Global identification of dominant ice-particle growth in cirrus clouds using EarthCARE satellite observations

Abstract. This study applies an ice-particle growth identification method to global observations obtained from the EarthCARE satellite. The method uses 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–log₁₀υ_d diagram), where the ratio of changes in Z_e to changes in log₁₀υ_d – referred to as the slope – serves as a quantitative indicator of dominant cloud microphysical processes. The analysis investigates the impact of random noise in Doppler velocity, which is a critical issue in EarthCARE products. In particular, three major error sources are addressed: observation window mode, along-track integration length, and bias correction related to antenna thermal distortion. These factors are found to significantly affect the derivation of slope values. To minimize the influence of noise, a representative slope is defined by calculating the median Doppler velocity in each Z_e bin before applying the log₁₀ transformation. Using this revised method, EarthCARE's global observations reveal a systematic increase in the representative slope with atmospheric temperature across all latitude bands. While regional variations in slope are generally small, they nonetheless reflect distinctive microphysical characteristics specific to each region. These findings demonstrate that the EarthCARE satellite can be used to globally monitor cirrus cloud growth processes and offer a quantitative metric for evaluating the performance of climate models in representing cloud microphysics.