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The UK Met Office has a 300‐m grid length numerical weather prediction (NWP) model running routinely over London and, in research mode, city‐scale hectometric grid length NWP has become commonplace. It is important to understand how moving from kilometre‐ to hectometre‐scale grid length NWP influences boundary‐layer vertical mixing. For a clear‐sky convective boundary layer (CBL) case study, using 55‐ and 100‐m grid length NWP, we demonstrate that CBL vertical mixing of passive scalar is almost fully resolved. Passive scalar converges near the surface after emission from an idealised pollution ground source representing city‐scale emissions, and is transported in updrafts preferentially into the upper boundary layer. Approximately 8 km downstream of the source edge, this causes 34%$$ 34\% $$ lower near‐surface concentrations compared with 1.5‐km grid length NWP, where vertical mixing is fully parameterised. This demonstrates that resolving ballistic‐type dispersion, which is not typically represented in NWP vertical mixing parameterisations, can have a leading‐order influence on city‐scale near‐surface pollution concentration. We present a simple analytical model that is able to capture diffusive and ballistic dispersion behaviour in terms of effective timescales. The timescale controlling how long it takes passive scalar to become well mixed in the CBL is ≈3$$ \approx 3 $$ times longer for 1.5‐km compared with 100‐ and 55‐m grid length NWP. [ABSTRACT FROM AUTHOR]

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 U.K. 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. Significance Statement: Understanding diagnostics is important to be able to analyze model data. Four diagnostics to identify elevated convection are characterized from kilometer-scale operational forecasts. Diagnosing elevated convection from model data is important as these events are often associated with impactful forecast busts. Therefore, being able to identify how the model is representing these events could lead to model improvements. Two diagnostics were deemed to be of practical use based on current kilometer-scale forecasts: convective available potential energy ratios and inflow layer properties. These diagnostics varied smoothly in space and time. The two diagnostics were combined to produce a filtered diagnostic that could be useful in both research and operations. [ABSTRACT FROM AUTHOR]

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 mismatch 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 mismatch 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 mismatch 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. 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. 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. [ABSTRACT FROM AUTHOR]

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 100 m models over cities. A key issue is how to handle the representation of partially resolved turbulence in these models. In this article we compare the representation of a clear convective boundary‐layer case in London in 100 and 50 m 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 50 m grid‐length model with the 100 m one. It is found that, although going to 50 m 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 50 m model resolves significantly more of the energetic eddies, and a length‐scale analysis that shows the 50 and 100 m models produce convective structures 2–3 times larger than observed. We conclude that, for the MetUM, model grid‐lengths of order 100 m 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 100 m 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. [ABSTRACT FROM AUTHOR]