Chiu J Christine, Yang C Kevin, van Leeuwen Peter Jan, Feingold Graham, Wood Robert, Blanchard Yann, Mei Fan, Wang Jian
Department of Atmospheric Science Colorado State University Fort Collins CO USA.
Department of Meteorology University of Reading Reading UK.
Geophys Res Lett. 2021 Jan 28;48(2):e2020GL091236. doi: 10.1029/2020GL091236. Epub 2021 Jan 23.
We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine-learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.
我们引入了用于自动转换和增长速率的新参数化方法，这极大地改善了暖雨增长过程的表示。新的参数化方法利用了机器学习和优化技术，并受到最近在亚速尔群岛进行的大气辐射测量计划野外考察的原位云探针测量的约束。自动转换和增长速率新估计值的不确定性分别约为15%和5%，优于现有的参数化方法。我们的结果证实，云水和毛毛雨含水量是决定增长速率的最重要因素。然而，对于自动转换，除了云水含量和液滴数浓度外，我们发现毛毛雨数浓度在当前参数化方法中缺失的关键作用。自动转换速率与毛毛雨数浓度之间的稳健关系令人惊讶但却是真实的，并且得到了理论的进一步支持。因此，在参数化中应考虑毛毛雨数浓度，以更好地表示自动转换过程。
