Beyond number concentration: Application of adiabatic cloud models to infer complete vertical profiles of warm cloud microphysical properties

The CloudSat mission has made significant advancements in deriving the vertical profile of cloud properties by utilizing radar, lidar, and visible imagery in a synergistic manner. Accurate calculation of radiative fluxes and atmospheric heating rates requires a comprehensive understanding of the vertical profile of cloud properties. However, a notable limitation has been the inability of the CloudSat radar to detect low-altitude liquid clouds, while the CALIOP lidar is strongly affected by liquid water, leading to an incomplete description of the vertical profile for many liquid clouds.

In this study, we present a novel approach that combines lidar cloud top heights with MODIS cloud optical properties to derive a complete vertical profile of cloud microphysical properties for liquid clouds. Our approach builds upon established methods that use adiabatic cloud models to estimate cloud drop number concentration based on MODIS observations. The uniqueness of our approach lies in three key aspects: (1) incorporating a cloud top height constraint from CALIOP, (2) introducing a parameterization of the cloud subadiabaticity profile, and (3) developing a new parameterization for the cloud drop spectral width. We validate the adiabatic model against CloudSat radar observations of non-precipitating clouds.

This new algorithm will be integrated into the final release of the CloudSat cloud water content product to address two specific scenarios: (1) for shallow liquid clouds that go undetected by the radar and (2) for precipitating clouds where the radar often underestimates the cloud base height by incorrectly identifying precipitation echoes as cloud echoes. This represents a significant enhancement to the operational CloudSat products and also provides a roadmap for future missions, such as the ESA/JAXA EarthCARE mission and the NASA Atmosphere Observing System (AOS) mission, which aim to tackle similar challenges.