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

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

Meteorological and Land Surface Properties Impacting Sea Breeze Extent and Aerosol Distribution in a Dry Environment

The properties of sea breeze circulations are influenced by a variety of meteorological and geophysical factors that interact with one another. These circulations can redistribute aerosol particles and pollution and therefore can play an important role in local air quality, as well as impact remote sensing. In this study, we select 11 factors that have the potential to impact either the sea breeze circulation properties and/or the spatial distribution of aerosols. Simulations are run to identify which of the 11 factors have the largest influence on the sea breeze properties and aerosol concentrations and to subsequently understand the mean response of these variables to the selected factors. All simulations are designed to be representative of conditions in coastal sub tropical environments and are thus relatively dry, as such they do not support deep convection associated with the sea breeze front. For this dry sea breeze regime, we find that the background wind speed was the most influential factor for the sea breeze propagation, with the soil saturation fraction also being important. For the spatial aerosol distribution, the most important factors were the soil moisture, sea‐air temperature difference, and the initial boundary layer height. The importance of these factors seems to be strongly tied to the development of the surface‐based mixed layer both ahead of and behind the sea breeze front. This study highlights potential avenues for further research regarding sea breeze dynamics and the impact of sea breeze circulations on pollution dispersion and remote sensing algorithms

Environmental Controls on Tropical Sea Breeze Convection and Resulting Aerosol Redistribution

Sea breeze fronts propagate inland from the coastline, driving convective initiation and aerosol redistribution. Forecasting sea breezes is challenging due to uncertainties in the initial conditions, as well as the covariance and interaction of various meteorological and surface parameters. Using the Regional Atmospheric Modeling System coupled to an interactive land‐surface model, we conduct an ensemble of 130 idealized cloud‐resolving simulations by simultaneously perturbing six atmospheric and four surface parameters describing the initial conditions. To identify the key parameters impacting the inland characteristics and the intensity of the sea breeze convection in a tropical rainforest, we apply statistical emulation and variance‐based sensitivity analysis techniques. This study extends a previous study which explored the impacts of various parameters on sea breeze characteristics in arid environments devoid of moist convection. Wind speed is identified as the main contributor to the inland extent, similar to the arid environment study. However, the relative impacts of surface properties on the inland extent are less significant in the moist environment where land‐surface heating can be suppressed via moist convective processes and vegetation‐atmosphere interactions. Two sea breeze‐initiated convection regimes are also identified: shallow and deep. Over the shallow regime, where convective available potential energy is limited, the inversion layer strength is the primary control of the convective intensity. Over the deep regime, boundary layer temperature exerts a robust control over the convective available potential energy and hence the convective intensity. The potential vertical redistribution of aerosols is closely related to the convective intensity

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

The energetics and magnitude of hydrometeor friction in clouds

As hydrometeors fall within or from a cloud, they reach a terminal velocity because of friction with the air through which they settle. This friction has previously been shown to result in significant vertically integrated dissipation of energy, but the nature and vertical profile of this dissipation warrant further investigation. Here, its energetic origin is discussed. It is confirmed explicitly that the dissipated energy originates from the conversion of hydrometeor potential energy during settling as suggested in an earlier study by Pauluis and Held. The magnitude of this heating is then analyzed in a cloud-resolving model simulation of tropical, aggregated convection. Maximum heating from hydrometeor friction reaches ~10 K h-1. The simulation is compared to one without hydrometeor frictional heating. For the case simulated, hydrometeor frictional heating results in a drier mean state, greater cloud cover, lessened convective mass flux, and a warmer atmosphere throughout much of the troposphere. It is suggested that the heating imparted to the atmosphere by dissipation allows the air to recover most of the energy previously expended in lofting hydrometeors

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

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

The relative impact of ice fall speeds and microphysics parameterization complexity on supercell evolution

The use of bin or bulk microphysics schemes in model simulations frequently produces large changes in the simulated storm and precipitation characteristics, but it is still unclear which aspects of these schemes give rise to these changes. In this study, supercell simulations using either a bin or a double-moment bulk microphysics scheme are conducted with the Regional Atmospheric Modeling System (RAMS). The two simulations produce very different storm morphologies. An additional simulation is run for each scheme in which the diameter-fall speed relationships for ice hydrometeors are modified to be similar to those used by the other scheme. When fall speed relationships are homogenized, the two parameterization schemes simulate similar storm morphology. Therefore, despite the use of largely dissimilar approaches to parameterizing microphysics, the difference in storm morphology is found to be related to the choice of diameter-fall speed relationships for ice hydrometeors. This result is investigated further to understand why. Higher fall speeds lead to higher mixing ratios of hydrometeors at low levels and thus more melting. Consequently, stronger downdrafts and cold pools exist in the high fall speed storms, and these stronger cold pools lead to storm splitting and the intensification of a left mover. The results point to the importance of hydrometeor fall speed on the evolution of supercells. It is also suggested that caution be used when comparing the response of a cloud model to different classes of microphysics schemes since the assumptions made by the schemes may be more important than the scheme class itself