Affecting Factors And Mechanisms Of Entrainment-mixing Process In Stratocumulus Clouds

The turbulent entrainment mixing process between clouds and environmental air is important to the radiative effects of clouds,aerosol-cloud interaction and precipitation process.However,the poor understanding of the physical process related to entrainment mixing has hindered the development of entrainment mixing mechanisms parameterizations.For example,there is a lack of systematic and quantitative research on the dependence of entrainment mixing mechanisms on horizontal sampling scales and vertical heights,which leads to the separation of the two classical theoretical models of entrainment mixing mechanisms for a long time.In order to solve this problem,the affecting factors and mechanisms of entrainment mixing process in stratocumulus clouds are studied by using aircraft observation data,numerical models,and theoretical analysis methods.The integration of two classical theoretical models of entrainment mixing process is discussed,and the variation of cloud microphysics properties after entrainment mixing are predicted by machine learning.The main results are as follows:(1)A new understanding of the influence of horizontal sampling scales on the entrainment mixing mechanisms is quantitatively revealed,and a new model of scale dependence of entrainment mixing mechanisms is constructed.The grid scales of different models range from hundreds of kilometers to tens of meters,or even smaller,and the entrainment mixing mechanisms corresponding to different horizontal scales are also different.Therefore,it is necessary to study the horizontal scale dependence of entrainment mixing mechanisms in depth.Previous researches on scale dependence of entrainment mixing mechanisms were few and mainly qualitative analysis,lacking of internal physical mechanisms exploration.Based on aircraft observations,we found that the cloud entrainment mixing mechanisms present two completely opposite scale dependent characteristics with incrasing sampling scales: it may be more inhomogeneous or more homogeneous,which overcomes the partical conclusion that the entrainment mixing mechanisms can only be more inhomogeneous in previous studies.The mechanism analysis shows that the direction and intensity of horizontal scale dependence are influenced by the microphysical properties in cloud and relative humidity of entrained environmental air,while the entrained air scale only affects the intensity of scale dependence.Based on the above analysis,a dimensionless parameter that can characterize the horizontal scale dependence is defined,and a model to study the scale dependence is constructed.The input data of the model are the number and location of cloud droplet spectrum samples and environmental air samples,the relative humidity and size of entrained environmental air,and the cloud microphysical properties far from and adjacent to the environmental air.The aircraft sampling process is simulated,the cloud microphysical quantities are averaged at different sampling scales,and the homogeneous mixing degrees are calculated.The results of the model could well reproduce the scale dependent characteristics and the effects of affecting factors.This study provides a new idea and theoretical basis for the development of scale adaptive entrainment mixing mechanimsm parameterization.(2)A new homogeneous mixing degree is defined,and the vetical distribution of entrainment mixing mechanism is explored.The vertical distribution of entrainmet mixing mechanisms is studied because it relects the evolution process of entrainmet mixing mechanisms in cloud after environmental air is entrained into cloud top.However,previous studies in this field often adopt the horizontal flight pattern,which has a low vertical resolution and hinders the in-depth analysis of vertical distribution of entrainment mixing mechanisms.In order to overcome this difficulty,this paper first constructed high-resolution cloud top horizontal stratification data with intervals of 5 m by using flight observations of sawtooth patterns near cloud top.Secondly,the factors affecting the vertical distribution of entrainment mixing mechanisms are analyzed from dynamical aspect.It is found that with increasing height,the turbulent dissipation rate decreases,the relative humidity of entrained air decreases,and the scale of entrained air increases.Therefore,entrainment mixing mechanisms change from homogeneous to inhomogeneous with the increase of height.Finally,from microphysical aspect,a new homogeneous mixing degree is defined to represent the entrainment mixing mechanisms,which overcomes the limitation that the adiabatic values in cloud need to be determined in the calculation of traditional homogeneous mixing degrees.This study provides reference and basis for understanding the interaction between thermodynamical,dynamical and microphysical processes at the top of stratocumulus clouds.(3)Try to combine the two classical models of entrainment mixing mechanisms to further explore the evolution law of entrainment mixing mechanisms in vertical direction.The homogeneous/inhomogeneous mixing model and the vertical mixing model are two independent theoretical frameworks to study entrainment mixing mechanisms.Based on the results of aircraft observations and explicit mixing parcel model,the two theoretical models are first connected in this paper.It is found that entrainment mixing process in stratocumulus clouds neither exists only at cloud top nor in the whole stratocumulus clouds.The vertical size where entrainment mixing occurs is affected by vertical velocity,simulation domain,initial number concentration,fraction of entrained air,relative humidity of entrainmental dry air and turbulent dissipation rate.Microphysics and dynamics analysis show that the entrainment mixing mechanisms change monotonically from homogeneous to inhomogeneous with the increase of height.The combination of these two theoretical models will contribute to a more comprehensive understanding of the evolution of entrainment mixing in clouds.(4)Using machine learning to predict the microphysical quantities in clouds.As can be seen from the first three studies,the entrainment mixing mechanisms are affected by many factors,which brings great challeges to the development of parameterized schemes.Machine learning is good at extracting complex nonlinear relations from data affected by multiple factors.Therefore,this paper first trains the machine learing model based on the 80% results of the explicit mixing parcel model,using the adiabatic microphysical quantities and the ambient air temperature,pressure,humidity,and other physical properties that are easy to obtain in the weather and climate models.Secondly,the model is applied to the remaining results of the explicit mixing parcel model(20%)to predict the number concentration and dispersion after entrainment mixing and evaporation processes.The results show that the prediction accuracy of machine learning model is much higher than that of traditional parameterization scheme.When the model is applied to the observation data used in this paper,the prediction results are also superior to the parameterized scheme.This study shows that it is possible to use machine learning model to replace the parameterization scheme of entrainment mxing mechanisms in weather and climate models.In this paper,we study the factors affecting the entrainment mixing mechanisms in stratocumulus clouds from horizontal and vertical perspectives,try to integrate the two classical models and introduce machine learning.The resules are helpful to systematically understand the evolution of entrainment mixing mechanisms in clouds and improve the diagnostic and prediction level of entrainment mixing mechanism and macro and micro physical properties in the models.