Emulating Daytime ABI Cloud Optical Properties at Night With Machine Learning

Abstract

Cloud optical property retrievals from passive satellite imagers tend to be most accurate during the daytime due to the availability of visible and near-infrared solar reflectance. Infrared (IR) channels have a relative lack of spectral sensitivity to optically thick clouds and are heavily influenced by cloud-top temperature making retrievals of cloud optical depth, cloud effective radius, and cloud water path more difficult at night. We examine whether the use of spatial context — information about the local structure and organization of cloud features — can help overcome these limitations of IR channels and provide more accurate estimates of nighttime cloud optical properties. We trained several neural networks to emulate the Advanced Baseline Imager (ABI) NOAA Daytime Cloud Optical and Microphysical Properties (DCOMP) algorithm using only IR channels. We then compared the neural networks to the NOAA operational daytime and nighttime products, and the Nighttime Lunar Cloud Optical and Microphysical Properties (NLCOMP) algorithm, which utilizes the low-light visible band on VIIRS. These comparisons show that the use of spatial context can improve estimates of nighttime cloud optical properties. The primary model we trained, U-NetCOMP, can reasonably match DCOMP during the day and significantly reduces artifacts associated with day/night terminator. We also find that U-NetCOMP estimates align more closely with NLCOMP at night compared to the nighttime NOAA operational products for ABI. Lastly, we perform a comparison with ground-based instruments and find that U-NetCOMP improves upon the nighttime operational product with some exceptions for thin cirrus clouds over cold surfaces.