The Three-Dimensional Morphology of Simulated and Observed Convective Storms over Southern England

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

doi:10.1175/mwr-d-13-00372.1

Cited by 43 publications

(59 citation statements)

References 47 publications

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“…Also, modelled cell size distributions often do not converge in simulations with increasing spatial resolution (e.g. Stein et al, 2014Stein et al, , 2015Hanley et al, 2015). These problems have been at least partly attributed to the representation of lateral mixing and parameter settings therein Hanley et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Aerosol–cloud interactions in mixed-phase convective clouds – Part 1: Aerosol perturbations

et al. 2018

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Abstract. Changes induced by perturbed aerosol conditions in moderately deep mixed-phase convective clouds (cloud top height ∼ 5 km) developing along sea-breeze convergence lines are investigated with high-resolution numerical model simulations. The simulations utilise the newly developed Cloud-AeroSol Interacting Microphysics (CASIM) module for the Unified Model (UM), which allows for the representation of the two-way interaction between cloud and aerosol fields. Simulations are evaluated against observations collected during the COnvective Precipitation Experiment (COPE) field campaign over the southwestern peninsula of the UK in 2013. The simulations compare favourably with observed thermodynamic profiles, cloud base cloud droplet number concentrations (CDNC), cloud depth, and radar reflectivity statistics. Including the modification of aerosol fields by cloud microphysical processes improves the correspondence with observed CDNC values and spatial variability, but reduces the agreement with observations for average cloud size and cloud top height.Accumulated precipitation is suppressed for higheraerosol conditions before clouds become organised along the sea-breeze convergence lines. Changes in precipitation are smaller in simulations with aerosol processing. The precipitation suppression is due to less efficient precipitation production by warm-phase microphysics, consistent with parcel model predictions.In contrast, after convective cells organise along the sea-breeze convergence zone, accumulated precipitation increases with aerosol concentrations. Condensate production increases with the aerosol concentrations due to higher vertical velocities in the convective cores and higher cloud top heights. However, for the highest-aerosol scenarios, no further increase in the condensate production occurs, as clouds grow into an upper-level stable layer. In these cases, the reduced precipitation efficiency (PE) dominates the precipitation response and no further precipitation enhancement occurs. Previous studies of deep convective clouds have related larger vertical velocities under high-aerosol conditions to enhanced latent heating from freezing. In the presented simulations changes in latent heating above the 0 • C are negligible, but latent heating from condensation increases with aerosol concentrations. It is hypothesised that this increase is related to changes in the cloud field structure reducing the mixing of environmental air into the convective core.The precipitation response of the deeper mixed-phase clouds along well-established convergence lines can be the opposite of predictions from parcel models. This occurs when clouds interact with a pre-existing thermodynamic environment and cloud field structural changes occur that are not captured by simple parcel model approaches.

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Section: Discussionmentioning

confidence: 99%

Aerosol–cloud interactions in mixed-phase convective clouds – Part 1: Aerosol perturbations

et al. 2018

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“…Following Stein et al (2014), the storm volumes were regridded to a regular Cartesian grid of 333 m × 333 m × 500 m, comparable to the horizontal resolution of the radar data. Doppler winds from the range height indicator (RHI) scans were interpolated onto a Cartesian grid with 500-m range by 250-m height resolution and were assumed to be equivalent to the horizontal wind parallel to the plane for scan elevations below 10°.…”

Section: Radar Observationsmentioning

confidence: 99%

“…Our simulations were run with a diagnostic split between ice crystals and ice aggregates based on cloud-top temperature. Stein et al (2014) showed that changes to the UM ice microphysics affect the distribution of water contents in the convective storms but do not greatly affect the overall storm morphology.…”

Section: A Radar Scanning Strategy For Convectionmentioning

confidence: 99%

“…To evaluate the simulations against the observed radar reflectivities, a "forward model" was used to calculate reflectivities from the UM hydrometeor fields using the UM microphysics assumptions on the particle size distribution [see appendix A of Stein et al (2014)]. Examples of the 3D structures thus obtained, as well as cross sections of reflectivity, are shown in Fig.…”

Section: A Radar Scanning Strategy For Convectionmentioning

confidence: 99%

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The DYMECS Project: A Statistical Approach for the Evaluation of Convective Storms in High-Resolution NWP Models

Stein

Hogan

Clark

et al. 2015

Bulletin of the American Meteorological Society

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“…The algorithm used is based on that of Stein et al, (2014) and has been modified for use on both simulations and observations (Crook et al, in preparation). The algorithm can be applied to 25 either rainfall or brightness temperatures to track convective systems over West Africa.…”

Section: Storm Trackingmentioning

confidence: 99%

Can explicit convection improve modelled dust in summertime West Africa?

Roberts¹,

Woodage²,

Marsham³

et al. 2018

Preprint

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Abstract. Global and regional models have large systematic errors in their modelled dust fields over West Africa. It is well established that cold pool outflows from moist convection (haboobs) can raise over 50 % of the dust over the Sahara and Sahel in summer, but parameterised moist convection tends to give a very poor representation of this in models. Here, we test the hypothesis that an explicit representation of convection improves haboob winds and so may reduce errors in modelled dust fields. The results show that despite varying both grid-spacing and the representation of convection there are only minor 15 changes in dust aerosol optical depth (AOD) and dust mass loading fields between simulations. In all simulations there is an AOD deficit over the observed central Saharan dust maximum and a high bias in AOD along the west coast: both features consistent with many climate (CMIP5) models. Cold pool outflows are present in the explicit simulations and do raise dust. Consistent with this there is an improved diurnal cycle in dust-generating winds with a seasonal peak in evening winds at locations with moist convection that is absent in simulations with parameterised convection. However, the explicit convection 20 does not change the AOD field significantly for several reasons. Firstly, the increased windiness in the evening from haboobs is approximately balanced by a reduction in morning winds associated with the breakdown of the nocturnal low-level jet (LLJ).Secondly, although explicit convection increases the frequency of the strongest winds, these are still weaker than observed, especially close to the observed summertime Saharan dust maximum: this results from the fact that although large mesoscale convective systems (and resultant cold pools) are generated, they have a lower frequency than observed and haboob winds are 25 too weak. Finally, major impacts of the haboobs on winds occur over the Sahel, where, although dust uplift is known to occur in reality, uplift in the simulations is limited by a seasonally constant bare soil fraction in the model, together with soil moisture and clay fractions which are too restrictive of dust emission in seasonally-varying vegetated regions. For future studies, the results demonstrate 1) the improvements in behaviour produced by the explicit representation of convection, 2) the value of simultaneously evaluating both dust and winds and 3) the need to develop parameterisations of the land surface alongside those 30 of dust-generating winds.Atmos. Chem. Phys. Discuss., https://doi

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The Three-Dimensional Morphology of Simulated and Observed Convective Storms over Southern England

Cited by 43 publications

References 47 publications

Aerosol–cloud interactions in mixed-phase convective clouds – Part 1: Aerosol perturbations

Aerosol–cloud interactions in mixed-phase convective clouds – Part 1: Aerosol perturbations

The DYMECS Project: A Statistical Approach for the Evaluation of Convective Storms in High-Resolution NWP Models

Can explicit convection improve modelled dust in summertime West Africa?

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