云中过冷水识别的飞机观测研究:云粒子数浓度与积冰概率和球形粒子占比的相关性OACSTPCD

Identifying supercooled liquid water(SLW)in clouds is critical for weather modification,aviation safety,and at-mospheric radiation calculations.Currently,aircraft identification in the SLW area mostly depends on empirical es-timation of cloud particle number concentration(Nc)in China,and scientific verification and quantitative identifica-tion 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 Nc,the proportion of spherical particles(Ps),and the probability of icing(Pi)in supercooled stratiform and cumulus-stratus clouds is statistically analyzed.Subsequently,this study proposes a method to identify SLW areas using Nc in combination with ambient temperat-ure.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 Nc,Ps,and Pi at the temperature from 0 to-18℃.When Nc is greater than a certain threshold of 5 cm-3,there is always the SLW,i.e.,Pi and Ps are high.Evaluation results demonstrate that the TSS and TS values for Nc =5 cm-3 are higher than those for Nc＜5 cm-3,and a larger Nc threshold(＞5 cm-3)corresponds to a higher SLW iden-tification hit rate and a higher SLW content.Therefore,Nc = 5 cm-3 can be used as the minimum criterion for identi-fying the SLW in clouds at temperature lower than 0℃.The SLW identification method proposed in this study is es-pecially helpful in common situations where aircraft are equipped with only Nc probes and without the CPI and RICE.