Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation

ABSTRACT

The prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science. However, given the uncertainties in the initial and boundary conditions and imperfections of microphysical schemes, the accurate prediction of these microphysical properties of cloud is still a big challenge. The ensemble approach may be a viable way to reduce forecast uncertainties. In this paper, a large-scale stratiform cloud precipitation process is studied by comparing results of a 10-member ensemble forecast model with aircraft observation data. By means of the ensemble average, the prediction of bulk parameters such as liquid water content and ice water content can be improved in comparison with the control member, but the particle number concentrations are still one to two orders of magnitude less than those from observations. Intercomparison of raindrop size spectra reveals a big distinction between observations and predictions for particles with a diameter less than 1000 μm.

Graphical Abstract

摘要

云中的液态含水量, 冰水含量以及粒子数浓度在大气科学领域有着重要的作用。由于初边界条件的不确定性以及微物理方案的内在缺陷, 云中微物理特征的准确预报仍然是一大挑战。集合预报方法是减少预报不确定性的有效途径。本文使用10个集合成员与飞机观测资料进行对比研究太行山东麓一次大范围层状云降水过程。研究结果发现:与控制预报相比, 集合平均对液态含水量和冰水含量的预报能力有所提高, 然而粒子数浓度的预报与观测相比存在低估, 主要是因为模式低估了小于1000微米的降水粒子。

KEYWORDS:

• Aircraft observation
• ensemble forecast model
• particle number concentration
• liquid/ice water content

关键词:

• 飞机观测
• 集合模式
• 粒子数浓度
• 液态含水量/冰水含量

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the National Key R&D Program of China [grant number 2018YFC1507900] and the Demonstration Project of Artificial Precipitation Enhancement and Hail Suppression Operation Technology at the Eastern Side of the Taihang Mountains [grant number hbrywcsy-2017-2], and was sponsored by the National Natural Science Foundation of China [grant numbers 41530427 and 41875172].