Dominant Ice Cloud Microphysics Characterized by Simultaneous Observations of Radar Reflectivity and Doppler Velocity

Abstract This study proposes a novel process-oriented analysis method using a joint probability density function of the equivalent radar reflectivity factor ( Z e ) and Doppler velocity ( υ d ) on a common logarithmic scale ( Z e –log 10 υ d diagram) to characterize dominant microphysical processes in cirrus clouds. The method focuses on the linear relationship between Z e and the ice terminal velocity ( υ t ) on a common logarithmic scale due to their physical definition. Assuming υ t ≈ υ d in nonconvective clouds, the ratio of changes in Z e to changes in log 10 υ d , denoted as the slope (Δ Z e /Δlog 10 υ d ), is found to be explained by microphysical properties. Consequently, the principal component of the Z e –log 10 υ d diagram, sampled within a cloud layer, represents the slope in the cloud. This representative slope serves as a quantitative index to characterize key cloud microphysical processes within the cloud. In cirrus clouds, the slope is shown to be sensitive to the microphysical balance between aggregation and vapor deposition, supported by cloud microphysics theory. Ground-based radar observations reveal that the representative slope generally increases with atmospheric temperature, reflecting shifts in dominant microphysical processes. Furthermore, to elucidate differences in the representative slope, sensitivity experiments with a high-resolution climate model are performed using numerical simulations. These numerical experiments demonstrate that overestimated aggregation or suppressed vapor deposition each reduces the representative slope in distinct ways, as expected. The proposed method provides new insights into the cloud microphysics governing high clouds. Significance Statement In May 2024, a new Earth observation satellite named EarthCARE was launched, providing the first-ever global Doppler velocity data. To effectively utilize the long-term data from the EarthCARE satellite, this study proposes a new analysis method based on ground-based radar observations, designed to validate the satellite’s data and interpret cloud processes more precisely. This method aims to help reveal how ice particles grow and evolve inside high-altitude clouds. With long-term data from EarthCARE , this approach has the potential to significantly reduce uncertainties in climate change projections and improve the prediction of extreme weather events, such as tropical cyclones.