informed Transformative Machine Learning Approach

Accurate retrieval of nighttime water cloud microphysical properties, including cloud optical thickness (COT) and cloud effective radius (CER), remains a long-standing challenge. This study develops a physics-informed machine-learning framework, UI-Cloud (Unified Illumination Cloud Microphysics Retrieval Framework), to retrieve nighttime COT and CER from observations of the Visible Infrared Imaging Radiometer Suite (VIIRS). The framework unifies solar and lunar illumination regimes by training on daytime data and applying physically constrained corrections to nighttime radiance inputs. The VIIRS Day/Night Band (DNB) top-of-atmosphere reflectance serves as a bridging variable linking daytime and nighttime conditions. Lookup tables generated using the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) quantify day–night brightness temperature (BT) offsets and enable dynamic correction of residual solar contributions in the 4.065 μm thermal emissive band (M13).,The daytime model demonstrates strong agreement with VIIRS standard retrievals (R = 0.98). Direct application to nighttime data results in CER overestimation, whereas incorporation of the UNL-VRTM-based M13 BT correction yields nighttime global mean values (COT = 16.0; CER = 16.8 μm) that are consistent with daytime references (14.6 and 15.9 μm). Validation against liquid water path (LWP) retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) demonstrates RMSE reduction from 261.9 to 147.9 g m⁻² and bias decrease from 180% to 62%. Retrieval accuracy is high for Moon Illumination Fraction (MIF) above 70% and remains robust down to approximately 50%, enabling the UI-Cloud framework to extend diurnal cloud microphysical observations beyond daytime-only conditions into lunar-illuminated nights.