Using an Arbitrary Moment Predictor to Investigate the Optimal Choice of Prognostic Moments in Bulk Cloud Microphysics Schemes

Abstract

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.Plain Language Summary Countless cloud droplets with a variety of sizes exist in every cloud.Since cloud models cannot keep track of every individual droplet, most models instead predict quantities such as the total mass of cloud droplets and the total number of cloud droplets inside a model grid box. The values of these quantities dictate how fast clouds grow, how spatially extensive they are, and how well they reflect sunlight. In this study we explore whether the evolution of clouds could be improved if models instead predicted other properties of the cloud droplets, such as total surface area of all droplets or total diameter of all droplets. Our results show that improvements to current cloud models are likely possible.

Limitations of Separate Cloud and Rain Categories in Parameterizing Collision‐Coalescence for Bulk Microphysics Schemes

“…An idea that has been suggested recently is that cloud processes may be better represented if different distribution moments were predicted. Igel (2019) found that the mass evolution during collision‐coalescence could be better represented by predicting the 0th, third, and eighth moments of the cloud droplet distribution rather than the 0th, third, and sixth. Different rain moment combinations were not tested.…”

“…This study makes use of the Arbitrary Moment Predictor (AMP) which was first described in Igel (2019). AMP uses the cloud microphysical parameterizations of the Hebrew University spectral bin model (Khain et al., 2004).…”

“…In Igel (2019), the liquid size distribution in AMP is split into two categories corresponding to cloud droplets and rain drops. Integral moments of the two categories are predicted separately.…”

“…We ran several test suites of collision-coalescence with a wide variety of initial conditions. The initial conditions are the same as described in Igel (2019), namely, we vary the initial mass mixing ratio from 1 g kg −1 to 5 g kg −1 in increments of 1 g kg −1 , we successively double the initial droplet concentration from 50 mg −1 -1,600 mg −1 , and we vary the shape parameter from 1 to 15 in increments of 2. With these ranges, the mass-mean diameter ranges from 10.6 to 57.6 μm.…”

“…In this study we aim to assess reasons 1–5. We will do so by employing a flexible, hybrid bulk‐bin scheme called the Arbitrary Moment Predictor (AMP; Igel, 2019). AMP and the simulations we run are described in Section 2.…”