Satellite‐Retrieved Cloud Base Droplet Number Concentration Improved by In‐Cloud Adiabatic Fraction

Accurate retrieval of cloud droplet number concentration ( N d ) is essential for understanding aerosol‐cloud interactions (ACI) and their climatic impacts. Conventional satellite‐based N d retrieval methods typically assume adiabatic clouds (cloud adiabatic fraction ( f ad ) = 1), leading to significant underestimations, particularly in environments characterized by low f ad . Furthermore, conventional methods retrieve N d near cloud tops, often much lower than near the cloud base. This study applies the recently developed f ad retrievals to retrieve cloud base N d accurately. The retrieval is based on the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard polar orbiting satellites. The validation was done against ship‐based measurements over the ocean at the Australian Great Barrier Reef. The results indicate that the traditional adiabatic assumption resulted in cloud base N d underestimations by a factor of 2.73. To resolve this, we implemented two averaged‐ f ad methods and one pixel‐level f ad correction. Averaged‐ f ad ‐based corrections improve N d accuracy but yield higher root mean square errors (RMSE) and lower correlation coefficients ( R ) due to mean − value reliance. Pixel‐level f ad ‐based correction minimizes bias by avoiding error amplification from f ad spatial averaging. When N d retrieved by pixels with cloud optical depth larger than the 50th value, the correction optimizes the following results: slope ∼1 (0.983 ± 0.088), lowest RMSE (30.365 ± 12.212 cm −3 ), highest R (0.728, P < 0.05), narrowest metrics confidence intervals, and ±30% validated error. This study underscores the importance of pixel‐level f ad correction in satellite‐based N d retrieval, offering improved accuracy for climate modeling and weather forecasting. Future work should expand validation to diverse regions and seasons to further assess the method's generalizability and limitations.