Derivation of aerosol profiles for MC3E convection studies and use in simulations of the 20 May squall line case

Fridlind, A. M.; Li, Xiaowen; Wu, Di; Lier-Walqui, Marcus van; Ackerman, A. S.; Wei, Tao; McFarquhar, Greg M.; Wu, Weihong; Dong, Xiquan; Wang, Jingyu; Ryzhkov, Alexander V.; Zhang, Pengfei; Poellot, Michael R.; Neumann, A.; Tomlinson, Jason

doi:10.5194/acp-17-5947-2017

Cited by 41 publications

(46 citation statements)

References 88 publications

(128 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present study, the background CN distribution is a sum of three log‐normal distributions with a concentration of (representing the three modes) 1,000, 800, and 0.72 cm −3 ; the resulting total CN concentration was ~1,800 cm −3 . This concentration agrees with surface measurements (Fridlind et al, ; Marinescu et al, ). The initial CN size distribution near the surface used in the simulations is shown in Figure b.…”

Section: Simulation Designsupporting

confidence: 87%

“…While FSBM‐2 realistically simulates ice number distributions in the stratiform region at altitudes down to ~6.7 km, the simulated number concentration of ice particles decreases down to 5.8 km at a stronger rate than the observations, as analyzed by Wang et al () and also shown by Fridlind et al (). Note that the substantial downward decrease of ice particle concentration in the stratiform region (above the melting level) was simulated by all microphysical schemes tested in the intercomparison study by Han et al ().…”

Section: Conclusion and Discussionmentioning

confidence: 82%

“…Thus, the high concentrations of ice crystals in cloud updrafts are caused by activation of the smallest CCN. Note that artificial limitation of the ice number concentration (for example, by~500 per liter as applied in Fridlind et al, 2017) may prevent from seeing the large ice concentration effect in cloud anvils. Lower concentration of ice particles is seen in H43 compared with H33 and G33, which can be caused by more intense riming within the convective area in this simulation.…”

Section: Overall Microphysical Properties and Convective Intensitymentioning

confidence: 99%

“…One of the most difficult problems in numerical simulation of MCSs is the accurate reproduction of size distributions of ice particles and liquid drops (Fridlind et al, 2017). Figure 14 shows mass size distributions of drops and hail (or graupel in G33) in the convective region, as well as drops and snow mass distributions in the stratiform region at t = 10 hr UTC.…”

Section: Size Distributions and Precipitationmentioning

confidence: 99%

See 3 more Smart Citations

Simulating a Mesoscale Convective System Using WRF With a New Spectral Bin Microphysics: 1: Hail vs Graupel

et al. 2019

Self Cite

View full text Add to dashboard Cite

A modified Fast Spectral Bin Microphysics scheme (FSBM‐2) embedded into the Weather Research and Forecasting (WRF) model is used to simulate a mesoscale deep convective system observed during the Midlatitude Continental Convective Clouds Experiment (MC3E). FSBM‐2 uses modified source codes as compared to the current FSBM (FSBM‐1). In contrast to FSBM‐1, FSBM‐2 can simulate hail of several centimeters in diameter and includes additional processes such as spontaneous breakup of raindrops and aerosol regeneration by drop evaporation. It is shown that allowing large hail particles of diameters exceeding about 1 cm substantially increases the agreement between the simulated and observed squall‐line structures in both the convective and stratiform regions. In contrast, if graupel particles are used to represent high‐density hydrometeors in convective areas, the ratio of convective‐to‐stratiform areas diverges from the ratio seen in observations and maximum radar reflectivities are substantially underestimated. Analysis of snow size distributions in the stratiform area shows an important link between the ice crystals formed by homogeneous freezing in the convective area to ice particle number concentration in the stratiform region. Simulated raindrop size distributions in the stratiform area below the melting level from FSBM‐2 show a good agreement with observations. The regeneration of cloud condensational nuclei (CCN) by droplet evaporation and detrainment of these CCN to the stratiform region increases the concentration of CCN there up to 8‐ to 9‐km altitude; the additional CCN penetrate clouds and produce new droplets, leading to some convection intensification.

show abstract

Section: Simulation Designsupporting

confidence: 87%

Section: Conclusion and Discussionmentioning

confidence: 82%

Section: Overall Microphysical Properties and Convective Intensitymentioning

confidence: 99%

Section: Size Distributions and Precipitationmentioning

confidence: 99%

See 2 more Smart Citations

Simulating a Mesoscale Convective System Using WRF With a New Spectral Bin Microphysics: 1: Hail vs Graupel

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…One can see that maximum concentrations of crystals and aggregates (snow) are reached at the diameters of 60-70 μm and 300-400 μm, respectively. These values are typical for distributions of these hydrometeors (Fridlind et al, 2017;Lawson et al, 2015). Thus, both mechanisms of ice multiplication lead to nearly similar size distributions of ice particles.…”

Section: 1029/2019jd031312mentioning

confidence: 61%

The Role of Ice Splintering on Microphysics of Deep Convective Clouds Forming Under Different Aerosol Conditions: Simulations Using the Model With Spectral Bin Microphysics

Хаин

Phillips

et al. 2020

JGR Atmospheres

View full text Add to dashboard Cite

Observations during the Ice in Clouds Experiment‐Tropical (ICE‐T) field experiment show that the ice particles concentration in a developing deep convective clouds at the level of T = −15 °C reached about 500 L−1, that is, many orders higher than that of ice‐nucleating particle. To simulate microphysics of these clouds, the 2‐D Hebrew University Cloud model (HUCM) with spectral bin microphysics was used in which two main types of ice multiplication mechanisms were included in addition to the Hallet‐Mossop mechanism. In the first ice multiplication mechanism ice splinters form by drop freezing and drop‐ice collisions. Ice multiplication of this type dominates during developing stage of cloud evolution, when liquid water content is significant. At later stage when clouds become nearly glaciated, ice crystals are produced largely by ice splintering during ice‐ice collisions (the second ice multiplication mechanism). Simulations show that droplet size distributions, as well as size distributions of ice particles, agree well with the measurements during ICE‐T. Simulations with different cloud condensation nuclei concentrations show the existence of the “optimum” cloud condensation nuclei concentration (or droplet concentration), at which concentration of ice splinters reaches maximum. In these simulations ice nucleation caused by the direct formation of ice crystals upon ice‐nucleating particles, as well as the Hallett‐Mossop process, has a negligible contribution to the ice crystal concentration.

show abstract

Cloud‐resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts

et al. 2017

Self Cite

View full text Add to dashboard Cite

An intercomparison study of a midlatitude mesoscale squall line is performed using the Weather Research and Forecasting (WRF) model at 1 km horizontal grid spacing with eight different cloud microphysics schemes to investigate processes that contribute to the large variability in simulated cloud and precipitation properties. All simulations tend to produce a wider area of high radar reflectivity (Ze > 45 dBZ) than observed but a much narrower stratiform area. The magnitude of the virtual potential temperature drop associated with the gust front passage is similar in simulations and observations, while the pressure rise and peak wind speed are smaller than observed, possibly suggesting that simulated cold pools are shallower than observed. Most of the microphysics schemes overestimate vertical velocity and Ze in convective updrafts as compared with observational retrievals. Simulated precipitation rates and updraft velocities have significant variability across the eight schemes, even in this strongly dynamically driven system. Differences in simulated updraft velocity correlate well with differences in simulated buoyancy and low‐level vertical perturbation pressure gradient, which appears related to cold pool intensity that is controlled by the evaporation rate. Simulations with stronger updrafts have a more optimal convective state, with stronger cold pools, ambient low‐level vertical wind shear, and rear‐inflow jets. Updraft velocity variability between schemes is mainly controlled by differences in simulated ice‐related processes, which impact the overall latent heating rate, whereas surface rainfall variability increases in no‐ice simulations mainly because of scheme differences in collision‐coalescence parameterizations.

show abstract

Derivation of aerosol profiles for MC3E convection studies and use in simulations of the 20 May squall line case

Cited by 41 publications

References 88 publications

Simulating a Mesoscale Convective System Using WRF With a New Spectral Bin Microphysics: 1: Hail vs Graupel

Simulating a Mesoscale Convective System Using WRF With a New Spectral Bin Microphysics: 1: Hail vs Graupel

The Role of Ice Splintering on Microphysics of Deep Convective Clouds Forming Under Different Aerosol Conditions: Simulations Using the Model With Spectral Bin Microphysics

Cloud‐resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts

Contact Info

Product

Resources

About