Data from: Using an arbitrary moment predictor to investigate the optimal choice of prognostic moments in bulk cloud microphysics schemes
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Most bulk cloud microphysics schemes predict up to three standard properties of hydrometeor size distributions, namely, the mass mixing ratio, number concentration, and reflectivity factor in order of increasing scheme complexity. However, it is unclear whether this combination of properties is optimal for obtaining the best simulation of clouds and precipitation in models. In this study, a bin microphysics scheme has been modified to act like a bulk microphysics scheme. The new scheme can predict an arbitrary combination of two or three moments of the hydrometeor size distributions. As a first test of the arbitrary moment predictor (AMP), box model simulations of condensation, evaporation, and collision‐coalescence are conducted for a variety of initial cloud droplet distributions and for a variety of configurations of AMP. The performance of AMP is assessed relative to the bin scheme from which it was built. The results show that no double‐ or triple‐moment configuration of AMP can simultaneously minimize the error of all cloud droplet distribution moments. In general, predicting low‐order moments helps to minimize errors in the cloud droplet number concentration, but predicting high‐order moments tends to minimize errors in the cloud mass mixing ratio. The results have implications for which moments should be predicted by bulk microphysics schemes for the cloud droplet category.
绝大多数整体云微物理方案（bulk cloud microphysics schemes）可按照方案复杂度由低到高，预测水凝物粒径分布的三类标准属性：质量混合比、数浓度与反射率因子。但目前尚无定论，该属性组合是否为模式中实现最优云与降水模拟效果的最优选择。本研究对一款分档云微物理方案（bin microphysics scheme）进行改进，使其运行逻辑趋近于整体云微物理方案。改进后的新方案可对水凝物粒径分布的任意两阶或三阶矩组合进行预测。作为对任意矩预测器（Arbitrary Moment Predictor，AMP）的首次测试，研究针对多种初始云滴分布与AMP配置场景，开展了凝结、蒸发及碰撞并合过程的箱式模式（box model）模拟。以构建AMP所依托的分档方案为参照基准，对AMP的模拟性能进行评估。结果显示，不存在可同时最小化所有云滴分布矩误差的双矩或三矩配置方案。总体而言，预测低阶矩有助于降低云滴数浓度的模拟误差，而预测高阶矩则往往可最小化云质量混合比的模拟误差。该研究结果可为云滴类别的整体云微物理方案应预测哪些矩量提供参考依据。
