Numerical Simulation of Deep Tropical Convection Associated with Large-Scale Convergence

Lipps, Frank B.; Hemler, Richard S.

doi:10.1175/1520-0469(1986)043<1796:nsodtc>2.0.co;2

Cited by 69 publications

(43 citation statements)

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“…This is referred to as cloud-microphysics precipitation efficiency (CMPE). The CMPE is similar to the precipitation efficiency defined by Weisman and Klemp (1982) and Lipps and Hemler (1986). The central questions in this study are: what is the relationship between the LSPE and CMPE?…”

Section: Introductionmentioning

confidence: 82%

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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

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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.

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Section: Introductionmentioning

confidence: 82%

“…Their observational analysis showed that the efficiency ranges from 10%-120%. Weisman and Klemp (1982) and Lipps and Hemler (1986) in their modeling studies defined precipitation efficiency as a ratio of total rainfall to total condensation. Ferrier et al (1996) showed that simulated efficiency ranges from 20%-50%.…”

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

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“…Fridlind and A. Ackerman) The DHARMA (Distributed Hydrodynamic AerosolRadiation-Microphysics Application) model treats atmospheric and cloud dynamics with a large-eddy simulation code (Stevens and Bretherton, 1996) that solves an anelastic approximation of the Navier-Stokes equations appropriate for deep convection (Lipps and Hemler, 1986).…”

Section: Rams (M Leriche and S Cautenet)mentioning

confidence: 99%

Cloud-scale model intercomparison of chemical constituent transport in deep convection

et al. 2007

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Abstract. Transport and scavenging of chemical constituents in deep convection is important to understanding the composition of the troposphere and therefore chemistryclimate and air quality issues. High resolution cloud chemistry models have been shown to represent convective processing of trace gases quite well. To improve the representation of sub-grid convective transport and wet deposition in large-scale models, general characteristics, such as species mass flux, from the high resolution cloud chemistry models can be used. However, it is important to understand how these models behave when simulating the same storm. The intercomparison described here examines transport of six species. CO and O 3 , which are primarily transported, show good agreement among models and compare well with observations. Models that included lightning production of NO x reasonably predict NO x mixing ratios in the anvil compared with observations, but the NO x variability is much larger than that seen for CO and O 3 . Predicted anvil mixing ratios of the soluble species, HNO 3 , H 2 O 2 , and CH 2 O, exhibit significant differences among models, attributed to different schemes in these models of cloud processing including the role of the Correspondence to: M. C. Barth (barthm@ucar.edu) ice phase, the impact of cloud-modified photolysis rates on the chemistry, and the representation of the species chemical reactivity. The lack of measurements of these species in the convective outflow region does not allow us to evaluate the model results with observations.

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“…Also, the dynamics of midlatitude supercells, that are usually associated with tornados, was another major focus in the 70's (i.e., Klemp and Wilhelmson 1978b;Wilhelmson and Klemp 1978). After GATE 1 , cloud ensemble modeling 2 was developed to study the collective feedback of clouds on the large-scale tropical environment with the aim of improving cumulus parameterization in large-scale models (i.e., Soong and Tao 1980;Tao and Soong 1986;Lipps and Helmer 1986;Tao et al 1987; and many others), a quest that continues to this day. The effect of ice processes on cloud formation and development, stratiform rain processes and their relation to convective cells, and the effect of wind shear on squall line development were the other major areas of interest involving CRMs in the 1980's (i.e., Nakajima and Matsuno 1988;Rotunno et al 1988;Fovell and Ogura 1988;Tao 1995;and many others).…”

Section: A a Brief History Of Cloud Resolving Modelsmentioning

confidence: 99%

Cloud Resolving Modeling

Tao

2007

Journal of the Meteorological Society of Japan

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One of the most promising methods to test the representation of cloud processes used in climate models is to use observations together with cloud resolving models (CRMs). CRMs use more sophisticated and realistic representations of cloud microphysical processes, and they can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems (with sizes ranging from about 2-200 km). CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results.This paper provides a brief discussion and review of the main characteristics of CRMs as well as some of their major applications. These include the use of CRMs to improve our understanding of: (1) convective organization, (2) cloud temperature and water vapor budgets, and convective momentum transport, (3) diurnal variation of precipitation processes, (4) radiative-convective quasi-equilibrium states, (5) cloud-chemistry interaction, (6) aerosol-precipitation interaction, and (7) improving moist processes in large-scale models. In addition, current and future developments and applications of CRMs will be presented.

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Numerical Simulation of Deep Tropical Convection Associated with Large-Scale Convergence

Cited by 69 publications

References 0 publications

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

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

Cloud-scale model intercomparison of chemical constituent transport in deep convection

Cloud Resolving Modeling

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