• Korean Journal of the Atmospheric Sciences
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• 제78권5호
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• pp.1629-1645
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• 2021

Accurate numerical modeling of clouds and precipitation is essential for weather forecasting and climate change research. While size-resolved (bin) cloud microphysics models predict particle size distributions without imposing shapes, results are subject to artificial size distribution broadening owing to numerical diffusion associated with various processes. Whereas Part I of this study addressed collision-coalescence, here we investigate numerical diffusion that occurs in solving condensation and evaporation. In a parcel model framework, all of the numerical schemes examined converge to one solution of condensation and evaporation as the mass grid is refined, and the advection-based schemes are recommended over the reassigning schemes. Including Eulerian vertical advection in a column limits the convergence to some extent, but that limitation occurs at a sufficiently fine mass grid, and the number of iterations in solving vertical advection should be minimized to reduce numerical diffusion. Insubstantial numerical diffusion in solving condensation can be amplified if collision-coalescence is also active, which in turn can be substantially diminished if turbulence effects on collision are incorporated. Large-eddy simulations of a drizzling stratocumulus field reveal that changes in moments of Doppler spectra obtained using different mass grids are consistent with those obtained from the simplified framework, and that spectral moments obtained using a mass grid designed to effectively reduce numerical diffusion are generally closer to observations. Notable differences between the simulations and observations still exist, and our results suggest a need to consider whether factors other than numerical diffusion in the fundamental process schemes employed can cause such differences.

• 대기
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• 제26권3호
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• pp.435-444
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• 2016

In spite of considerable progress in the recent decades, there still remain large uncertainties in numerical cloud models. In this study, effects of uncertainty in terminal velocity of graupel on cloud simulation are investigated. For this, a two-dimensional bin microphysics cloud model is employed, and deep convective clouds are simulated under idealized environmental conditions. In the sensitivity experiments, the terminal velocity of graupel is changed to twice and half the velocity in the control experiment. In the experiment with fast graupel terminal velocity, a large amount of graupel mass is present in the lower layer. On the other hand, in the experiment with slow graupel terminal velocity, almost all graupel mass remains in the upper layer. The graupel size distribution exhibits that as graupel terminal velocity increases, in the lower layer, the number of graupel particles increases and the peak radius in the graupel mass size distribution decreases. In the experiment with fast graupel terminal velocity, the vertical velocity is decreased mainly due to a decrease in riming that leads to a decrease in latent heat release and an increase in evaporative cooling via evaporation, sublimation, and melting that leads to more stable atmosphere. This decrease in vertical velocity causes graupel particles to fall toward the ground easier. By the changes in graupel terminal velocity, the accumulated surface precipitation amount differs up to about two times. This study reveals that the terminal velocity of graupel should be estimated more accurately than it is now.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.587-598
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• 2018

The impacts of aerosol loading on surface precipitation from mid-latitude deep convective systems are examined using a bin microphysics model. For this, a precipitation case over north central Mongolia, which is a high-altitude inland region, on 21 August 2014 is simulated with aerosol number concentrations of 150, 300, 600, 1200, 2400, and $4800cm^{-3}$. The surface precipitation amount slightly decreases with increasing aerosol number concentration in the range of $150-600cm^{-3}$, while it notably increases in the range of $600-4800cm^{-3}$ (22% increase with eightfold aerosol loading). We attempt to explain why the surface precipitation amount increases with increasing aerosol number concentration in the range of $600-4800cm^{-3}$. A higher aerosol number concentration results in more drops of small sizes. More drops of small sizes grow through condensation while being transported upward and some of them freeze, thus increasing the mass content of ice crystals. The increased ice crystal mass content leads to an increase in the mass content of small-sized snow particles largely through deposition, and the increased mass content of small-sized snow particles leads to an increase in the mass content of large-sized snow particles largely through riming. In addition, more drops of small sizes increase the mass content of supercooled drops, which also leads to an increase in the mass content of large-sized snow particles through riming. The increased mass content of large-sized snow particles resulting from these pathways contributes to a larger surface precipitation amount through melting and collision-coalescence.

• 대기
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• 제22권1호
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• pp.73-85
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• 2012

The collision efficiency data for collision between graupel or hail particles and cloud drops that take into account the differences of particle density are applied to the Takahashi cloud model. The original setting assumes that graupel or hail collision efficiency is the same as that of the cloud drops of the same volume. The Takahashi cloud model is run with the new collision efficiency data and the results are compared with those with the original. As an initial condition, a thermodynamic profile that can initiate strong convection is provided. Three different CCN concentration values and therefore three initial cloud drop spectra are prescribed that represent maritime (CCN concentration = 300 $cm^{-3}$), continental (1000 $cm^{-3}$) and extreme continental (5000 $cm^{-3}$) air masses to examine the aerosol effects on cloud and precipitation development. Increase of CCN concentration causes cloud drop sizes to decrease and cloud drop concentrations to increase. However, the concentration of ice particles decreases with the increase of CCN concentration because small drops are difficult to freeze. These general trends are well captured by both model runs (one with the new collision efficiency data and the other with the original) but there are significant differences: with the new data, the development of cloud and raindrop formation are delayed by (1) decrease of ice collision efficiency, (2) decrease of latent heat from riming process and (3) decrease of ice crystals generated by ice multiplication. These results indicate that the model run with the original collision efficiency data overestimates precipitation rates.