A Point Cloud Method for Retrieval of High-Resolution 3D Gridded Reflectivity from Weather Radar Networks for Air Traffic Management

Scovell, Robert; Al-Sakka, Hassan

doi:10.1175/jtech-d-15-0051.1

Cited by 11 publications

(19 citation statements)

References 42 publications

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“…The 18 dBZ contour is often used in radar data sets to determine the "echo top" (e.g. Lakshmanan et al, 2013;Scovell and al Sakka, 2016). The mean height of the 18 dBZ contour increases from about 2 km in the morning to 3 km in the afternoon in the observations and the model simulations (Fig.…”

Section: Radar Reflectivity and Surface Precipitationmentioning

confidence: 99%

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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 Precipitationmentioning

confidence: 99%

“…In addition to the campaign-specific data, we use a 3-D radar composite provided by the Met Office (Scovell and al Sakka, 2016). The composite data used here have a horizontal resolution of 1 km, a vertical resolution of 500 m over the study area, and a temporal resolution of 10 min.…”

Section: Observational Data From Copementioning

confidence: 99%

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

et al. 2018

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“…Recent studies have highlighted that it is necessary to simulate entire cloud fields over long periods in order to quantify a climate-relevant aerosol signal (e.g. Grabowski, 2006;van den Heever et al, 2011;Seifert et al, 2012) due to interactions between clouds and their thermodynamic environment. These interactions can at least partly compensate for the large changes simulated for individual clouds (e.g.…”

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confidence: 99%

Aerosol–cloud interactions in mixed-phase convective clouds – Part 2: Meteorological ensemble

et al. 2018

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Abstract. The relative contribution of variations in meteorological and aerosol initial and boundary conditions to the variability in modelled cloud properties is investigated with a high-resolution ensemble (30 members). In the investigated case, moderately deep convection develops along sea-breeze convergence zones over the southwestern peninsula of the UK. A detailed analysis of the mechanism of aerosol–cloud interactions in this case has been presented in the first part of this study (Miltenberger et al., 2018). The meteorological ensemble (10 members) varies by about a factor of 2 in boundary-layer moisture convergence, surface precipitation, and cloud fraction, while aerosol number concentrations are varied by a factor of 100 between the three considered aerosol scenarios. If ensemble members are paired according to the meteorological initial and boundary conditions, aerosol-induced changes are consistent across the ensemble. Aerosol-induced changes in CDNC (cloud droplet number concentration), cloud fraction, cell number and size, outgoing shortwave radiation (OSR), instantaneous and mean precipitation rates, and precipitation efficiency (PE) are statistically significant at the 5 % level, but changes in cloud top height or condensate gain are not. In contrast, if ensemble members are not paired according to meteorological conditions, aerosol-induced changes are statistically significant only for CDNC, cell number and size, outgoing shortwave radiation, and precipitation efficiency. The significance of aerosol-induced changes depends on the aerosol scenarios compared, i.e. an increase or decrease relative to the standard scenario. A simple statistical analysis of the results suggests that a large number of realisations (typically >100) of meteorological conditions within the uncertainty of a single day are required for retrieving robust aerosol signals in most cloud properties. Only for CDNC and shortwave radiation small samples are sufficient. While the results are strictly only valid for the investigated case, the presented evidence combined with previous studies highlights the necessity for careful consideration of intrinsic predictability, meteorological conditions, and co-variability between aerosol and meteorological conditions in observational or modelling studies on aerosol indirect effects.

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“…The domain is focused on the south of the United Kingdom (specifically the Heathrow domain from Scovell and Al-Sakka, 2016). This is the part of the United Kingdom that most frequently experiences thunderstorms (Cecil et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Intensification of single cell storms prior to lightning onset

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Stein

Harrison

et al. 2019

Atmospheric Science Letters

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Single cell storms in the United Kingdom can produce lightning, despite apparently only having developed to towering cumulus rather than cumulonimbus. Such marginal thunderstorms still present severe weather hazards but are difficult to identify and predict and therefore provide a warning. Observations from the Met Office radar mosaic and ATDNet (Arrival Time Difference Network) show that these single cell storms demonstrate a characteristic increase in the area of high reflectivity storm core during the 15 min prior to the first lightning. By using the Met Office Unified Model to investigate reflectivity development in modelled storms, a microphysical explanation for the observed reflectivity increase is identified. During a rapid reflectivity increase, the updraft area at the melting layer, the peak updraft velocity and the storm graupel mass increase. The three quantities examined are linked to each other and to the generation of charge within the storm. The production of graupel is promoted by the increase in updraft area and charge separation is enhanced by the faster peak updraft velocity. This explains some of the physical differences between single cell storms that produce lightning and apparently similar storm systems which do not. It also provides a new basis with which to predict lightning hazard for marginal storms.

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A Point Cloud Method for Retrieval of High-Resolution 3D Gridded Reflectivity from Weather Radar Networks for Air Traffic Management

Cited by 11 publications

References 42 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

Aerosol–cloud interactions in mixed-phase convective clouds – Part 2: Meteorological ensemble

Intensification of single cell storms prior to lightning onset

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