Identifying Supercooled Liquid Water in Cloud Based on Airborne Observations: Correlation of Cloud Particle Number Concentration with Icing Probability and Proportion of Spherical Particles

Identifying supercooled liquid water (SLW) in clouds is critical for weather modification, aviation safety, and atmospheric radiation calculations. Currently, aircraft identification in the SLW area mostly depends on empirical estimation of cloud particle number concentration (N_c) in China, and scientific verification and quantitative identification criteria are urgently needed. In this study, the observations are from the Fast Cloud Droplets Probe, Rosemount ice detector (RICE), and Cloud Particle Imager (CPI) onboard a King Air aircraft during seven flights in 2018 and 2019 over central and eastern China. Based on this, the correlation among N_c, the proportion of spherical particles (P_s), and the probability of icing (P_i) in supercooled stratiform and cumulus-stratus clouds is statistically analyzed. Subsequently, this study proposes a method to identify SLW areas using N_c in combination with ambient temperature. The reliability of this method is evaluated through the true skill statistics (TSS) and threat score (TS) methods. Numerous airborne observations during the seven flights reveal a strong correlation among N_c, P_s, and P_i at the temperature from 0 to −18°C. When N_c is greater than a certain threshold of 5 cm⁻³, there is always the SLW, i.e., P_i and P_s are high. Evaluation results demonstrate that the TSS and TS values for N_c = 5 cm⁻³ are higher than those for N_c < 5 cm⁻³, and a larger N_c threshold (> 5 cm⁻³) corresponds to a higher SLW identification hit rate and a higher SLW content. Therefore, N_c = 5 cm⁻³ can be used as the minimum criterion for identifying the SLW in clouds at temperature lower than 0°C. The SLW identification method proposed in this study is especially helpful in common situations where aircraft are equipped with only N_c probes and without the CPI and RICE.