The third version of the Regional Atmosphere and Land (RAL3) science configuration is documented. Developed through international partnerships, RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator (JULES) when applied across timescales with kilometre and sub-kilometre-scale model grids. The RAL3 configuration represents a major advance compared to previous versions by delivering a common science definition suitable for application to tropical and mid-latitude regions. Developments within RAL3 include the introduction of a double-moment microphysics scheme and a bimodal cloud scheme, replacing use of a single-moment scheme and different cloud schemes for mid-latitudes and tropics in previous versions. Updates have been implemented to the boundary layer scheme and a consolidation of land model settings to be more consistent with global atmosphere and land (GAL) science configurations. Physics developments aimed to address priorities for model performance improvement identified by users. This paper documents the RAL3 science configuration, including a series of iterative revisions delivered since its first release, and their characteristics. Evidence is provided from the variety of assessments of RAL3, relative to the previous version (RAL2). Collaborative development and evaluation across organizations have enabled evaluation across a range of domains, grid spacing and timescales. The analysis indicates more realistic precipitation distributions, improved representation of clouds and of visibility, a continued trend to more realistic representation of convection, and reduced near-surface wind speeds but a persistent cold-temperature bias. Overall the convective-scale verification scores and climatological model distributions relative to observations improve for the majority of variables. Ensemble results show improvements to the spread-error relationship. User feedback from subjective assessment activities has also been positive. Differences between RAL3 revisions and RAL2 are further illustrated through a process-based analysis of a convective system over the UK. The latest RAL3 configuration (RAL3.3) is therefore recommended for research, operational numerical weather prediction, and climate production at kilometre and sub-kilometre scales.

For a number of years research has been carried out in several centres which has demonstrated the potential benefits of 100-m scale models for a range of meteorological phenomena. More recently, some meteorological services have started to consider seriously the operational implementation of practical hectometric models. Many, but by no means all, of the applications are likely to relate to urban areas, where the enhanced resolution has obvious benefits. This article is concerned with the issues that need to be addressed to bridge the gap between research at 100-m scales and practical models. We highlight a number of key issues that need to be addressed, with suggestions of important avenues for future development. An overarching issue is the high computational cost of these models. Although some ideas to reduce this are presented, it will always be a serious constraint. This means that the benefits of these models over lower resolution ones, or other techniques for generating high-resolution forecasts, will need to be clearly understood, as will the trade-offs with resolution. We discuss issues with model dynamical cores and physics–dynamics coupling. There are a number of challenges around model parameterisations, where some of the traditional problems (e.g., convection) become easier but a number of new challenges (e.g., around surface parameterisations) appear. Observational data at these scales present a challenge and novel types of observations will need to be considered. Data assimilation will be needed for short-range forecasts, but there is currently little knowledge of this, although some of the likely issues are clear. An ensemble approach will be essential in many cases (e.g., convection), but research is needed into ensembles at these scales and significant work on post-processing systems is required to make the best use of models at these grid lengths.

Since 2012 the Met Office has been running a short‐range convective‐scale ensemble prediction system over the United Kingdom, known as MOGREPS‐UK. In this article we consider MOGREPS‐UK in its past, present and future configurations. We describe the evolution of the system during its first few years as an operational model and explain the rationale behind its development. The operational configuration of MOGREPS‐UK is evaluated using neighbourhood verification techniques which allow the comparison of ensemble and deterministic forecasts in a probabilistic sense. We compare the performance of MOGREPS‐UK to that of the higher‐resolution UK deterministic convective‐scale model, the UKV, and show that over a 3‐month long trial MOGREPS‐UK performs better for all variables considered. Plans of future upgrade options of MOGREPS‐UK that take advantage of the increased computing capacity at the Met Office are discussed. Three different options are compared: increasing the domain size (now implemented), decreasing the horizontal grid‐spacing, and increasing the number of ensemble members. Objective verification results from month‐long winter and summer trials show that all options have their benefits, with the most improvement seen with the increase in ensemble size, particularly for precipitation.

Representing model uncertainty in convection‐permitting ensemble prediction systems is a developing area of research. While methods for including variability to account for model uncertainty at the global scales are quite mature, it is not clear that these methods will necessarily be applicable at the convective scale. One such method is the Random Parameter (RP) scheme, where parameters from the physics parametrizations are perturbed at regular intervals throughout the forecast. In this work, we adapt the RP scheme to represent model uncertainty in the Met Office's convection‐permitting ensemble prediction system for the UK (MOGREPS‐UK). The revised version of the RP scheme is applied to a sub‐set of model parameters, chosen to target specific physical processes relevant to the UK forecast. Objective verification scores from two one‐month trials show particular improvements for visibility and surface temperature when the RP scheme is used. Application of the RP scheme results in a modest increase in the ensemble spread for all surface parameters. The results of low‐visibility case‐studies show that applying the RP scheme enables the ensemble to capture observed fog events otherwise missed by the forecast. Overall, the RP scheme has a positive effect on MOGREPS‐UK, and demonstrates the benefit of schemes that target known areas of model uncertainty.