Numerous modifications to the Kain‐Fritsch convective parameterization have been implemented over the last decade. These modifications are described, and the motivating factors for the changes are discussed. Most changes were inspired by feedback from users of the scheme (primarily numerical modelers) and interpreters of the model output (mainly operational forecasters). The specific formulation of the modifications evolved from an effort to produce desired effects in numerical weather prediction while also rendering the scheme more faithful to observations and cloud-resolving modeling studies.

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The Kain–Fritsch Convective Parameterization: An Update

Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

[1] A new convective parameterization is introduced that can make use of a large variety of assumptions previously introduced in earlier formulations. The assumptions are chosen so that they will generate a large spread in the solution. We then show two methods in which ensemble and data assimilation techniques may be used to find the best value to feed back to the larger scale model. First, we can use simple statistical methods to find the most probable solution. Second, the ensemble probability density function can be considered as an appropriate “prior” (a'priori density) for Bayesian data assimilation. Using this “prior”, and information about observation likelihood, measured meteorological or climatological data can be directly assimilated into model fields. Given proper observations, the application of this technique is not restricted to convective parameterizations, but may be applied to other parameterizations as well.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2002GL015311

A generalized approach to parameterizing convection combining ensemble and data assimilation techniques

¶An overview of the Regional Atmospheric Modeling System (RAMS) is presented. We focus on new developments in the RAMS physics and computational algorithms since 1992. We also summarize some of the recent applications of RAMS that includes synoptic-scale weather systems and climate studies, to small-scale research using RAMS configured as a large eddy simulation model or to even flow around urban buildings. The applications include basic research on clouds, cloud systems, and storms, examination of interactions between tropical deep convective systems and ocean circulations, simulations of tropical cyclones, extreme precipitation estimation, regional climatic studies of the interactions between the atmosphere and the biosphere or snow-covered land-surfaces, prototype realtime mesoscale numerical weather prediction, air pollution applications, and airflow around buildings.

https://rams.atmos.colostate.edu/cotton/vita/138.pdf

RAMS 2001: Current status and future directions

Preface Acknowledgements 1. Introduction 2. Atmospheric structure, composition and thermodynamics 3. The continuity and thermodynamic energy equations 4. The momentum equation in Cartesian and spherical coordinates 5. Vertical-coordinate conversions 6. Numerical solutions to partial differential equations 7. Finite-differencing the equations of atmospheric dynamics 8. Boundary-layer and surface processes 9. Radiative energy transfer 10. Gas-phase species, chemical reactions and reaction rates 11. Urban, free-tropospheric and stratospheric chemistry 12. Methods of solving chemical ordinary differential equations 13. Particle components, size distributions and size structures 14. Aerosol emission and nucleation 15. Coagulation 16. Condensation, evaporation, deposition and sublimation 17. Chemical equilibrium and dissolution processes 18. Cloud thermodynamics and dynamics 19. Irreversible aqueous chemistry 20. Sedimentation, dry deposition and air-sea exchange 21. Model design, application and testing Appendix A. Conversions and constants Appendix B. Tables References Index.

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Fundamentals of atmospheric modeling

A new cumulus parametefization is developed for ffse in mesoscale model simulations of precipitating convective systems. It is designed to estimate convective properties using a cloud model that interacts with the mesoscale model in a physically consistent manner. The cloud model is initiated by and interacts with the grid-scalecirculation without being constrained in instantaneous equilibrium with the grid-scale flow. The parameterization is designed for use in conjunction with explicit moist processes. Simulations of tropical convective lines performed using the scheme are presented in a companion paper (Part II).

Simulation of Tropical Convective Systems. Part I: A Cumulus Parameterization

Deep cumulonimbus clouds are simulated using a model that makes accurate diagnoses of entrainment and detrainment rates and of the properties of entrained and detrained air. Clouds generated by a variety of initial thermodynamic soundings are compared. In the simulations, updraft entrainment rates are large near and above cloud base, through the entire depth of the conditionally unstable layer. Stronger updrafts in a more unstable environment are better able to entrain relatively undisturbed environmental air, while weaker updrafts can entrain only air that has been modified by the clouds. When the maximum buoyancy is large, the updraft includes parcels with a wide range of buoyancies, while weaker clouds are more horizontally uniform. Strong downdrafts originate from levels at which updrafts detrain, and their mass flux depends on the mass flux of the updraft. The magnitude of mixing between cloud and environment, not the entrainment rate, varies inversely with the cloud radius. How much of the mixed air is entrained depends on the buoyancy.

/pdf/a-quantitative-investigation-of-entrainment-and-detrainment-39dess43dk.pdf

A Quantitative Investigation of Entrainment and Detrainment in Numerically Simulated Cumulonimbus Clouds

Four cumulus parameterizations in the Penn State-NCAR model MM5 are compared in idealized sea-breeze simulations, with the aim of discovering why they work as they do. The most realistic results appear to be those using the Kain-Fritsch scheme. Rainfall is significantly delayed with the Betts-Miller-Janjic scheme, due to the method of computing the reference sounding. This method can be corrected, but downdrafts should be added in a physically realistic manner. Even without downdrafts, a corrected version of the BMJ scheme produces nearly the same timing and location of deep convection as the KF scheme, despite the very different physics. In order to simulate the correct timing of the rainfall, a minimum amount of mass is required in the layer that is the source of a parameterized updraft. The Grell parameterization, in the present simulation, always derives the updraft from the top of the mixed layer, where vertical advection predominates over horizontal advection in increasing the moist static energy. This makes the application of the quasi-equilibrium closure more correct than it would be if the updrafts were always derived from the most unstable layer, but it evades the question of whether or not horizontal advection generates instability. Using different physics, the parameterizations produce significantly different cloud-top heights.

A Comparison of Cumulus Parameterizations in Idealized Sea-Breeze Simulations

A simple cloud model is developed which is designed for both diagnostic studies and mesoscale cumulus parameterization experiments. The cloud model is combined with an observed population of tropical convective updrafts and used to examine the vertical distributions of convective beating and moistening produced by tropical cloud ensembles. Although the cloud model ensembles are dominated by deep cumulonimbi, their vertical beating and moistening profiles differ significantly from those of individual clouds. These profiles and the total rainfall are sensitive to assumptions that affect the vertical mass flux distributions of the clouds. The ensemble heating and moistening profiles are in general agreement with large-scale budget analyses except for a tendency for the former to concentrate more of the heating above 600 mb. Modeled convective properties are found to be highly sensitive to assumptions concerning the convective environment immediately surrounding the updrafts and downdrafts. This has ...

Properties of Tropical Cloud Ensembles Estimated Using a Cloud Model and an Observed Updraft Population

Observations in the tropics have shown that the lapse rate of virtual Potential temperature θv normally resembles that of a reversible moist adiabat. In the present study, a mesoscale numerical model with parameterized convection is used to examine the adjustment to equilibrium of the tropical atmosphere in the presence of large-scale forcing. The physical processes that enable the model to produce an equilibrium lapse rate similar to what is observed are examined. The results indicate that a spectrum of cloud sizes, which is produced by a variable entrainment rate, is essential to enable the atmosphere to approach an equilibrium state of small conditional instability in the presence of large-scale destabilization. Lateral detrainment is not essential for modeling the large-scale stability of the atmosphere, despite its significance for modeling individual mesoscale convective systems. When downdrafts are removed from the parameterized convection, the model only temporarily produces a realistic l...

Charles Cohen

Papers

Simulation of Tropical Convective Systems. Part I: A Cumulus Parameterization

A Quantitative Investigation of Entrainment and Detrainment in Numerically Simulated Cumulonimbus Clouds

A Comparison of Cumulus Parameterizations in Idealized Sea-Breeze Simulations

Properties of Tropical Cloud Ensembles Estimated Using a Cloud Model and an Observed Updraft Population

A Numerical Study of Lapse-Rate Adjustments in the Tropical Atmosphere