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

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

doi:10.1175/bams-d-13-00279.1

Cited by 84 publications

(105 citation statements)

References 37 publications

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“…The lack of development of larger cells is consistent with the previously described tendency of high-resolution models to produce too many too-small cells (e.g. Stein et al, 2015;Hanley et al, 2015). In contrast to the weakly forced convection in the morning, the organisation into larger cells later in the simulation is supported by the establishment of sea-breeze convergence lines (Fig.…”

Section: Radar Reflectivity and Surface Precipitationsupporting

confidence: 89%

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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: Radar Reflectivity and Surface Precipitationsupporting

confidence: 89%

“…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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“…On the other hand, in the convective-scale regime, with so many modes actively involved in the dynamics, solutions of the governing equations are computable with much smaller accuracy at any practical resolution, and the solutions do not converge with increasing resolution. For example, the Met Office Unified Model finds no tendency toward forecast convergence when increasing horizontal grid spacing from 1.5 km to 100 m (Stein et al 2015), since the increase of horizontal resolution gradually resolves more turbulent processes. As a conventional wisdom, grid spacings at least as fine as O(10 1 -10 2 ) m are required for large-eddy simulations (LESs) to be meaningful.…”

Section: Data Assimilationmentioning

confidence: 99%

Scientific Challenges of Convective-Scale Numerical Weather Prediction

Yano

Ziemiański

Cullen

et al. 2018

Bulletin of the American Meteorological Society

118

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After extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days. Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows. The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.

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“…The 1.5-order turbulent kinetic energy (TKE) closure scheme is used (e.g., Skamarock et al 2008). Stein et al (2015) showed sensitivity to the assumed subgrid-scale turbulence scheme. However, for consistency among the simulations discussed herein, we examine the results using a single scheme.…”

Section: A Experimental Setupmentioning

confidence: 99%

Effects of Horizontal and Vertical Grid Spacing on Mixing in Simulated Squall Lines and Implications for Convective Strength and Structure

Lebo

Morrison

2015

Monthly Weather Review

105

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The sensitivity of an idealized squall line to horizontal D h and vertical D y grid spacing is investigated using a new approach. Simulations are first performed at a horizontal grid spacing of 1 km until the storm reaches its mature stage. The model output is then interpolated to smaller (and larger) grid spacings, and the model is restarted using the interpolated state plus small thermodynamic perturbations to spin up small-scale motions. This framework allows an investigation of the sensitivity of the storm to changes in D h without complications from differences in storm initiation and early evolution. The restarted simulations reach a quasi steady state within approximately 1 h. Results demonstrate that there are two D h -dependent regimes with the transition between regimes occurring for D h between 250 and 500 m. Some storm characteristics, such as the mean convective core area, change substantially for D h . 250 m but show limited sensitivity as D h is decreased below 250 m, despite better resolving smaller-scale turbulent motions. This transition is found to be independent of the chosen D y . Mixing in the context of varying D h and D y is also investigated via passive tracers that are initialized 1 h after restarting the simulations (i.e., after the spin up of small-scale motions). The tracer field at the end of the simulations reveals that entrainment and detrainment are suppressed in the simulations with D h $ 500 m. For decreasing D h , entrainment and detrainment are substantially more important, limiting the flux of low-level tracer to the upper troposphere, which has important implications for modeling studies of convective transport from the boundary layer through the troposphere.

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

Abstract: The 3D structures of over 1,000 convective storms observed by the Chilbolton radar are used to constrain storm dynamics and microphysics in models with resolutions between 100 and 1,500 m.

Cited by 84 publications

References 37 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

Scientific Challenges of Convective-Scale Numerical Weather Prediction

Effects of Horizontal and Vertical Grid Spacing on Mixing in Simulated Squall Lines and Implications for Convective Strength and Structure

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