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Identifying modes of convection can be useful in both forecasting and research. For example, it allows for potentially different impacts to be determined in forecasting contexts and stratification of model behavior in research contexts. One area where identification could be particularly beneficial is elevated convection. Elevated convection is not routinely examined (outside of an operational environment) within a physical-process perspective in operational numerical weather prediction model evaluation or verification. Using convection-allowing model (CAM) output the characteristics of four elevated convection diagnostics (based on boundary layer, Convective Available Potential Energy (CAPE) ratios, downdraft, and inflow layer properties) are examined in operational forecasts during the UK Testbed Summer 2021 run at the Met Office. A survey of the practical use of these diagnostics in a simulated operational environment revealed that diagnostics based on CAPE ratios and inflow layer properties were preferred. These diagnostics were the smoothest varying in both space and time. Treating the CAPE ratio and downdraft properties diagnostics as proxies for updrafts and downdrafts, respectively, showed that updrafts were slightly more likely to be resolved than downdrafts. However, a substantial proportion of both are unresolved in current CAMs. Filtering the CAPE ratios by the inflow layer properties led to improved spatial and temporal characteristics, and thus indicates a potentially useful diagnostic for both research and forecasting.

In numerical weather prediction (NWP), observations and models are quantitatively compared for the purposes of data assimilation and forecast verification. The spatial and temporal scales represented by the observation and model may differ and this results in a scale mis‐match error which may be biased and correlated. The aim of this paper is to investigate the structure of representation error in convection‐permitting NWP models for four meteorological variables: temperature, specific humidity, zonal and meridional wind. We use high resolution data from the experimental Met Office London Model (approximately 300 m grid‐length) to simulate perfect observations and lower resolution model data. The scale mis‐match error and its bias, variance and correlation are calculated from the perfect observation and low‐resolution model equivalents. Our new results show that the scale mis‐match bias is significant in the boundary layer for temperature and specific humidity, whereas the variance is significant in the boundary layer for all analysed variables. Furthermore, they are shown to be related to the mismatch in the high‐ and low‐resolution orography. Contrary to previous studies using low‐resolution, (km‐scale) data, horizontal correlations are shown to be insignificant. However, all variables exhibit considerable vertical representation error correlation throughout the boundary layer; for temperature a significant positive vertical correlation persists for all model levels in the troposphere. Our results suggest that significant biases and vertical correlations exist that should be accounted for to give maximum observation impact in data assimilation and for fairness in model verification and validation.

Convection-permitting forecasts have improved the forecasts of flooding from intense rainfall. However, probabilistic forecasts, generally based upon ensemble methods, are essential to quantify forecast uncertainty. This leads to a need to understand how different aspects of the model system affect forecast behavior. We compare the uncertainty due to initial and boundary condition (IBC) perturbations and boundary layer turbulence using a superensemble (SE) created to determine the influence of 12 IBC perturbations versus 12 stochastic boundary layer (SBL) perturbations constructed using a physically based SBL scheme. We consider two mesoscale extreme precipitation events. For each, we run a 144-member SE. The SEs are analyzed to consider the growth of differences between the simulations, and the spatial structure and scales of those differences. The SBL perturbations rapidly spin up, typically within 12 h of precipitation commencing. The SBL perturbations eventually produce spread that is not statistically different from the spread produced by the IBC perturbations, though in one case there is initially increased spread from the IBC perturbations. Spatially, the growth from IBC occurs on larger scales than that produced by the SBL perturbations (typically by an order of magnitude). However, analysis across multiple scales shows that the SBL scheme produces a random relocation of precipitation up to the scale at which the ensemble members agree with each other. This implies that statistical postprocessing can be used instead of running larger ensembles. Use of these statistical postprocessing techniques could lead to more reliable probabilistic forecasts of convective events and their associated hazards.

Various studies in the UK, Great Plains and Southeastern USA have highlighted the presence of certain radar signatures prior to the onset of or during severe convection. One type of such radar signature is a differential reflectivity (ZDR) column, which is defined as a vertical columnar region of enhanced ZDR that extends above the freezing level. Several field campaigns synthesising radar and in-situ measurements confirmed that such columns contain large supercooled millimetre-sized droplets lofted into convective storms and are in, or near strong updrafts. Recent work using a single research radar in Oklahoma also exploited the usefulness of detecting ZDR columns for informing nowcasters of severe convection.The goal of this study is to identify potential severe convective events in the UK mostly for cases over the summer season using polarimetric radar measurements. The UK Met Office has fully upgraded all 18 C-band radars since January 2018 with full dual-polarisation operational capability. From this network, we derive a 3D radar composite, which provides large coverage on the order of 1000 km for monitoring potentially hazardous weather. Environmental conditions are also investigated prior to and during the onset of convection to understand the effectiveness in ZDR columns as precursors of severe convection depending on synoptic regime.Using past cases, we track storm cells using maximum reflectivity in the column and identify whether the cells contain ZDR columns, where a ZDR column is identified based on a 3D volume thresholded by reflectivity (ZH) and ZDR. For nowcasting of severe storms, with ZH > 50 dBZ, we find optimal ZH and ZDR thresholds of around 30 dBZ and 2.0 dB respectively existing within ZDR columns. This agrees with past literature and physical understanding indicating a low concentration of large super-cooled water droplets within ZDR columns explained by condensation-coalescence processes, especially during early stages of convective development. In contrast, other works may show ZDR columns associated with areas of high ZH, suggesting detection of such columns in more mature stages of a storm. Algorithm performance in identifying ZDR columns for early detection of severe convection and its optimal parameters vary with synoptic regime.

With a number of operational centres looking forward to the possibilities of “city scale” NWP and climate modelling it is important to understand the behaviour of order 100m models over cities. A key issue is how to handle the representation of partially resolved turbulence in these models. In this paper we compare the representation of a clear convective boundary layer case in London in 100m and 50m grid-length versions of the Unified Model (MetUM) with observations. Comparison of Doppler lidar observations of the vertical velocity shows that convective overturning in the boundary layer is broadly well represented in terms of its depth and magnitude. The role of model resolution was investigated by comparing a 50m grid-length model with the 100m one. It is found that, although going to 50m grid-length does not greatly change many of the bulk properties (mixing height, heat flux profiles, etc.) the spatial structure of the overturning is significantly different. This is confirmed with spectral analysis which shows that the 50m model resolves significantly more of the energetic eddies, and a length scale analysis that shows the 50m and 100m models produce convective structures 2-3 times larger than observed. We conclude that, for the MetUM, model grid-lengths of order 100m may well be sufficient for predicting many bulk and statistical properties of convective boundary layers however the details of the spatial structures around convective overturning in these situations are likely to be still under-resolved. Spin up artefacts emanating from the inflow boundary of the model are investigated by comparing with a smaller 100m grid-length domain which is more dominated by such effects. These manifest themselves as along wind boundary layer rolls which produce a less realistic comparison with the lidar observations. A stability analysis is presented in order to better understand the formation of these rolls.

In numerical weather prediction (NWP), observations and models are quantitatively compared for the purposes of data assimilation and forecast verification. The spatial and temporal scales represented by the observation and model may differ and this results in a scale mis‐match error which may be biased and correlated. The aim of this paper is to investigate the structure of representation error in convection‐permitting NWP models for four meteorological variables: temperature, specific humidity, zonal and meridional wind. We use high resolution data from the experimental Met Office London Model (approximately 300 m grid‐length) to simulate perfect observations and lower resolution model data. The scale mis‐match error and its bias, variance and correlation are calculated from the perfect observation and low‐resolution model equivalents. Our new results show that the scale mis‐match bias is significant in the boundary layer for temperature and specific humidity, whereas the variance is significant in the boundary layer for all analysed variables. Furthermore, they are shown to be related to the mismatch in the high‐ and low‐resolution orography. Contrary to previous studies using low‐resolution, (km‐scale) data, horizontal correlations are shown to be insignificant. However, all variables exhibit considerable vertical representation error correlation throughout the boundary layer; for temperature a significant positive vertical correlation persists for all model levels in the troposphere. Our results suggest that significant biases and vertical correlations exist that should be accounted for to give maximum observation impact in data assimilation and for fairness in model verification and validation.

Background: global longitudinal strain (GLS) measures myocardial deformation and is a sensitive modality for detecting subclinical myocardial dysfunction and predicting cardiac outcomes. The accuracy of speckle-tracking echocardiography (STE) is dependent on temporal resolution. A novel software enables relatively high frame rate (Hi-FR) (~200 fps) echocardiographic images acquisition which empowers us to investigate the impact of Hi-FR imaging on GLS analysis. The goal of this pilot study was to demonstrate the feasibility of Hi-FR for STE. Methods: In this prospective study, we acquired echocardiographic images using clinical scanners on patients with normal left ventricular systolic function using Hi-FR and conventional frame rate (Reg-FR) (~50 FPS). GLS values were evaluated on apical 4-, 2- and 3-chamber images acquired in both Hi-FR and Reg-FR. Inter-observer and intra-observer variabilities were assessed in Hi-FR and Reg-FR. Results: There were 143 resting echocardiograms with normal LVEF included in this study. The frame rate of Hi-FR was 190 ± 25 and Reg-FR was 50 ± 3, and the heart rate was 71 ± 13. Strain values measured in Hi-FR were significantly higher than those measured in Reg-FR (all p < 0.001). Inter-observer and intra-observer correlations were strong in both Hi-FR and Reg-FR. Conclusions: We demonstrated that strain values were significantly higher using Hi-FR when compared with Reg-FR in patients with normal LVEF. It is plausible that higher temporal resolution enabled the measurement of myocardial strain at desired time point. The result of this study may inform clinical adoption of the novel technology. Further investigations are necessary to evaluate the value of Hi-FR to assess myocardial strain in stress echocardiography in the setting of tachycardia.

Operational forecasting centres increasingly rely on convection-permitting NWP simulations to assist in their forecasting of convective events. The evaluation of upgrades in the underlying NWP modeling system normally happens through routine verification using traditional metrics on two-dimensional fields, such as gridded rainfall data. Object- and process-based evaluation can identify specific physical mechanisms for model improvement, but such evaluation procedures normally require targeted and expensive field campaigns. Here, we explore the potential use of the UK operational radar network observations and its derived 3D composite product for evaluating the representation of convective storms in the Met Office Unified Model. A comparison of the 1 km x 1 km x 0.5 km 3D radar composites against observations made with the research-grade radar at Chilbolton in the southern UK indicates that the 3D radar composite data can reliably be used to evaluate the morphology of convective storms. The 3D radar composite data are subsequently used to evaluate the development of convective storms in the Met Office Unified Model. Such analysis was heretofore unavailable due to a lack of high-frequency three-dimensional radar data. The operational nature of the UK radar data makes these 3D composites a valuable resource for future studies of the initiation, growth, development, and organisation of convective storms over the UK.

Turbulent mixing processes are important in determining the evolution of convective clouds,and the production of convective precipitation. However, the exact nature of these impacts remains uncertain due to limited observations. Model simulations show that assumptions made in parametrizing turbulence can have a marked effect on the characteristics of simulated clouds. This leads to significant uncertainty in forecasts from convection‐permitting numerical weather prediction (NWP) models. This contribution presents a comprehensive method to retrieve turbulence using Doppler weather radar to investigate turbulence in observed clouds. This method involves isolating the turbulent component of the Doppler velocity spectrum width, expressing turbulence intensity as an eddy dissipation rate, ϵ. By applying this method throughout large datasets of observations collected over the southern United Kingdom using the (0.28° beam‐width) Chilbolton Advanced Meteorological Radar (CAMRa), statistics of convective cloud turbulence are presented. Two contrasting case days are examined: a shallow “shower” case, and a “deep convection” case, exhibiting stronger and deeper updraughts. In our observations, ϵ generally ranges from 10−3 to 10−1 m2/s3, with the largest values found within, around and above convective updraughts. Vertical profiles of ϵ suggest that turbulence is much stronger in deep convection; 95th percentile values increase with height from 0.03 to 0.1 m2/s3, compared to approximately constant values of 0.02–0.03 m2/s3 throughout the depth of shower cloud. In updraught regions on both days, the 95th percentile of ϵ has significant (p < 10−3) positive correlations with the updraught velocity, and the horizontal shear in the updraught velocity, with weaker positive correlations with updraught dimensions. The ϵ‐retrieval method presented considers a very broad range of conditions, providing a reliable framework for turbulence retrieval using high‐resolution Doppler weather radar. In applying this method across many observations, the derived turbulence statistics will form the basis for evaluating the parametrization of turbulence in NWP models.

With a number of operational centres looking forward to the possibilities of “city scale” NWP and climate modelling it is important to understand the behaviour of order 100m models over cities. A key issue is how to handle the representation of partially resolved turbulence in these models. In this paper we compare the representation of a clear convective boundary layer case in London in 100m and 50m grid-length versions of the Unified Model (MetUM) with observations. Comparison of Doppler lidar observations of the vertical velocity shows that convective overturning in the boundary layer is broadly well represented in terms of its depth and magnitude. The role of model resolution was investigated by comparing a 50m grid-length model with the 100m one. It is found that, although going to 50m grid-length does not greatly change many of the bulk properties (mixing height, heat flux profiles, etc.) the spatial structure of the overturning is significantly different. This is confirmed with spectral analysis which shows that the 50m model resolves significantly more of the energetic eddies, and a length scale analysis that shows the 50m and 100m models produce convective structures 2-3 times larger than observed. We conclude that, for the MetUM, model grid-lengths of order 100m may well be sufficient for predicting many bulk and statistical properties of convective boundary layers however the details of the spatial structures around convective overturning in these situations are likely to be still under-resolved. Spin up artefacts emanating from the inflow boundary of the model are investigated by comparing with a smaller 100m grid-length domain which is more dominated by such effects. These manifest themselves as along wind boundary layer rolls which produce a less realistic comparison with the lidar observations. A stability analysis is presented in order to better understand the formation of these rolls.