Structure and Evolution of a Severe Squall Line over Oklahoma

Heymsfield, Gerald M.; Schotz, Steven

doi:10.1175/1520-0493(1985)113<1563:saeoas>2.0.co;2

Cited by 51 publications

(32 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Earlier research by Petersen et al (1984) and Mostek et al (1986) As in Figure 4.3 but from 0315 UTC 17 July 34 thunderstorms reached altitudes of 14-16 km. These estimated cloud tops are almost identical to those calculated by Houze (1977) for a GATE tropical squall line, Leary and Rappaport (1987) for a Texas MCC that developed from a squall line, Smull and Houze (1985) for an Oklahoma squall line, and Heymsfield and Schotz (1985) for a different Oklahoma squall line.…”

Section: Statement By Authorsupporting

confidence: 67%

Tropical Squall Lines of the Arizona Monsoon

Smith

Gall

1989

Mon. Wea. Rev.

View full text Add to dashboard Cite

Section: Statement By Authorsupporting

confidence: 67%

Tropical Squall Lines of the Arizona Monsoon

Smith

Gall

1989

Mon. Wea. Rev.

View full text Add to dashboard Cite

“…Braham (1952) defined precipitation efficiency as a ratio of surface rain rate to the inflow of water vapor into the storm through cloud base. The efficiency has been studied by many researchers (e.g., Auer and Marwitz 1968, Heymsfield and Schotz 1985, Chong and Hauser 1989. Their observational analysis showed that the efficiency ranges from 10%-120%.…”

Section: Introductionmentioning

confidence: 99%

Precipitation Efficiency in the Tropical Deep Convective Regime: A 2-D Cloud Resolving Modeling Study.

Sui

Lau

2002

Journal of the Meteorological Society of Japan

View full text Add to dashboard Cite

Precipitation efficiency in the tropical deep convective regime is analyzed based on the hourly data from a 2-D cloud resolving simulation. The cloud resolving model is forced by the large-scale vertical velocity, zonal wind and large-scale horizontal advections derived from TOGA COARE for a 20-day period. Large-scale precipitation efficiency (LSPE) is defined as a ratio of surface rain rate to the sum of surface evaporation and moisture convergence, and cloud-microphysics precipitation efficiency (CMPE) is defined as a ratio of surface rain rate to the sum of condensation and deposition rates of supersaturated vapor. Moisture budget shows that the local atmospheric moisture gains (loses) when the LSPE is less (more) than 100%. The LSPE could be larger than 100% for strong convection. This suggests that in order to avoid moisture bias, the cumulus parameterization schemes used in general circulation models should allow the precipitation rate to be larger than the sum of surface evaporation and moisture convergence in strong convective region. Statistical analysis shows that the LSPE is about 80% of the CMPE when it is less than 60%, and the CMPE is a constant of about 70% when the LSPE is more than 60%. The CMPE increases with increasing mass-weighted mean temperature and increasing surface rain rate. This suggests that precipitation is more efficient for warm environment and strong convection.

show abstract

“…Precipitation efficiency is defined as the ratio of surface rainfall rate to the sum of vapor convergence and surface evaporation rates which is referred to as large-scale precipitation efficiency (LSPE) (e.g., Braham et al, 1952;Heymsfield and Schotz, 1985;Doswell et al, 1996) or the ratio of surface rainfall rate to the sum of vapor condensation and deposition rates which is referred to as cloud-microphysics precipitation efficiency (CMPE) (e.g., Lipps and Hemler, 1986). While CMPE is only affected by cloud microphysical processes and LSPE is related with vapor and cloud processes, both LSPE and CMPE are statistically equivalent since the total moisture source (surface evaporation and vertically integrated moisture convergence) is converted into hydrometeors through vapor condensation and deposition rates .…”

Section: Precipitation Efficiencymentioning

confidence: 99%

A review of cloud-resolving model studies of convective processes

2008

Adv. Atmos. Sci.

View full text Add to dashboard Cite

Convective processes affect large-scale environments through cloud-radiation interaction, cloud microphysical processes, and surface rainfall processes. Over the last three decades, cloud-resolving models (CRMs) have demonstrated to be capable of simulating convective-radiative responses to an imposed largescale forcing. The CRM-produced cloud and radiative properties have been utilized to study the convectiverelated processes and their ensemble effects on large-scale circulations. This review summarizes the recent progress on the understanding of convective processes with the use of CRM simulations, including precipitation processes; cloud microphysical and radiative processes; dynamical processes; precipitation efficiency; diurnal variations of tropical oceanic convection; local-scale atmosphere-ocean coupling processes; and tropical convective-radiative equilibrium states. Two different ongoing applications of CRMs to general circulation models (GCMs) are discussed: replacing convection and cloud schemes for studying the interaction between cloud systems and large-scale circulation, and improving the schemes for climate simulations.

show abstract

Structure and Evolution of a Severe Squall Line over Oklahoma

Cited by 51 publications

References 0 publications

Tropical Squall Lines of the Arizona Monsoon

Tropical Squall Lines of the Arizona Monsoon

Precipitation Efficiency in the Tropical Deep Convective Regime: A 2-D Cloud Resolving Modeling Study.

A review of cloud-resolving model studies of convective processes

Contact Info

Product

Resources

About