Mixing‐length controls on high‐resolution simulations of convective storms

Hanley, Kirsty; Plant, Robert S.; Stein, Thorwald H. M.; Hogan, Robin J.; Nicol, John; Lean, Humphrey; Halliwell, Carol; Clark, Peter A.

doi:10.1002/qj.2356

Cited by 122 publications

(182 citation statements)

References 38 publications

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“…These values are well outside the range of maximum vertical velocities presented for oceanic convection by Heymsfield et al (2010) and agree with other studies showing excessive tropical vertical velocities simulated by convection-permitting models. Hanley et al (2014) demonstrated that the UM with a grid length of 1.5 km simulated convective cells that were too intense and were initiated too early, as was also shown by Varble et al (2014a), suggesting that convection is underresolved at grid lengths of order of 1 km. Improved initiation time was shown by Hanley et al (2014) to occur when the grid length was reduced to 500 and 200 m. However, the intensity of the convective cells was not necessarily improved, with the results being case-dependent.…”

Section: Maximum Reflectivity Profiles and Vertical Velocitiessupporting

confidence: 53%

Controls on phase composition and ice water content in a convection-permitting model simulation of a tropical mesoscale convective system

et al. 2016

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Abstract. Simulations of tropical convection from an operational numerical weather prediction model are evaluated with the focus on the model's ability to simulate the observed high ice water contents associated with the outflow of deep convection and to investigate the modelled processes that control the phase composition of tropical convective clouds. The 1 km horizontal grid length model that uses a singlemoment microphysics scheme simulates the intensification and decay of convective strength across the mesoscale convective system. However, deep convection is produced too early, the OLR (outgoing longwave radiation) is underestimated and the areas with reflectivities > 30 dBZ are overestimated due to too much rain above the freezing level, stronger updraughts and larger particle sizes in the model. The inclusion of a heterogeneous rain-freezing parameterisation and the use of different ice size distributions show better agreement with the observed reflectivity distributions; however, this simulation still produces a broader profile with many high-reflectivity outliers demonstrating the greater occurrence of convective cells in the simulations. Examining the phase composition shows that the amount of liquid and ice in the modelled convective updraughts is controlled by the following: the size of the ice particles, with larger particles growing more efficiently through riming and producing larger IWC (ice water content); the efficiency of the warm rain process, with greater cloud water contents being available to support larger ice growth rates; and exclusion or limitation of graupel growth, with more mass contained in slower falling snow particles resulting in an increase of in-cloud residence times and more efficient removal of LWC (liquid water content). In this simulated case using a 1 km grid length model, horizontal mass divergence in the mixed-phase regions of convective updraughts is most sensitive to the turbulence formulation. Greater mixing of environmental air into cloudy updraughts in the region of −30 to 0 • C produces more mass divergence indicative of greater entrainment, which generates a larger stratiform rain area. Above these levels in the purely ice region of the simulated updraughts, the convective updraught buoyancy is controlled by the ice particle sizes, demonstrating the importance of the microphysical processes on the convective dynamics in this simulated case study using a single-moment microphysics scheme. The single-moment microphysics scheme in the model is unable to simulate the observed reduction of mean mass-weighted ice diameter as the ice water content increases. The inability of the model to represent the observed variability of the ice size distribution would be improved with the use of a double-moment microphysics scheme.

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Section: Maximum Reflectivity Profiles and Vertical Velocitiessupporting

confidence: 53%

Controls on phase composition and ice water content in a convection-permitting model simulation of a tropical mesoscale convective system

et al. 2016

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“…A tendency for slower triggering by lower-resolution models was already noted by Khairoutdinov et al (2009), with higher resolutions (200 m) producing a longer forced-fair weather convection regime but a more abrupt deep convective initiation. Hanley et al (2014) also noted an improvement in the simulation of storms passing from 500-to 200-m resolution.…”

Section: A Ppm Integrationmentioning

confidence: 87%

Triggering Deep Convection with a Probabilistic Plume Model

D’Andrea

Gentine

Betts

et al. 2014

Journal of the Atmospheric Sciences

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A model unifying the representation of the planetary boundary layer and dry, shallow, and deep convection, the probabilistic plume model (PPM), is presented. Its capacity to reproduce the triggering of deep convection over land is analyzed in detail. The model accurately reproduces the timing of shallow convection and of deep convection onset over land, which is a major issue in many current general climate models.PPM is based on a distribution of plumes with varying thermodynamic states (potential temperature and specific humidity) induced by surface-layer turbulence. Precipitation is computed by a simple ice microphysics, and with the onset of precipitation, downdrafts are initiated and lateral entrainment of environmental air into updrafts is reduced.The most buoyant updrafts are responsible for the triggering of moist convection, causing the rapid growth of clouds and precipitation. Organization of turbulence in the subcloud layer is induced by unsaturated downdrafts, and the effect of density currents is modeled through a reduction of the lateral entrainment. The reduction of entrainment induces further development from the precipitating congestus phase to full deep cumulonimbus.Model validation is performed by comparing cloud base, cloud-top heights, timing of precipitation, and environmental profiles against cloud-resolving models and large-eddy simulations for two test cases. These comparisons demonstrate that PPM triggers deep convection at the proper time in the diurnal cycle and produces reasonable precipitation. On the other hand, PPM underestimates cloud-top height.

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“…While this paper has touched on the meteorology of extremes, the triggers of model extremes have not been examined. A full understanding of the unusual modelsimulated meteorological events will require nonsupervised detection and categorizing of extremes and their convective environment, and existing tools from mesoscale meteorology and cloud dynamics may be employed for such work (Tsakraklides and Evans 2003;Baldwin et al 2005;Davis et al 2006;Roberts and Lean 2008;Hanley et al 2014).…”

Section: Discussionmentioning

confidence: 99%

The Value of High-Resolution Met Office Regional Climate Models in the Simulation of Multihourly Precipitation Extremes

et al. 2014

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Extreme value theory is used as a diagnostic for two high-resolution (12-km parameterized convection and 1.5-km explicit convection) Met Office regional climate model (RCM) simulations. On subdaily time scales, the 12-km simulation has weaker June-August (JJA) short-return-period return levels than the 1.5-km RCM, yet the 12-km RCM has overly large high return levels. Comparisons with observations indicate that the 1.5-km RCM is more successful than the 12-km RCM in representing (multi)hourly JJA very extreme events. As accumulation periods increase toward daily time scales, the erroneous 12-km precipitation extremes become more comparable with the observations and the 1.5-km RCM. The 12-km RCM fails to capture the observed low sensitivity of the growth rate to accumulation period changes, which is successfully captured by the 1.5-km RCM. Both simulations have comparable December-February (DJF) extremes, but the DJF extremes are generally weaker than in JJA at daily or shorter time scales. Case studies indicate that ''gridpoint storms'' are one of the causes of unrealistic very extreme events in the 12-km RCM. Caution is needed in interpreting the realism of 12-km RCM JJA extremes, including short-return-period events, which have return values closer to observations. There is clear evidence that the 1.5-km RCM has a higher degree of realism than the 12-km RCM in the simulation of JJA extremes.

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Mixing‐length controls on high‐resolution simulations of convective storms

Cited by 122 publications

References 38 publications

Controls on phase composition and ice water content in a convection-permitting model simulation of a tropical mesoscale convective system

Controls on phase composition and ice water content in a convection-permitting model simulation of a tropical mesoscale convective system

Triggering Deep Convection with a Probabilistic Plume Model

The Value of High-Resolution Met Office Regional Climate Models in the Simulation of Multihourly Precipitation Extremes

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